diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 00000000..d4581c9f --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,18 @@ +name: pre-commit +on: + push: + branches: [main, dev] + pull_request: + branches: [main, dev] +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: "3.13" + + - name: Run pre-commit + uses: pre-commit/action@v3.0.1 diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..8d263f22 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,362 @@ +(changelog)= +# ORBIT Changelog + +## Unreleased + +- The existing documentation has been converted from a Sphinx-based setup to a Jupyter Book v1 build + to create more easily modifiable documentation components and integrate better code examples + directly into the documentation site. +- The `save_config` function is now compatible with the `ProjectManager.config` object, allowing + for direct saving without user modification. +- Fixes a bug in `CustomArraySystemDesign.create_project_csv()` where the output file location does + not adjust to the user's `folder` input. + +## 1.2.6 + +- Implements `create_layout_df` for the `CustomArraySystemDesign` model to ensure + compatibility with workflows relying on the layout generation tools. + +## 1.2.5 + +- Allow for a Pandas DataFrame to be passed directly to the `CustomArraySystemDesign.layout_data` + configuration input. +- Move the matplotlib import from the import section of `/ORBIT/phases/design/array_system_design.py` + to the `CustomArraySystemDesign.plot_array_system` for missing module error handling. +- Adds a general layout `DataFrame` creation method as `ArraySystemDesign.create_layout_df()` that + is called by the `save_layout` method to maintain backwards compatibility, but opens up the ability + gather the layout without saving it to a file. +- Updated default `soft_capex` factors. [PR #201](https://github.com/NLRWindSystems/ORBIT/pull/201) + - `construction_insurance_factor` updated from 0.115 to 0.0207 based on industry benchmarking, resulting in higher construction insurance costs. + - `interest_during_construction` updated from 4.4% to 6.5% based on financial assumptions from the 2025 Annual Technology Baseline (ATB), increasing construction financing costs. + - `decommissioning_factor` updated from 0.175 to 0.2 based on industry benchmarking, leading to higher decommissioning costs than in previous versions. +- Updated default `project_capex` values. [PR #201](https://github.com/NLRWindSystems/ORBIT/pull/201) + - `site_auction_price` increased from 100M to 105M USD to account for rent fees before operation. + - `site_assessment_cost`, `construction_plan_cost`, and `installation_plan_cost` increased from 50M, 1M, and 0.25M USD to 200M, 25M, and 25M USD, respectively. + - Total `project_capex` excluding `site_auction_price` now sums to 250M USD, aligning with DevEx recommendations based on industry benchmarking. + - These updates lead to higher default total project costs than in previous versions. +- Included onshore substation costs in BOS CapEx and project breakdown. [PR #201](https://github.com/NLRWindSystems/ORBIT/pull/201) + - The `ElectricalDesign` module previously calculated onshore substation costs but did not include them in `capex_breakdown` or `bos_capex`. + - These costs are now incorporated when `ElectricalDesign` is used, resulting in higher `bos_capex`, `soft_capex`, and `total_capex` than in prior versions. +- Cable configuration file updates. [PR #201](https://github.com/NLRWindSystems/ORBIT/pull/201) + - Added a new dynamic cable configuration file for floating cases: `library/cables/XLPE_1200mm_220kV_dynamic.yaml`. + - Updated cost values for `library/cables/XLPE_630mm_66kV.yaml` and `library/cables/XLPE_630mm_66kV_dynamic.yaml` based on industry benchmarking. + - All cable cost updates are expressed in 2024 USD for consistency with other library configuration files. + +## 1.2.4 + +- Support Python 3.14 + +## 1.2.3 + +- Adjusted the recent np.trapz fix to be compatible with numpy < 2.0 + +## 1.2.2 + +- Replaced the deprecated `numpy.trapz` with `numpy.trapezoid`. +- Deprecates Python 3.9 support in preparation for EOL and seamless benedict compatibility. + +## 1.2.1 + +- Removed `wisdem_api.py` because NLRWindSystems now uses orbit as a pip installed package. +- Added Python 3.12 and 3.13 to the workflow files. +- Moved matplotlib as an optional dependency + +## 1.2 + +- New cable `library/cables/XLPE_1200mm_220kV.yaml` Is a 220kV cable that can carry ~400MW of HVAC power. +- Fixed frozen python-benedict version + - `ParametricManager` can still use '.' as a keypath separator (no change to user inputs) and is compatible with latest python-benedict +- Updated various default costs to 2024 USD. [PR #187](https://github.com/NLRWindSystems/ORBIT/pull/187) + - Cost rates for different models were determined by benchmarking the costs through industry outreach, + along with adjustments based on commodity prices, inflation, and labor indices. + - ORBIT assumes a procurement year of 2024 in the files: + - `defaults/common_costs.yaml` represents all the design related costs. + - `ORBIT/manager.py` includes project related costs + - `library/cables/*` shows all the cables with updated `cost_per_km` + - `library/vessels/*` shows all the vessels with updated `day_rate` + - Added `defaults/costs_by_procurement_year.csv` which provides the default costs for other procurement year, + but in 2024 USD. +- Bug Fix: Characteristic Impedance calculation correction. [Issue #186](https://github.com/NLRWindSystems/ORBIT/issues/186) + - There were some documentation typos and a units error in the calculation, where mH (10^-3) was divided by nF (10^-9) + - Updated several tests with new values that correlate to the latest cable power capacity +- Updated NLRWindSystems API (`wisdem_api.py`) + - Match some variable names and inputs that have diverged over time. + - Caught turbine_capex double count in NLRWindSystems when using `total_capex` from ORBIT. + - Updated some tests. +- Enhanced `ProjectManager`: [PR #177](https://github.com/NLRWindSystems/ORBIT/pull/177) + - Improvements made to `soft_capex` calculations because previous versions + used default `$/kW` values from the 2018 Cost of Wind Energy Review unless provided by + the user. Those default values are out of date and do not scale with the size of the + project which is not entirely accurate. + - `soft_capex` is now calculated as sum of `construction_insurance_capex`, `decommissioning_capex`, + `commissioning_capex`, `procurement_contingency_capex`, `installation_contingency_capex`, + `construction_financing_capex`. NOTE: user can still specify the same `$/kW` values if they choose. + - New factors were implemented to calculated updated project_parameters if the user does not specify + any inputs. + +## 1.1 + +### New features + +- Enhanced `MooringSystemDesign`: + - Can specify catenary or semitaut mooring systems. (use `mooring_type`) + - Can specify drag embedment or suction pile anchors. (use `anchor_type`) + - Description: This class received some new options that the user can + specify to customize the mooring system. By default, this design uses + catenary mooring lines and suction pile anchors. The new semitaut mooring + lines use interpolation to calculate the geometry and cost based on + (Cooperman et al. 2022, ). + - See `5. Example Floating Project` for more details. +- New `ElectricalDesign`: + - Now has HVDC or HVAC transmission capabilities. + - New tests created `test_electrical_export.py` + - Description: This class combines the elements of `ExportSystemDesign` and the + `OffshoreSubstationDesign` modules. Its purpose is to represent the + entire export system more accurately by linking the type of cable + (AC versus DC) and substation’s components (i.e. transformers versus converters). + Most export and substation component costs were updated to include a per-unit cost + rather than a per-MW cost rate and they can be added to the project config file too. + Otherwise, those per-unit costs use default and were determined with the help of + industry experts. + + > - This module’s components’ cost scales with number of cables and + > substations rather than plant capacity. + > - The offshore substation cost is calculated based on the cable type + > and number of cables, rather than scaling function based on plant capacity. + > - The mass of an HVDC and HVAC substation are assumed to be the same. + > Therefore, the substructure mass and cost functions did not change. + > - An experimental onshore cost function was also added to account for + > the duplicated interconnection components. Costs will vary depending + > on the cable type. + + - See new example `Example - Using HVDC or HVAC` for more details. +- Enhanced `FloatingOffshoreSubStation`: + - Fixed the output substructure type from Monopile to Floating. (use `oss_substructure_type`) + - Removes any pile or fixed-bottom substructure geometry. + - See `Example 5. Example Floating Project` for more details. +- Updated `MoredSubInstallation`: + - Uses an AHTS vessel which must be added to project config file. + - See `example/example_floating_project.yaml` (use `ahts_vessel`) +- New `22MW_generic.yaml` turbine. + - Based on the IEA - 22 MW reference wind turbine. + - See `library/turbines` for more details. +- New cables: + - Varying HVDC ratings + - Varying HVDC and HVAC "dynamic" cables for floating projects. + - See `library/cables` for all the cables and more details. + +### Updated default values + +- `defaults/process_times.yaml` + - `` drag_embedment_install_time` `` increased from 5 to 12 hours. +- `phases/install/quayside_assembly_tow/common.py`: + - lift and attach tower section time changed from 12 to 4 hours per section, + - lift and attach nacelle time changed from 7 to 12 hours. +- `library/cables/XLPE_500mm_132kV.yaml`: + - `cost_per_km` changed from \$200k to \$500k. +- `library/vessels/example_cable_lay_vessel.yaml`: + - `min_draft` changed from 4.8m to 8.5m, + - `overall_length` changed from 99m to 171m, + - `max_mass` changed 4000t to 13000t, +- `library/vessels/example_towing_vessel.yaml`: + - `max_waveheight` changed from 2.5m to 3.0m, + - `max_windspeed` changed 20m to 15m, + - `transit_speed` changed 6km/h to 14 km/h, + - `day_rate` changed \$30k to \$35k + +### Improvements + +- All design classes have new tests to track total cost to flag any changes that may + impact final project cost. +- Relocated all the get design costs in each design class to `common_cost.yaml`. +- Fully adopted `pyproject.toml` for managing all possible tool settings, and + removed the tool-specific files from the top-level of the directory. +- Replaced flake8 and pylint with ruff to adopt a cleaner, faster, and easier + to manage linting and autoformatting workflow. As a result, some of the more + onerous checks have been removed to discourage the use of + `git commit --no-verify`. This change has also added in other rules that + discourage Python anti-patterns and encourage modern Python usage. +- NOTE: Users may wish to run + `git config blame.ignoreRevsFile .git-blame-ignore-revs` to ignore the + reformatting edits in their blame. + +## 1.0.8 + +- Added explicit methods for adding custom design or install phases to + `ProjectManager`. +- Added WOMBAT compatibility for custom array system files. +- Fixed bug in custom array cable system design that breaks for plants with + more than two substations. + +## 1.0.7 + +- Added `SupplyChainManager`. +- Added `JacketInstallation` module. +- Added option to use dynamic supply chain in `MonopileInstallation` module. + +## 1.0.6 + +- Expanded tutorial and examples. +- Added templates for design and install modules. +- Added ports to library pathing. +- Misc. bugfixes. + +## 1.0.5 + +- Added initial floating offshore substation installation module. +- Added option to specific floating cable depth in cable design modules. +- Bugfix in `project.total_capex`. + +## 1.0.4 + +- Added ability to directly prescribe weather downtime through the + `availability` keyword +- Added support for generating linear models using `ParametricManager` + +## 1.0.2 + +- Added `ProjectManager.capex_breakdown`. + +## 1.0.1 + +- Default behavior of `ParametricManager` has been changed. Input parameters + are now zipped together and ran as a discrete set of configs. To use the past + functionality (finding the product of all input parameters), use the option + `product=True` +- Bugfix: Added port costs to floating substructure installation modules. +- Revised docs for running the Example notebooks and added link to a tutorial + about working with jupyter notebooks. + +## 1.0.0 + +- New feature: `ParametricManager` for running parametric studies. +- Added procurement cost inputs and total cost methods to installation phases. + Design phases are now only used to fill in the design and do not return a + cost associated with the design. +- Refactored aggregation project level outputs in `ProjectManager`. +- Revised Net Present Value calculation to utilize new project outputs. +- Added `load_config` and `save_config` functions. +- Moved `ORBIT.library` to `OBRIT.core.library`. +- Centralized model defaults to `ORBIT.core.defaults`. +- `ProjectManager.project_actions` renamed to `ProjectManager.actions` +- `ProjectManager.project_logs` renamed to `ProjectManager.logs` +- `ProjectManager.run_project()` renamed to `ProjectManager.run()` +- Moved documentation hosting to gh-pages. + +## 0.5.1 + +- Process time kwargs should now be passed through `ProjectManager` in a + dictionary named `processes` in the config. +- Revised `prep_for_site_operations` and related processes to allow for + dynamically positioned vessels. +- Updated NLRWindSystems API to include floating functionality. + +## 0.5.0 + +- Initial release of floating substructure functionality in ORBIT. +- New design modules: `MooringSystemDesign`, `SparDesign` and + `SemiSubmersibleDesign`. +- New installation modules: `MooringSystemInstallation` and + `MooredSubInstallation` +- Cable design and installation modules modified to calculate catenary lengths + of suspended cable at depths greater than 60m. + +## 0.4.3 + +- New feature: Cash flow and net present value calculation within + `ProjectManager`. +- Revised `CustomArraySystemDesign` module. +- Revised assumptions in `MonopileDesign` module to bring results in line + with industry numbers. + +## 0.4.2 + +- New feature: Phase dependencies in `ProjectManager`. +- New feature: Windspeed constraints at multiple heights, including automatic + interpolation/extrapolation of configured windspeed profiles. +- Added option to define `mobilization_days` and `mobilization_mult` in a + `Vessel` configuration file. +- Added option for pre-installation trenching operations to + `ArrayCableInstallation` and `ExportCableInstallation`. +- Revised `OffshoreSubstationDesign` to scale the size of the substations + with the user-configured number of substations. +- Bugfix in the returned argument order of `ProjectManager.run_install_phase` + where the cost of a prior phase would be incorrectly applied as the elapsed + time. + +## 0.4.1 + +- Modified installation to require version of marmot-agents that has an + internal copy of simpy. +- Added/expanded `detailed_outputs` for all modules. +- Standardized naming of weight/mass terms to mass throughout the model. +- Cleanup in `ProjectManager`. + +## 0.4.0 + +- Vessel mobilization added to all vessels in all installation modules. + Defaults to 7 days at 50% day-rate. +- Cable lay, bury and simulataneous lay/bury methods are not flagged as + suspendable to avoid unrealistic project delays. +- Cost of onshore transmission construction added to + `ExportCableInstallation`. +- Simplified `ArrayCableInstallation`, `ExportCableInstallation` modules. +- Removed `pandas` from the internals of the model, though it is still useful + for tabulating the project logs. +- Revised package structure. Functionally formerly in ORBIT.simulation or + ORBIT.vessels has been moved to ORBIT.core. +- `InstallPhase` cleaned up and slimmed down. +- `Environment` and associated functionality has been replaced with + `marmot.Environment`. +- Logging functionality revised. No longer uses the base python logging module. +- `Vessel` now inherits from `marmot.Agent`. +- Tasks that were in `ORBIT.vessels.tasks` have been moved to their + respective modules and restructured with `marmot.process` and + `Agent.tasks`. +- Modules inputs cleaned up. `type` parameters are no longer required for + monopile, transition piece or turbine component definitions. +- Removed old/irrelevant tests. + +## 0.3.5 + +- Added 'per kW' properties to `ProjectManager` CAPEX results. + +## 0.3.4 + +- Added configuration to `ProjectManager` that allows exceptions to be caught + within individual modules and allows the project as a whole to continue. +- Fixed installation process when installing from GitHub. + +## 0.3.3 + +- Added configuration for multiple tower sections in `TurbineInstallation`. +- Added configuration for seperate lay/burial in `ArrayCableInstallation` and + `ExportCableInstallation`. +- Overhauled test suite and associated library. +- Bugfix in `CableCarousel`. +- Expanded NLRWindSystems Fixed API. + +## 0.3.2 + +- Initial release of fixed substructure NLRWindSystems API +- Material cost for monopiles and transition pieces added to `MonopileDesign` +- Updated `ProjectManager` to allow user to override default `DesignPhase` + results +- Moved config validation to `BasePhase` and added call to + `self.validate_config` for all current modules +- Config validation logic reworked so dicts of optional values are not + required +- Added method to resolve project capacity in `ProjectManager`. A user can + now input `plant.num_turbines` and `turbine.turbine_rating` and + `plant.capacity` will be added to the config. +- Added initial set of standardized inputs to `ProjectManager`: + - `self.installation_capex` + - `self.installation_time` + - `self.project_days` + - `self.bos_capex` + - `self.turbine_capex` + - `self.total_capex` + +## 0.3.1 + +- Updated README diff --git a/LICENSE b/LICENSE index 1c0c15ea..bd2d5df8 100644 --- a/LICENSE +++ b/LICENSE @@ -188,7 +188,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright [2020] [National Renewable Energy Laboratory] + Copyright [2026] [National Laboratory of the Rockies] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/ORBIT/__init__.py b/ORBIT/__init__.py index 086b6995..b6cdf96e 100644 --- a/ORBIT/__init__.py +++ b/ORBIT/__init__.py @@ -6,9 +6,9 @@ "Rob Hammond", "Nick Riccobono", ] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Nick Riccobono" -__email__ = ["nicholas.riccobono@nrel.gov", "rob.hammond@nrel.gov"] +__email__ = ["nicholas.riccobono@nlr.gov", "rob.hammond@nlr.gov"] __status__ = "Development" diff --git a/ORBIT/config.py b/ORBIT/config.py index 579cacd1..fe1ac663 100644 --- a/ORBIT/config.py +++ b/ORBIT/config.py @@ -1,9 +1,9 @@ """Provides the configuration loading and saving methods.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from pathlib import Path diff --git a/ORBIT/core/__init__.py b/ORBIT/core/__init__.py index 2618f713..d670a84a 100644 --- a/ORBIT/core/__init__.py +++ b/ORBIT/core/__init__.py @@ -1,9 +1,9 @@ """Core functionality of ORBIT installation phases.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from .port import Port, WetStorage diff --git a/ORBIT/core/components.py b/ORBIT/core/components.py index 15ba6392..d039cfec 100644 --- a/ORBIT/core/components.py +++ b/ORBIT/core/components.py @@ -1,9 +1,9 @@ """Provides the `Crane` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import simpy @@ -88,12 +88,12 @@ def __init__(self, dp_specs): def extract_dp_specs(self, dp_specs): """ - Extracts and defines jacking system specifications. + Extracts and defines dynamic positioning system specifications. Parameters ---------- - jacksys_specs : dict - Dictionary containing jacking system specifications. + dp_specs : dict + Dictionary containing dynamic positioning system specifications. """ self.dp_class = dp_specs.get("class", 1) diff --git a/ORBIT/core/defaults/__init__.py b/ORBIT/core/defaults/__init__.py index bbc9ee9f..e04265b3 100644 --- a/ORBIT/core/defaults/__init__.py +++ b/ORBIT/core/defaults/__init__.py @@ -1,9 +1,9 @@ """Default inputs used throughout ORBIT.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from pathlib import Path diff --git a/ORBIT/core/defaults/common_costs.yaml b/ORBIT/core/defaults/common_costs.yaml index 2ba111f4..c3c7c078 100644 --- a/ORBIT/core/defaults/common_costs.yaml +++ b/ORBIT/core/defaults/common_costs.yaml @@ -24,9 +24,9 @@ substation_design: topside_fab_cost_rate: 14500 # USD/t topside_design_cost: # USD #oldHVAC: 4.5e6 - HVAC: 193140000 - HVDC-monopole: 529200000 - HVDC-bipole: 856800000 + HVAC: 193140000 + HVDC-monopole: 529200000 + HVDC-bipole: 856800000 shunt_cost_rate: 44163 # USD/MW mpt_unit_cost: 2992926 # USD/mpt shunt_unit_cost: 10428 # USD/cable @@ -38,27 +38,27 @@ substation_design: topside_assembly_factor: 0.075 # % converter_cost: # USD HVAC: 0 - HVDC-monopole: 228600000 - HVDC-bipole: 532800000 + HVDC-monopole: 228600000 + HVDC-bipole: 532800000 oss_substructure_cost_rate: 3785 # USD/t oss_pile_cost_rate: 0 # USD/t # Onshore substation component cost rates onshore_substation_design: onshore_converter_cost: # USD - HVAC: 0 - HVDC-monopole: 163724546 - HVDC-bipole: 364991025 + HVAC: 0 + HVDC-monopole: 163724546 + HVDC-bipole: 364991025 shunt_unit_cost: 13557 # USD/cable switchgear_cost: 9729618 # USD/cable compensation_rate: # USD/cable - HVAC: 32640626 - HVDC-monopole: 0 - HVDC-bipole: 0 + HVAC: 32640626 + HVDC-monopole: 0 + HVDC-bipole: 0 onshore_construction_rate: # USD - HVAC: 5214158 - HVDC-monopole: 91039190 - HVDC-bipole: 104283150 + HVAC: 5214158 + HVDC-monopole: 91039190 + HVDC-bipole: 104283150 # Semisubmersible component cost rates semisubmersible_design: diff --git a/ORBIT/core/environment.py b/ORBIT/core/environment.py index dee120e3..cec27d6a 100644 --- a/ORBIT/core/environment.py +++ b/ORBIT/core/environment.py @@ -1,9 +1,9 @@ """ORBIT specific marmot.Environment.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from bisect import bisect diff --git a/ORBIT/core/exceptions.py b/ORBIT/core/exceptions.py index b3219c38..45edded3 100644 --- a/ORBIT/core/exceptions.py +++ b/ORBIT/core/exceptions.py @@ -1,9 +1,9 @@ """Custom exceptions used throughout ORBIT.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import os diff --git a/ORBIT/core/library.py b/ORBIT/core/library.py index 3fcb588e..092ff5ca 100644 --- a/ORBIT/core/library.py +++ b/ORBIT/core/library.py @@ -25,9 +25,9 @@ """ __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" import os diff --git a/ORBIT/core/logic/__init__.py b/ORBIT/core/logic/__init__.py index 24b00f63..6a581122 100644 --- a/ORBIT/core/logic/__init__.py +++ b/ORBIT/core/logic/__init__.py @@ -1,9 +1,9 @@ """Provides the simulation logic shared across several modules.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from .vessel_logic import ( # shuttle_items_to_queue diff --git a/ORBIT/core/logic/vessel_logic.py b/ORBIT/core/logic/vessel_logic.py index a906a89a..1db68887 100644 --- a/ORBIT/core/logic/vessel_logic.py +++ b/ORBIT/core/logic/vessel_logic.py @@ -1,9 +1,9 @@ """Provides common simulation logic related to vessels.""" __author__ = ["Jake Nunemaker", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from marmot import process diff --git a/ORBIT/core/port.py b/ORBIT/core/port.py index 9078768a..b0cd2709 100644 --- a/ORBIT/core/port.py +++ b/ORBIT/core/port.py @@ -1,9 +1,9 @@ """Provides the `Port` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import simpy diff --git a/ORBIT/core/supply_chain.py b/ORBIT/core/supply_chain.py index 6314bbdb..3a36edc8 100644 --- a/ORBIT/core/supply_chain.py +++ b/ORBIT/core/supply_chain.py @@ -1,9 +1,9 @@ """Supply chain related infrastructure.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2022, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2022, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from marmot import Agent, process diff --git a/ORBIT/core/vessel.py b/ORBIT/core/vessel.py index 430ea2bc..ff61d3a3 100644 --- a/ORBIT/core/vessel.py +++ b/ORBIT/core/vessel.py @@ -1,9 +1,9 @@ """Provides the `Vessel` class.""" __author__ = ["Jake Nunemaker", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from collections import Counter, namedtuple diff --git a/ORBIT/manager.py b/ORBIT/manager.py index b53ca9ca..a88b4466 100644 --- a/ORBIT/manager.py +++ b/ORBIT/manager.py @@ -4,9 +4,9 @@ """ __author__ = ["Jake Nunemaker"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = ["jake.nunemaker@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov"] import re @@ -182,7 +182,7 @@ def run(self, **kwargs): self.run_all_design_phases(design_phases, **kwargs) - if isinstance(install_phases, (list, set)): + if isinstance(install_phases, list | set): self.run_multiple_phases_in_serial(install_phases, **kwargs) elif isinstance(install_phases, dict): @@ -670,7 +670,7 @@ def run_install_phase(self, name, start, **kwargs): if phase.installation_capex: self.installation_costs[name] = phase.installation_capex - + return time, logs def get_phase_class(self, phase): @@ -747,8 +747,6 @@ def run_design_phase(self, name, **kwargs): self.detailed_outputs, phase.detailed_output ) - - def run_multiple_phases_in_serial(self, phase_list, **kwargs): """ Runs multiple phases listed in self.config['install_phases'] in serial. @@ -878,7 +876,7 @@ def get_dependency_start_time(self, target, perc): start = self.phase_starts[target] elapsed = self.phase_times[target] - if isinstance(perc, (int, float)): + if isinstance(perc, int | float): if (perc < 0.0) or (perc > 1.0): raise ValueError( @@ -961,7 +959,7 @@ def _parse_install_phase_values(self, phases): for k, v in phases.items(): - if isinstance(v, (int, float, str)): + if isinstance(v, int | float | str): defined[k] = v elif isinstance(v, tuple) and len(v) == 2: @@ -1057,7 +1055,7 @@ def outputs(self, include_logs=False, npv_detailed=False): "supply_chain_capex": self.supply_chain_capex, "supply_chain_capex_kw": self.supply_chain_capex_per_kw, "onshore_substation_capex": self.onshore_substation_capex, - "onshore_substation_capex_kw": self.onshore_substation_capex_per_kw, + "onshore_substation_capex_kw": self.onshore_substation_capex_per_kw, # noqa: E501 } if include_logs: @@ -1256,7 +1254,9 @@ def _filter_logs(self, keys): @property def progress_summary(self): - """Returns a summary of progress by month.""" + """Returns a summary of the number of completed component installations + by month. + """ arr = np.array( self.progress_logs, dtype=[("progress", "U32"), ("time", "i4")] @@ -1269,7 +1269,12 @@ def progress_summary(self): unique, counts = np.unique( arr["progress"][dig == i], return_counts=True ) - summary[i] = dict(zip(unique, counts)) + summary[i] = dict(zip(unique, counts, strict=False)) + + summary = { + k: {str(_k): int(_v) for _k, _v in v.items()} + for k, v in summary.items() + } return summary @@ -1447,7 +1452,7 @@ def capex_breakdown(self): outputs[name] = cost outputs["Onshore Substation"] = self.onshore_substation_capex - + outputs["Turbine"] = self.turbine_capex outputs["Soft"] = self.soft_capex @@ -1497,7 +1502,11 @@ def capex_detailed_soft_capex_breakdown_per_kw(self): def bos_capex(self): """Returns total balance of system CapEx.""" - return self.system_capex + self.installation_capex + self.onshore_substation_capex + return ( + self.system_capex + + self.installation_capex + + self.onshore_substation_capex + ) @property def bos_capex_per_kw(self): @@ -1570,25 +1579,28 @@ def supply_chain_capex_per_kw(self): @property def onshore_substation_capex(self): - """Returns the onshore substation CapEx if available in 'ElectricalDesign', otherwise 0.""" + """Returns the onshore substation CapEx if available in + 'ElectricalDesign', otherwise 0. + """ if "ElectricalDesign" in self.phases: try: - return self.phases["ElectricalDesign"].detailed_output["export_system"]["onshore_substation_costs"] + return self.phases["ElectricalDesign"].detailed_output[ + "export_system" + ]["onshore_substation_costs"] except KeyError: return 0 return 0 @property def onshore_substation_capex_per_kw(self): - """Returns the onshore substation CapEx/kW. - """ + """Returns the onshore substation CapEx/kW.""" if "ElectricalDesign" in self.phases: try: return self.onshore_substation_capex / (self.capacity * 1000) except KeyError: return None return None - + @property def overnight_capex(self): """Returns the overnight capital cost of the project.""" @@ -1983,7 +1995,7 @@ def complete_array_strings(self): turbines = self.chunk_max(_turbines, per_string) num_turbines = list(self.chunk_len(_turbines, per_string)) - data = list(zip(strings, subs, turbines)) + data = list(zip(strings, subs, turbines, strict=False)) return [max(el) for el in data], num_turbines diff --git a/ORBIT/parametric.py b/ORBIT/parametric.py index ecc5fa9a..4effe12b 100644 --- a/ORBIT/parametric.py +++ b/ORBIT/parametric.py @@ -1,9 +1,9 @@ """Provides the ParametricManager class for a parameter sweeps.""" __author__ = ["Jake Nunemaker"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = ["jake.nunemaker@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov"] import re @@ -121,9 +121,11 @@ def run_list(self): runs = list(product(*self.params.values())) else: - runs = list(zip(*self.params.values())) + runs = list(zip(*self.params.values(), strict=False)) - return [dict(zip(self.params.keys(), run)) for run in runs] + return [ + dict(zip(self.params.keys(), run, strict=False)) for run in runs + ] @property def num_runs(self): @@ -358,7 +360,7 @@ def perc_diff(self): pd.Series """ - inputs = dict(zip(self.X.T.index, self.X.T.to_numpy())) + inputs = dict(zip(self.X.T.index, self.X.T.to_numpy(), strict=False)) predicted = self.predict(inputs) return (self.Y - predicted) / self.Y diff --git a/ORBIT/phases/__init__.py b/ORBIT/phases/__init__.py index 324fe7ad..501113d4 100644 --- a/ORBIT/phases/__init__.py +++ b/ORBIT/phases/__init__.py @@ -4,9 +4,9 @@ """ __author__ = ["Jake Nunemaker", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = ["Jake Nunemaker", "Rob Hammond"] -__email__ = ["jake.nunemaker@nrel.gov" "rob.hammond@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov" "rob.hammond@nlr.gov"] from .base import BasePhase diff --git a/ORBIT/phases/base.py b/ORBIT/phases/base.py index 3e73b951..9291174f 100644 --- a/ORBIT/phases/base.py +++ b/ORBIT/phases/base.py @@ -1,9 +1,9 @@ """Provides the `BasePhase` class.""" __author__ = ["Jake Nunemaker", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from abc import ABC, abstractmethod diff --git a/ORBIT/phases/design/__init__.py b/ORBIT/phases/design/__init__.py index 70eabc07..e8d73a36 100644 --- a/ORBIT/phases/design/__init__.py +++ b/ORBIT/phases/design/__init__.py @@ -1,9 +1,9 @@ """The design package contains `DesignPhase` and its subclasses.""" __author__ = ["Jake Nunemaker", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = ["Jake Nunemaker", "Rob Hammond"] -__email__ = ["jake.nunemaker@nrel.gov" "rob.hammond@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov" "rob.hammond@nlr.gov"] from .design_phase import DesignPhase # isort:skip @@ -17,3 +17,17 @@ from .mooring_system_design import MooringSystemDesign from .scour_protection_design import ScourProtectionDesign from .semi_submersible_design import SemiSubmersibleDesign + +design_phases = [ + "MonopileDesign", + "ScourProtectionDesign", + "SparDesign", + "SemiSubmersibleDesign", + "MooringSystemDesign", + "ArraySystemDesign", + "CustomArraySystemDesign", + "ElectricalDesign", + "ExportSystemDesign", + "OffshoreSubstationDesign", + "OffshoreFloatingSubstationDesign", +] diff --git a/ORBIT/phases/design/_cables.py b/ORBIT/phases/design/_cables.py index 81d328b0..5da15d15 100644 --- a/ORBIT/phases/design/_cables.py +++ b/ORBIT/phases/design/_cables.py @@ -1,9 +1,9 @@ """Provides the base `Cable`, `Plant`, and `CableSystem` classes.""" __author__ = ["Matt Shields", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" import math @@ -17,7 +17,7 @@ class Cable: - r""" + """ Base cable class. Parameters @@ -421,7 +421,7 @@ def _get_catenary_length(self, d, h): @property def free_cable_length(self): - """Returns the vertical length of a cable section, in :mat:`km`.""" + """Returns the vertical length of a cable section, in :math:`km`.""" _design = f"{self.cable_type}_system_design" depth = self.config["site"]["depth"] @@ -531,14 +531,14 @@ def design_result(self): output : dict Dictionary of the number of section lengths and the linear density of each cable type. - - <`cable_type`>_system: dict + + - <`cable_type`>_system: dict - cables: dict - `Cable.name`: dict - - sections: [ - (length of unique section, number of sections) - ], + - sections: [(length of unique section, number of sections)], - linear_density: `Cable.linear_density` - """ + + """ # noqa: E501 if self.cables is None: raise Exception(f"Has {self.__class__.__name__} been ran?") diff --git a/ORBIT/phases/design/array_system_design.py b/ORBIT/phases/design/array_system_design.py index 2f5ab108..40039634 100644 --- a/ORBIT/phases/design/array_system_design.py +++ b/ORBIT/phases/design/array_system_design.py @@ -1,9 +1,9 @@ """Provides the `ArraySystemDesign` class.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" import warnings @@ -209,7 +209,8 @@ def create_strings(self): Calculates the required full and partial string design. .. note:: For custom layouts this is to provide guidance on the number - of strings. + of strings. + """ self._compute_maximum_turbines_per_cable() @@ -825,7 +826,8 @@ def create_project_csv(self, save_name, folder="cables"): ] rows.insert(0, first) rows.insert(0, self.COLUMNS) - print(f"Saving custom array to: /cables/{save_name}.csv") + fp = folder if folder == "cables" else "project/plant" + print(f"Saving custom array to: /{fp}/{save_name}.csv") export_library_specs(folder, save_name, rows, file_ext="csv") def _format_windfarm_data(self): diff --git a/ORBIT/phases/design/design_phase.py b/ORBIT/phases/design/design_phase.py index acf7b829..23922b62 100644 --- a/ORBIT/phases/design/design_phase.py +++ b/ORBIT/phases/design/design_phase.py @@ -1,9 +1,9 @@ """Provides the base `DesignPhase` class.""" __author__ = ["Jake Nunemaker", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from abc import abstractmethod diff --git a/ORBIT/phases/design/electrical_export.py b/ORBIT/phases/design/electrical_export.py index db06635b..bc3d03f7 100644 --- a/ORBIT/phases/design/electrical_export.py +++ b/ORBIT/phases/design/electrical_export.py @@ -1,7 +1,7 @@ """Provides the `ElectricalDesign` class.""" __author__ = ["Sophie Bredenkamp"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "" __email__ = [] @@ -12,8 +12,8 @@ from ORBIT.phases.design._cables import CableSystem """ -[1] Maness et al. 2017, NREL Offshore Balance-of-System Model. -https://www.nrel.gov/docs/fy17osti/66874.pdf +[1] Maness et al. 2017, NLR Offshore Balance-of-System Model. +https://www.nlr.gov/docs/fy17osti/66874.pdf """ @@ -37,9 +37,9 @@ class ElectricalDesign(CableSystem): Total length of cable required to trasmit power. total_mass : float Total mass of cable required to transmit power. - sections_cables : np.ndarray, shape: (`num_cables, ) + sections_cables : np.ndarray, shape: (`num_cables`, ) An array of `cable`. - sections_lengths : np.ndarray, shape: (`num_cables, ) + sections_lengths : np.ndarray, shape: (`num_cables`, ) An array of `length`. """ diff --git a/ORBIT/phases/design/export_system_design.py b/ORBIT/phases/design/export_system_design.py index f6ab00d2..ce9d487a 100644 --- a/ORBIT/phases/design/export_system_design.py +++ b/ORBIT/phases/design/export_system_design.py @@ -1,9 +1,9 @@ """Provides the `ExportSystemDesign` class.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" from warnings import warn @@ -32,9 +32,9 @@ class ExportSystemDesign(CableSystem): Total length of cable required to trasmit power. total_mass : float Total mass of cable required to transmit power. - sections_cables : np.ndarray, shape: (`num_cables, ) + sections_cables : np.ndarray, shape: (``num_cables``, ) An array of `cable`. - sections_lengths : np.ndarray, shape: (`num_cables, ) + sections_lengths : np.ndarray, shape: (``num_cables``, ) An array of `length`. """ diff --git a/ORBIT/phases/design/monopile_design.py b/ORBIT/phases/design/monopile_design.py index 3a32c815..704b805a 100644 --- a/ORBIT/phases/design/monopile_design.py +++ b/ORBIT/phases/design/monopile_design.py @@ -1,9 +1,9 @@ """Provides the `MonopileDesign` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from math import pi, log @@ -113,7 +113,7 @@ def design_monopile( Solves system of equations for the required pile diameter to satisfy the 50 year extreme operating gust moment. Using the result from the diameter equation, calculates the wall thickness and the required - embedment length and other important sizing parameters. + embedment length and other important sizing parameters [1]_. Parameters ---------- @@ -143,9 +143,7 @@ def design_monopile( References ---------- - This class was adapted from [#arany2017]_. - - .. [#arany2017] Laszlo Arany, S. Bhattacharya, John Macdonald, + .. [1] Laszlo Arany, S. Bhattacharya, John Macdonald, S.J. Hogan, Design of monopiles for offshore wind turbines in 10 steps, Soil Dynamics and Earthquake Engineering, Volume 92, 2017, Pages 126-152, ISSN 0267-7261, diff --git a/ORBIT/phases/design/mooring_system_design.py b/ORBIT/phases/design/mooring_system_design.py index 8447d206..dcfd558b 100644 --- a/ORBIT/phases/design/mooring_system_design.py +++ b/ORBIT/phases/design/mooring_system_design.py @@ -1,11 +1,11 @@ """`MooringSystemDesign` and related functionality.""" __author__ = "Jake Nunemaker, Becca Fuchs" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Nicholas Riccobono" __email__ = ( - "jake.nunemaker@nrel.gov, rebecca.fuchs@nrel.gov," - "nicholas.riccobono@nrel.gov" + "jake.nunemaker@nlr.gov, rebecca.fuchs@nlr.gov," + "nicholas.riccobono@nlr.gov" ) from math import sqrt @@ -15,11 +15,11 @@ from ORBIT.phases.design import DesignPhase """ -[1] Maness et al. 2017, NREL Offshore Balance-of-System Model. -https://www.nrel.gov/docs/fy17osti/66874.pdf +[1] Maness et al. 2017, NLR Offshore Balance-of-System Model. +https://www.nlr.gov/docs/fy17osti/66874.pdf [2] Cooperman et al. (2022), Assessment of Offshore Wind Energy Leasing Areas -for Humboldt and Morry Bay. https://www.nrel.gov/docs/fy22osti/82341.pdf +for Humboldt and Morry Bay. https://www.nlr.gov/docs/fy22osti/82341.pdf """ @@ -128,7 +128,7 @@ def determine_mooring_line(self): _key, ), ) - if isinstance(mooring_line_cost_rate, (int, float)): + if isinstance(mooring_line_cost_rate, int | float): mooring_line_cost_rate = [mooring_line_cost_rate] * 3 if fit <= 0.09: @@ -158,7 +158,7 @@ def calculate_line_length_mass(self): Returns the mooring line length and mass. SemiTaut model based on: - https://github.com/NREL/MoorPy/blob/dev/moorpy/MoorProps_default.yaml + https://github.com/NLR/MoorPy/blob/dev/moorpy/MoorProps_default.yaml TODO: Improve TLP line length and mass diff --git a/ORBIT/phases/design/oss_design.py b/ORBIT/phases/design/oss_design.py index 9418e2fc..c27ae23d 100644 --- a/ORBIT/phases/design/oss_design.py +++ b/ORBIT/phases/design/oss_design.py @@ -1,9 +1,9 @@ """Provides the 'OffshoreSubstationDesign` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" import numpy as np diff --git a/ORBIT/phases/design/oss_design_floating.py b/ORBIT/phases/design/oss_design_floating.py index f1bc3610..5a9f4100 100644 --- a/ORBIT/phases/design/oss_design_floating.py +++ b/ORBIT/phases/design/oss_design_floating.py @@ -1,9 +1,9 @@ """Provides the 'OffshoreSubstationDesign` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" import numpy as np diff --git a/ORBIT/phases/design/scour_protection_design.py b/ORBIT/phases/design/scour_protection_design.py index 8dbac190..80136f75 100644 --- a/ORBIT/phases/design/scour_protection_design.py +++ b/ORBIT/phases/design/scour_protection_design.py @@ -1,9 +1,9 @@ """Provides the `ScourProtectionDesign` class.""" __author__ = ["Rob Hammond", "Jake Nunemaker"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" from math import ceil @@ -15,7 +15,7 @@ class ScourProtectionDesign(DesignPhase): """ Calculates the necessary scour protection material for a fixed - substructure. + substructure based on Design of Offshore Wind Turbine Structures [1]_. Parameters ---------- @@ -91,9 +91,9 @@ def __init__(self, config, **kwargs): self.protection_depth = self._design.get("scour_protection_depth", 1) def compute_scour_protection_tonnes_to_install(self): - r""" + """ Computes the amount of scour protection material that needs to be - installed around a fixed substructure. + installed around a fixed substructure [1]_. Terms: * :math:`S =` Scour depth @@ -107,8 +107,8 @@ def compute_scour_protection_tonnes_to_install(self): References ---------- .. [1] Det Norske Veritas AS. (2014, May). Design of Offshore Wind - Turbine Structures. Retrieved from - https://rules.dnvgl.com/docs/pdf/DNV/codes/docs/2014-05/Os-J101.pdf + Turbine Structures. Retrieved from + https://rules.dnvgl.com/docs/pdf/DNV/codes/docs/2014-05/Os-J101.pdf """ # noqa: E501 self.scour_depth = self.equilibrium * self.diameter @@ -158,7 +158,7 @@ def design_result(self): Returns ------- output : dict - - ``scour_protection`` :`dict` + - ``scour_protection`` :`dict` - ``tonnes_per_substructure`` : `int` """ diff --git a/ORBIT/phases/design/semi_submersible_design.py b/ORBIT/phases/design/semi_submersible_design.py index 17fd0a80..f792443a 100644 --- a/ORBIT/phases/design/semi_submersible_design.py +++ b/ORBIT/phases/design/semi_submersible_design.py @@ -1,15 +1,15 @@ """Provides the `SemiSubmersibleDesign` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from ORBIT.phases.design import DesignPhase """ -[1] Maness et al. 2017, NREL Offshore Balance-of-System Model. -https://www.nrel.gov/docs/fy17osti/66874.pdf +[1] Maness et al. 2017, NLR Offshore Balance-of-System Model. +https://www.nlr.gov/docs/fy17osti/66874.pdf """ diff --git a/ORBIT/phases/design/spar_design.py b/ORBIT/phases/design/spar_design.py index e3713c4c..12623f3e 100644 --- a/ORBIT/phases/design/spar_design.py +++ b/ORBIT/phases/design/spar_design.py @@ -1,9 +1,9 @@ """Provides the `SparDesign` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from numpy import exp, log @@ -11,8 +11,8 @@ from ORBIT.phases.design import DesignPhase """ -[1] Maness et al. 2017, NREL Offshore Balance-of-System Model. -https://www.nrel.gov/docs/fy17osti/66874.pdf +[1] Maness et al. 2017, NLR Offshore Balance-of-System Model. +https://www.nlr.gov/docs/fy17osti/66874.pdf """ diff --git a/ORBIT/phases/install/__init__.py b/ORBIT/phases/install/__init__.py index 5a6d0035..958c218a 100644 --- a/ORBIT/phases/install/__init__.py +++ b/ORBIT/phases/install/__init__.py @@ -1,9 +1,9 @@ """The install package contains `InstallPhase` and its subclasses.""" __author__ = ["Jake Nunemaker", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = ["Jake Nunemaker", "Rob Hammond"] -__email__ = ["jake.nunemaker@nrel.gov" "rob.hammond@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov" "rob.hammond@nlr.gov"] from .install_phase import InstallPhase # isort:skip from .oss_install import ( @@ -20,3 +20,17 @@ GravityBasedInstallation, ) from .scour_protection_install import ScourProtectionInstallation + +install_phases = [ + "MonopileInstallation", + "JacketInstallation", + "ScourProtectionInstallation", + "GravityBasedInstallation", + "MooredSubInstallation", + "MooringSystemInstallation", + "TurbineInstallation", + "ArrayCableInstallation", + "ExportCableInstallation", + "OffshoreSubstationInstallation", + "FloatingSubstationInstallation", +] diff --git a/ORBIT/phases/install/cable_install/__init__.py b/ORBIT/phases/install/cable_install/__init__.py index 7997fda6..e6cf8793 100644 --- a/ORBIT/phases/install/cable_install/__init__.py +++ b/ORBIT/phases/install/cable_install/__init__.py @@ -1,9 +1,9 @@ """Initialize cable installation functionality.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" from .array import ArrayCableInstallation from .common import SimpleCable diff --git a/ORBIT/phases/install/cable_install/array.py b/ORBIT/phases/install/cable_install/array.py index d24f5144..cbb2c835 100644 --- a/ORBIT/phases/install/cable_install/array.py +++ b/ORBIT/phases/install/cable_install/array.py @@ -1,9 +1,9 @@ """`ArrayCableInstallation` class and related processes.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/ORBIT/phases/install/cable_install/common.py b/ORBIT/phases/install/cable_install/common.py index 6a60d41d..900e9d9b 100644 --- a/ORBIT/phases/install/cable_install/common.py +++ b/ORBIT/phases/install/cable_install/common.py @@ -1,9 +1,9 @@ """Common processes and cargo types for Cable Installations.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from marmot import process diff --git a/ORBIT/phases/install/cable_install/export.py b/ORBIT/phases/install/cable_install/export.py index 642681ec..86a3076b 100644 --- a/ORBIT/phases/install/cable_install/export.py +++ b/ORBIT/phases/install/cable_install/export.py @@ -1,9 +1,9 @@ """`ExportCableInstallation` and related processes.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/ORBIT/phases/install/install_phase.py b/ORBIT/phases/install/install_phase.py index 930686e1..478b96a1 100644 --- a/ORBIT/phases/install/install_phase.py +++ b/ORBIT/phases/install/install_phase.py @@ -1,9 +1,9 @@ """`InstallPhase` base class.""" __author__ = ["Jake Nunemaker", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = ["Jake Nunemaker", "Rob Hammond"] -__email__ = ["jake.nunemaker@nrel.gov", "rob.hammond@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov", "rob.hammond@nlr.gov"] from abc import abstractmethod diff --git a/ORBIT/phases/install/jacket_install/__init__.py b/ORBIT/phases/install/jacket_install/__init__.py index a093ed95..b2dc82bf 100644 --- a/ORBIT/phases/install/jacket_install/__init__.py +++ b/ORBIT/phases/install/jacket_install/__init__.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2021, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2021, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from .standard import JacketInstallation diff --git a/ORBIT/phases/install/jacket_install/common.py b/ORBIT/phases/install/jacket_install/common.py index 3ffaa436..1dcd20bd 100644 --- a/ORBIT/phases/install/jacket_install/common.py +++ b/ORBIT/phases/install/jacket_install/common.py @@ -4,9 +4,9 @@ """ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2021, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2021, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from marmot import false, process diff --git a/ORBIT/phases/install/jacket_install/standard.py b/ORBIT/phases/install/jacket_install/standard.py index efaef805..07126b1e 100644 --- a/ORBIT/phases/install/jacket_install/standard.py +++ b/ORBIT/phases/install/jacket_install/standard.py @@ -1,9 +1,9 @@ """Provides the jacket installation class and model.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2021, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2021, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import numpy as np @@ -250,7 +250,7 @@ def setup_simulation_with_feeders(self, **kwargs): for x in range(len(self.feeders)) ] - for assigned, feeder in zip(assignments, self.feeders): + for assigned, feeder in zip(assignments, self.feeders, strict=False): shuttle_items_to_queue_wait( feeder, port=self.port, diff --git a/ORBIT/phases/install/monopile_install/__init__.py b/ORBIT/phases/install/monopile_install/__init__.py index 71c63aff..f198d1a7 100644 --- a/ORBIT/phases/install/monopile_install/__init__.py +++ b/ORBIT/phases/install/monopile_install/__init__.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from .standard import MonopileInstallation diff --git a/ORBIT/phases/install/monopile_install/common.py b/ORBIT/phases/install/monopile_install/common.py index cdfa70b8..276ec2f1 100644 --- a/ORBIT/phases/install/monopile_install/common.py +++ b/ORBIT/phases/install/monopile_install/common.py @@ -1,9 +1,9 @@ """Common processes and cargo types for Monopile installations.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from marmot import false, process diff --git a/ORBIT/phases/install/monopile_install/standard.py b/ORBIT/phases/install/monopile_install/standard.py index 6e47b685..e8068450 100644 --- a/ORBIT/phases/install/monopile_install/standard.py +++ b/ORBIT/phases/install/monopile_install/standard.py @@ -1,9 +1,9 @@ """`MonopileInstallation` class and related processes.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import numpy as np @@ -223,7 +223,7 @@ def setup_simulation_with_feeders(self, **kwargs): for x in range(len(self.feeders)) ] - for assigned, feeder in zip(assignments, self.feeders): + for assigned, feeder in zip(assignments, self.feeders, strict=False): shuttle_items_to_queue_wait( feeder, port=self.port, diff --git a/ORBIT/phases/install/mooring_install/__init__.py b/ORBIT/phases/install/mooring_install/__init__.py index 6e6af116..0ba0d31d 100644 --- a/ORBIT/phases/install/mooring_install/__init__.py +++ b/ORBIT/phases/install/mooring_install/__init__.py @@ -1,9 +1,9 @@ """Mooring Installation Modules.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from .mooring import MooringSystemInstallation diff --git a/ORBIT/phases/install/mooring_install/mooring.py b/ORBIT/phases/install/mooring_install/mooring.py index d9a86d7d..dc220d0d 100644 --- a/ORBIT/phases/install/mooring_install/mooring.py +++ b/ORBIT/phases/install/mooring_install/mooring.py @@ -1,9 +1,9 @@ """Installation strategies for mooring systems.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from marmot import process diff --git a/ORBIT/phases/install/oss_install/__init__.py b/ORBIT/phases/install/oss_install/__init__.py index 25d46bea..71a6550e 100644 --- a/ORBIT/phases/install/oss_install/__init__.py +++ b/ORBIT/phases/install/oss_install/__init__.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from .floating import FloatingSubstationInstallation diff --git a/ORBIT/phases/install/oss_install/common.py b/ORBIT/phases/install/oss_install/common.py index 2e0db712..a987c56e 100644 --- a/ORBIT/phases/install/oss_install/common.py +++ b/ORBIT/phases/install/oss_install/common.py @@ -1,9 +1,9 @@ """Common processes and cargo types for Offshore Substation installations.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from marmot import process diff --git a/ORBIT/phases/install/oss_install/floating.py b/ORBIT/phases/install/oss_install/floating.py index c30ad1f5..44863c34 100644 --- a/ORBIT/phases/install/oss_install/floating.py +++ b/ORBIT/phases/install/oss_install/floating.py @@ -1,9 +1,9 @@ """`FloatingSubstationInstallation` and related processes.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2021, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2021, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from warnings import warn diff --git a/ORBIT/phases/install/oss_install/standard.py b/ORBIT/phases/install/oss_install/standard.py index 54e459c2..133eecd6 100644 --- a/ORBIT/phases/install/oss_install/standard.py +++ b/ORBIT/phases/install/oss_install/standard.py @@ -1,9 +1,9 @@ """`OffshoreSubstationInstallation` and related processes.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import simpy diff --git a/ORBIT/phases/install/quayside_assembly_tow/__init__.py b/ORBIT/phases/install/quayside_assembly_tow/__init__.py index e97eed02..1a7902f6 100644 --- a/ORBIT/phases/install/quayside_assembly_tow/__init__.py +++ b/ORBIT/phases/install/quayside_assembly_tow/__init__.py @@ -1,9 +1,9 @@ """Quayside assembly and tow-out modules.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from .moored import MooredSubInstallation diff --git a/ORBIT/phases/install/quayside_assembly_tow/common.py b/ORBIT/phases/install/quayside_assembly_tow/common.py index b62624ba..4dc14586 100644 --- a/ORBIT/phases/install/quayside_assembly_tow/common.py +++ b/ORBIT/phases/install/quayside_assembly_tow/common.py @@ -3,9 +3,9 @@ """ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from marmot import Agent, le, false, process diff --git a/ORBIT/phases/install/quayside_assembly_tow/gravity_base.py b/ORBIT/phases/install/quayside_assembly_tow/gravity_base.py index 63b2a349..f7d3384b 100644 --- a/ORBIT/phases/install/quayside_assembly_tow/gravity_base.py +++ b/ORBIT/phases/install/quayside_assembly_tow/gravity_base.py @@ -1,9 +1,9 @@ """Installation strategies for gravity-base substructures.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from warnings import warn @@ -82,15 +82,14 @@ def setup_simulation(self, **kwargs): self.initialize_port() self.initialize_substructure_production() - + if "wtiv" not in self.config: self.initialize_turbine_assembly() - + self.initialize_queue() self.initialize_towing_groups() self.initialize_support_vessel() - - + @property def system_capex(self): """Returns total procurement cost of the substructures.""" @@ -142,10 +141,12 @@ def initialize_substructure_production(self): self.env.register(a) a.start() - + if "wtiv" in self.config: - self.env.process(self.forward_substructures_to_assembly_storage(a)) - + self.env.process( + self.forward_substructures_to_assembly_storage(a) + ) + self.sub_assembly_lines.append(a) try: @@ -183,7 +184,6 @@ def initialize_turbine_assembly(self): self.env.register(a) a.start() self.turbine_assembly_lines.append(a) - def initialize_towing_groups(self, **kwargs): """ @@ -285,29 +285,32 @@ def detailed_output(self): # Start with the base operational delays delays = { - k: self.operational_delay(str(k)) - for k in self.sub_assembly_lines + k: self.operational_delay(str(k)) for k in self.sub_assembly_lines } # Add turbine assembly lines only if "wtiv" not in config if "wtiv" not in self.config: - delays.update({ - k: self.operational_delay(str(k)) - for k in self.turbine_assembly_lines - }) + delays.update( + { + k: self.operational_delay(str(k)) + for k in self.turbine_assembly_lines + } + ) # Add installation groups - delays.update({ - k: self.operational_delay(str(k)) - for k in self.installation_groups - }) + delays.update( + { + k: self.operational_delay(str(k)) + for k in self.installation_groups + } + ) # Add support vessel - delays[self.support_vessel] = self.operational_delay(str(self.support_vessel)) + delays[self.support_vessel] = self.operational_delay( + str(self.support_vessel) + ) - return { - "operational_delays": delays - } + return {"operational_delays": delays} def operational_delay(self, name): """Gathers the operational delays from the logs.""" @@ -317,22 +320,30 @@ def operational_delay(self, name): return delay - def forward_substructures_to_assembly_storage(self, SubstructureAssemblyLine): + def forward_substructures_to_assembly_storage( + self, SubstructureAssemblyLine + ): + """Move the substructures to the assembly storage area until ready for + assembly. + """ while True: # Wait until there is both: # - an item in wet_storage # - room in assembly_storage - if len(self.assembly_storage.items) < self.assembly_storage.capacity: + if ( + len(self.assembly_storage.items) + < self.assembly_storage.capacity + ): item = yield self.wet_storage.get() yield self.assembly_storage.put(item) - # submit action log item saying what happened "moved from wet to sub assembly storage" SubstructureAssemblyLine.submit_action_log( - "Move GBF from Wet Storage to Assembly Storage", 0 + "Move GBF from Wet Storage to Assembly Storage", 0 ) else: # Wait a short amount of time before trying again yield self.env.timeout(0.001) - + + @process def transfer_gbf_substructures_from_storage( group, diff --git a/ORBIT/phases/install/quayside_assembly_tow/moored.py b/ORBIT/phases/install/quayside_assembly_tow/moored.py index a550df78..ed1b03f8 100644 --- a/ORBIT/phases/install/quayside_assembly_tow/moored.py +++ b/ORBIT/phases/install/quayside_assembly_tow/moored.py @@ -1,9 +1,9 @@ """Installation strategies for moored floating systems.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from warnings import warn diff --git a/ORBIT/phases/install/scour_protection_install/__init__.py b/ORBIT/phases/install/scour_protection_install/__init__.py index b2f99b17..a7dafc60 100644 --- a/ORBIT/phases/install/scour_protection_install/__init__.py +++ b/ORBIT/phases/install/scour_protection_install/__init__.py @@ -1,6 +1,6 @@ __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" from .standard import ScourProtectionInstallation diff --git a/ORBIT/phases/install/scour_protection_install/standard.py b/ORBIT/phases/install/scour_protection_install/standard.py index 4b8a628a..7c57e4fa 100644 --- a/ORBIT/phases/install/scour_protection_install/standard.py +++ b/ORBIT/phases/install/scour_protection_install/standard.py @@ -1,9 +1,9 @@ """`ScourProtectionInstallation` and related processes.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" from math import ceil diff --git a/ORBIT/phases/install/turbine_install/__init__.py b/ORBIT/phases/install/turbine_install/__init__.py index 188a862e..278b5c31 100644 --- a/ORBIT/phases/install/turbine_install/__init__.py +++ b/ORBIT/phases/install/turbine_install/__init__.py @@ -1,6 +1,6 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from .standard import TurbineInstallation diff --git a/ORBIT/phases/install/turbine_install/common.py b/ORBIT/phases/install/turbine_install/common.py index 45106098..204df7e8 100644 --- a/ORBIT/phases/install/turbine_install/common.py +++ b/ORBIT/phases/install/turbine_install/common.py @@ -1,9 +1,9 @@ """Common processes and cargo types for Turbine Installations.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from marmot import process diff --git a/ORBIT/phases/install/turbine_install/standard.py b/ORBIT/phases/install/turbine_install/standard.py index f0afea5a..3d42d0b3 100644 --- a/ORBIT/phases/install/turbine_install/standard.py +++ b/ORBIT/phases/install/turbine_install/standard.py @@ -1,9 +1,9 @@ """`TurbineInstallation` class and related processes.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/ORBIT/supply_chain.py b/ORBIT/supply_chain.py index 2c09bd70..cf64a9f4 100644 --- a/ORBIT/supply_chain.py +++ b/ORBIT/supply_chain.py @@ -1,9 +1,9 @@ """Provides the ``SupplyChainManager`` model.""" __author__ = ["Jake Nunemaker"] -__copyright__ = "Copyright 2022, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2022, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = ["jake.nunemaker@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov"] from copy import deepcopy diff --git a/README.rst b/README.rst index dd424e52..58a12671 100644 --- a/README.rst +++ b/README.rst @@ -8,7 +8,7 @@ Offshore Renewables Balance of system and Installation Tool |Binder| |Pre-commit| |Black| |isort| |Ruff| :Authors: `Jake Nunemaker `_, `Matt Shields `_, `Rob Hammond `_, `Nick Riccobono `_ -:Documentation: `ORBIT Docs `_ +:Documentation: `ORBIT Docs `_ Installation ------------ @@ -56,7 +56,7 @@ Instructions conda deactivate 4. Clone the repository: - ``git clone https://github.com/WISDEM/ORBIT.git`` + ``git clone https://github.com/NLRWindSystems/ORBIT.git`` 5. Navigate to the top level of the repository (``/ORBIT/``) and install ORBIT as an editable package with following command. @@ -123,7 +123,7 @@ Recommended packages for easy iteration and running of code: .. |image| image:: https://img.shields.io/pypi/pyversions/orbit-nrel.svg :target: https://pypi.python.org/pypi/orbit-nrel .. |Binder| image:: https://mybinder.org/badge_logo.svg - :target: https://mybinder.org/v2/gh/WISDEM/ORBIT/dev?filepath=examples + :target: https://mybinder.org/v2/gh/NLRWindSystems/ORBIT/dev?filepath=examples .. |Pre-commit| image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white :target: https://github.com/pre-commit/pre-commit .. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 51285967..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,19 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_config.yml b/docs/_config.yml new file mode 100644 index 00000000..8d1eaa79 --- /dev/null +++ b/docs/_config.yml @@ -0,0 +1,127 @@ +# Book settings +# Learn more at https://jupyterbook.org/customize/config.html + +title: ORBIT +author: Jake Nunemaker, Matt Shields, Rob Hammond, Nick Riccobono +# logo: logo.png +copyright: "2026 Alliance for Energy Innovation, LLC" +only_build_toc_files: false + +exclude_patterns: [_build, Thumbs.db, .DS_Store, "**.ipynb_checkpoints"] + +execute: + execute_notebooks: auto + timeout: -1 + allow_errors: true + exclude_patterns: + - _build + - Thumbs.db + - DS_Store + - "**.ipynb_checkpoints" + +# Define the name of the latex output file for PDF builds +latex: + latex_documents: + targetname: book.tex + +# Add a bibtex file so that we can create citations +bibtex_bibfiles: + - refs.bib + +bibliography: + - refs.bib + +# Information about where the book exists on the web +repository: + url: https://github.com/NLRWindSystems/ORBIT + path_to_book: docs # Optional path to your book, relative to the repository root + branch: main # Which branch of the repository should be used when creating links (optional) + +# Add GitHub buttons to your book +# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository +html: + use_issues_button: true + use_repository_button: true + use_edit_page_button: true + home_page_in_navbar: false + +intersphinx_mapping: + python: https://docs.python.org/3.12 + sphinx: https://www.sphinx-doc.org/en/3.x + +suppress_warnings: ["myst.domains"] + +parse: + myst_url_schemes: [mailto, http, https] + myst_heading_anchors: 3 + myst_enable_extensions: + - dollarmath + - amsmath + - deflist + - linkify + - colon_fence + # - html_admonition + # - html_image + # - smartquotes + # - replacements + # - substitution + +sphinx: + extra_extensions: + - myst_nb + - sphinx_book_theme + - sphinx.ext.autodoc + - sphinx.ext.autosectionlabel + - sphinx.ext.autosummary + - sphinx.ext.coverage + - sphinx.ext.napoleon + - sphinx.ext.viewcode + - sphinx_autodoc_typehints + - sphinxcontrib.autoyaml + - sphinxcontrib.bibtex + config: + bibtex_bibfiles: ["refs.bib"] + templates_path: ["_templates"] + html_theme: sphinx_book_theme + html_theme_options: + icon_links: [ + { + name: GitHub, + url: "https://github.com/NLRWindSystems/ORBIT", + icon: fa-brands fa-github, + }, + { + name: PyPI version, + url: "https://pypi.org/project/orbit-nrel/", + icon: "https://img.shields.io/pypi/v/orbit-nrel", + type: url, + }, + { + name: Launch Binder, + url: "https://mybinder.org/v2/gh/NLRWindSystems/ORBIT/main?filepath=examples", + icon: "https://mybinder.org/badge_logo.svg", + type: url, + }, + ] + language: 'python' + autoyaml_level: 3 + autosummary_generate: true + autosectionlabel_prefix_document: true + autodoc_default_options: + members: true + member-order: bysource + undoc-members: true + private-members: true + # special-members: true + # inherited-members + # show-inheritance + # ignore-module-all + # imported-members: true + # exclude-members + # class-doc-from + # no-value + autodoc_typehints: description + napoleon_google_docstring: false + napoleon_use_admonition_for_notes: true + napoleon_use_rtype: false + nb_merge_streams: true diff --git a/docs/source/_static/css/style.css b/docs/_static/css/style.css similarity index 100% rename from docs/source/_static/css/style.css rename to docs/_static/css/style.css diff --git a/docs/_toc.yml b/docs/_toc.yml new file mode 100644 index 00000000..cee72272 --- /dev/null +++ b/docs/_toc.yml @@ -0,0 +1,88 @@ +# Table of contents +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: index +parts: +- caption: Getting Started + chapters: + - file: getting_started/overview + - file: getting_started/bos + - file: getting_started/install +- caption: User Guide + chapters: + - file: tutorials/index + sections: + - file: tutorials/introduction + - file: tutorials/project_manager + - file: tutorials/available_outputs + - file: tutorials/parametric_manager + - file: topical_guides/index + sections: + - file: topical_guides/fixed_bottom_installations + - file: topical_guides/supply_chains + - file: topical_guides/custom_array + - file: topical_guides/hvdc_hvac_export + - file: topical_guides/cable_installation +- caption: API Reference + chapters: + - file: api/ProjectManager + - file: api/ParametricManager + - file: api/DesignPhase + sections: + - file: api/phases/design/MonopileDesign + - file: api/phases/design/ScourProtectionDesign + - file: api/phases/design/CableHelpers + - file: api/phases/design/ArraySystemDesign + - file: api/phases/design/ExportSystemDesign + - file: api/phases/design/ElectricalDesign + - file: api/phases/design/OffshoreSubstationDesign + - file: api/phases/design/SemiSubmersibleDesign + - file: api/phases/design/SparDesign + - file: api/phases/design/MooringSystemDesign + - file: api/InstallPhase + sections: + - file: api/phases/install/MonopileInstallation + - file: api/phases/install/JacketInstall + - file: api/phases/install/ScourProtectionInstall + - file: api/phases/install/TurbineInstallation + - file: api/phases/install/ArrayCableInstall + - file: api/phases/install/ExportCableInstall + - file: api/phases/install/OffshoreSubstationInstall + - file: api/phases/install/MooredSubInstallation + - file: api/phases/install/MooringSystemInstallation + - file: api/phases/install/GravityBasedInstallation +- caption: Methodology + chapters: + - file: methods/ProjectManager + - file: methods/ParametricManager + - file: methods/DesignPhase + sections: + - file: methods/design/MonopileDesign + - file: methods/design/ScourProtectionDesign + - file: methods/design/CableHelpers + - file: methods/design/ArraySystemDesign + - file: methods/design/ExportSystemDesign + - file: methods/design/ElectricalDesign + - file: methods/design/OffshoreSubstationDesign + - file: methods/design/SemiSubmersibleDesign + - file: methods/design/SparDesign + - file: methods/design/MooringSystemDesign + - file: methods/InstallPhase + sections: + - file: methods/install/MonopileInstall + - file: methods/install/JacketInstall + - file: methods/install/ScourProtectionInstall + - file: methods/install/TurbineInstall + - file: methods/install/ArrayCableInstall + - file: methods/install/ExportCableInstall + - file: methods/install/OffshoreSubstationInstall + - file: methods/install/MooredSubInstallation + - file: methods/install/MooringSystemInstallation + - file: methods/install/GravityBasedInstallation + - file: methods/CommonCost +- caption: About + chapters: + - file: publications + - file: ../CHANGELOG + - file: team diff --git a/docs/api/DesignPhase.md b/docs/api/DesignPhase.md new file mode 100644 index 00000000..1b401a14 --- /dev/null +++ b/docs/api/DesignPhase.md @@ -0,0 +1,31 @@ +(design-phases)= +# Design Phases + +ORBIT includes the following design modules that can be used within +ProjectManager. These design processes are intended to broadly capture scaling +trends but are not intended to be used for actual designs. + +## Substructures + +### Fixed-Bottom + +- [Monopile](#monopile-design-api) + +- [Scouring Protection](#scour-protection-design-api) + +### Floating + +- [Semi-Submersible](#semi-submersible-design-api) +- [Spar](#spar-design-api) +- [Mooring System](#mooring-design-api) + +## Cabling + +- [Cable Helpers](#cable-helpers-api) +- [Array System](#array-design-api) +- [Electrical System](#electrical-design-api) +- [Export System](#export-design-api) + +## Substation + +- [Offshore Substation](#oss-design-api) diff --git a/docs/api/InstallPhase.md b/docs/api/InstallPhase.md new file mode 100644 index 00000000..a3720a11 --- /dev/null +++ b/docs/api/InstallPhase.md @@ -0,0 +1,31 @@ +(install-phases)= +# Install Phases + +ORBIT includes the following installation modules that can be used within +ProjectManager. These modules utilize SimPy to model individual processes and +their constraints due to weather and vessel interactions. For a more detailed +description of vessel scheduling within ORBIT, please see `add link`. + +## Substructures + +### Fixed-Bottom + +- [Monopile](#monopile-install-api) +- [Jacket](#jacket-install-api) +- [Scouring Protection](#scour-protection-install-api) + +### Floating + +- [Moored Substructure](#moored-sub-install-api) +- [Gravity-Based Substructure](#gravity-install-api) +- [Mooring System](#mooring-install-api) + +## Cabling + +- [Array System](#array-install-api) +- [Export System](#export-install-api) + +## Substation & Turbine + +- [Turbine](#turbine-install-api) +- [Offshore Substation](#oss-install-api) diff --git a/docs/api/ParametricManager.md b/docs/api/ParametricManager.md new file mode 100644 index 00000000..e1ab86e4 --- /dev/null +++ b/docs/api/ParametricManager.md @@ -0,0 +1,7 @@ +(parametric-manager-api)= +# Parametric Configurations + +```{eval-rst} +.. autoclass:: ORBIT.ParametricManager + :members: +``` diff --git a/docs/api/ProjectManager.md b/docs/api/ProjectManager.md new file mode 100644 index 00000000..d74eff85 --- /dev/null +++ b/docs/api/ProjectManager.md @@ -0,0 +1,7 @@ +(project-manager-api)= +# Project Configuration and Management + +```{eval-rst} +.. autoclass:: ORBIT.manager.ProjectManager + :members: +``` diff --git a/docs/source/phases/design/api_ArraySystemDesign.rst b/docs/api/phases/design/ArraySystemDesign.md similarity index 57% rename from docs/source/phases/design/api_ArraySystemDesign.rst rename to docs/api/phases/design/ArraySystemDesign.md index d89817e2..2501abd1 100644 --- a/docs/source/phases/design/api_ArraySystemDesign.rst +++ b/docs/api/phases/design/ArraySystemDesign.md @@ -1,11 +1,14 @@ -Array System Design API -======================= +(array-design-api)= +# Array System Design For detailed methodology, please see -:doc:`Array System Design `. +{doc}`Array System Design <../../../methods/design/ArraySystemDesign>`. +```{eval-rst} .. autoclass:: ORBIT.phases.design.ArraySystemDesign :members: .. autoclass:: ORBIT.phases.design.CustomArraySystemDesign :members: + +``` diff --git a/docs/source/phases/design/api_CableHelpers.rst b/docs/api/phases/design/CableHelpers.md similarity index 64% rename from docs/source/phases/design/api_CableHelpers.rst rename to docs/api/phases/design/CableHelpers.md index d7979485..16c437c0 100644 --- a/docs/source/phases/design/api_CableHelpers.rst +++ b/docs/api/phases/design/CableHelpers.md @@ -1,9 +1,10 @@ -Cabling Helper Classes -====================== +(cable-helpers-api)= +# Cabling Helper Classes For detailed methodology, please see -:doc:`Cable Helper Design `. +{doc}`Cable Helper Design <../../../methods/design/CableHelpers>`. +```{eval-rst} .. autoclass:: ORBIT.phases.design._cables.Cable :members: @@ -12,3 +13,4 @@ For detailed methodology, please see .. autoclass:: ORBIT.phases.design._cables.CableSystem :members: +``` diff --git a/docs/api/phases/design/ElectricalDesign.md b/docs/api/phases/design/ElectricalDesign.md new file mode 100644 index 00000000..e33d37ee --- /dev/null +++ b/docs/api/phases/design/ElectricalDesign.md @@ -0,0 +1,10 @@ +(electrical-design-api)= +# Electrical System Design + +For detailed methodology, please see +{doc}`Electrical System Design <../../../methods/design/ElectricalDesign>`. + +```{eval-rst} +.. autoclass:: ORBIT.phases.design.ElectricalDesign + :members: +``` diff --git a/docs/api/phases/design/ExportSystemDesign.md b/docs/api/phases/design/ExportSystemDesign.md new file mode 100644 index 00000000..56b3d8c0 --- /dev/null +++ b/docs/api/phases/design/ExportSystemDesign.md @@ -0,0 +1,10 @@ +(export-design-api)= +# Export System Design + +For detailed methodology, please see +{doc}`Export System Design <../../../methods/design/ExportSystemDesign>`. + +```{eval-rst} +.. autoclass:: ORBIT.phases.design.ExportSystemDesign + :members: +``` diff --git a/docs/api/phases/design/MonopileDesign.md b/docs/api/phases/design/MonopileDesign.md new file mode 100644 index 00000000..1a2f8b8c --- /dev/null +++ b/docs/api/phases/design/MonopileDesign.md @@ -0,0 +1,10 @@ +(monopile-design-api)= +# Monopile Design + +For detailed methodology, please see the +[Monopile Design methodology documentation](#monopile-design-methods). + +```{eval-rst} +.. autoclass:: ORBIT.phases.design.MonopileDesign + :members: +``` diff --git a/docs/api/phases/design/MooringSystemDesign.md b/docs/api/phases/design/MooringSystemDesign.md new file mode 100644 index 00000000..1e32587e --- /dev/null +++ b/docs/api/phases/design/MooringSystemDesign.md @@ -0,0 +1,10 @@ +(mooring-design-api)= +# Mooring System Design + +For detailed methodology, please see +{doc}`Mooring System Design <../../../methods/design/MooringSystemDesign>`. + +```{eval-rst} +.. autoclass:: ORBIT.phases.design.MooringSystemDesign + :members: +``` diff --git a/docs/api/phases/design/OffshoreSubstationDesign.md b/docs/api/phases/design/OffshoreSubstationDesign.md new file mode 100644 index 00000000..0a399a67 --- /dev/null +++ b/docs/api/phases/design/OffshoreSubstationDesign.md @@ -0,0 +1,10 @@ +(oss-design-api)= +# Offshore Substation Design + +For detailed methodology, please see +{doc}`Offshore Substation Design <../../../methods/design/OffshoreSubstationDesign>`. + +```{eval-rst} +.. autoclass:: ORBIT.phases.design.OffshoreSubstationDesign + :members: +``` diff --git a/docs/api/phases/design/ScourProtectionDesign.md b/docs/api/phases/design/ScourProtectionDesign.md new file mode 100644 index 00000000..68f1320c --- /dev/null +++ b/docs/api/phases/design/ScourProtectionDesign.md @@ -0,0 +1,10 @@ +(scour-protection-design-api)= +# Scour Protection Design + +For detailed methodology, please see +{doc}`Scour Protection Design <../../../methods/design/ScourProtectionDesign>`. + +```{eval-rst} +.. autoclass:: ORBIT.phases.design.ScourProtectionDesign + :members: +``` diff --git a/docs/api/phases/design/SemiSubmersibleDesign.md b/docs/api/phases/design/SemiSubmersibleDesign.md new file mode 100644 index 00000000..0c8ba07a --- /dev/null +++ b/docs/api/phases/design/SemiSubmersibleDesign.md @@ -0,0 +1,10 @@ +(semi-submersible-design-api)= +# Semi-Submersible Design + +For detailed methodology, please see +{doc}`Semi-Submersible Design <../../../methods/design/SemiSubmersibleDesign>`. + +```{eval-rst} +.. autoclass:: ORBIT.phases.design.SemiSubmersibleDesign + :members: +``` diff --git a/docs/api/phases/design/SparDesign.md b/docs/api/phases/design/SparDesign.md new file mode 100644 index 00000000..0682c96a --- /dev/null +++ b/docs/api/phases/design/SparDesign.md @@ -0,0 +1,10 @@ +(spar-design-api)= +# Spar Design + +For detailed methodology, please see +{doc}`Spar Design <../../../methods/design/SparDesign>`. + +```{eval-rst} +.. autoclass:: ORBIT.phases.design.SparDesign + :members: +``` diff --git a/docs/api/phases/install/ArrayCableInstall.md b/docs/api/phases/install/ArrayCableInstall.md new file mode 100644 index 00000000..2bc45574 --- /dev/null +++ b/docs/api/phases/install/ArrayCableInstall.md @@ -0,0 +1,10 @@ +(array-install-api)= +# Array Cabling System Installation + +For detailed methodology, please see the +[Array Cabling Installation methodology documentation](#array-install-methods) + +```{eval-rst} +.. autoclass:: ORBIT.phases.install.ArrayCableInstallation + :members: +``` diff --git a/docs/api/phases/install/ExportCableInstall.md b/docs/api/phases/install/ExportCableInstall.md new file mode 100644 index 00000000..5bbbe8dc --- /dev/null +++ b/docs/api/phases/install/ExportCableInstall.md @@ -0,0 +1,10 @@ +(export-install-api)= +# Export Cabling System Installation + +For detailed methodology, please see the +[Export Cable Installation methodology documentation](#export-install-methods) + +```{eval-rst} +.. autoclass:: ORBIT.phases.install.ExportCableInstallation + :members: +``` diff --git a/docs/api/phases/install/GravityBasedInstallation.md b/docs/api/phases/install/GravityBasedInstallation.md new file mode 100644 index 00000000..20af67de --- /dev/null +++ b/docs/api/phases/install/GravityBasedInstallation.md @@ -0,0 +1,10 @@ +(gravity-install-api)= +# Gravity-Based Foundation Installation + +For detailed methodology, please see the +[Gravity-Based Foundation Installation methodology documentation](#gravity-install-methods) + +```{eval-rst} +.. autoclass:: ORBIT.phases.install.GravityBasedInstallation + :members: +``` diff --git a/docs/api/phases/install/JacketInstall.md b/docs/api/phases/install/JacketInstall.md new file mode 100644 index 00000000..f40b680e --- /dev/null +++ b/docs/api/phases/install/JacketInstall.md @@ -0,0 +1,14 @@ +(jacket-install-api)= +# Jacket Installation + +For detailed methodology, please see the +[Jacket Installation methodology documentation](#jacket-install-methods). + +```{eval-rst} +.. automodule:: ORBIT.phases.install.monopile_install.standard + :members: + :exclude-members: extract_vessel_specs + +.. automodule:: ORBIT.phases.install.monopile_install.common + :members: +``` diff --git a/docs/api/phases/install/MonopileInstallation.md b/docs/api/phases/install/MonopileInstallation.md new file mode 100644 index 00000000..a1ff5750 --- /dev/null +++ b/docs/api/phases/install/MonopileInstallation.md @@ -0,0 +1,14 @@ +(monopile-install-api)= +# Monopile Installation + +For detailed methodology, please see the +[Monopile Installation methodology documentation](#monopile-install-methods) + +```{eval-rst} +.. automodule:: ORBIT.phases.install.monopile_install.standard + :members: + :exclude-members: extract_vessel_specs + +.. automodule:: ORBIT.phases.install.monopile_install.common + :members: +``` diff --git a/docs/api/phases/install/MooredSubInstallation.md b/docs/api/phases/install/MooredSubInstallation.md new file mode 100644 index 00000000..012edce0 --- /dev/null +++ b/docs/api/phases/install/MooredSubInstallation.md @@ -0,0 +1,10 @@ +(moored-sub-install-api)= +# Moored Substructure Installation + +For detailed methodology, please see the +[Moored Substructure Installation methodology documentation](#moored-sub-install-methods) + +```{eval-rst} +.. autoclass:: ORBIT.phases.install.MooredSubInstallation + :members: +``` diff --git a/docs/api/phases/install/MooringSystemInstallation.md b/docs/api/phases/install/MooringSystemInstallation.md new file mode 100644 index 00000000..319f34d6 --- /dev/null +++ b/docs/api/phases/install/MooringSystemInstallation.md @@ -0,0 +1,10 @@ +(mooring-install-api)= +# Mooring System Installation + +For detailed methodology, please see the +[Mooring System Installation methodology documentation](#mooring-install-methods) + +```{eval-rst} +.. autoclass:: ORBIT.phases.install.MooringSystemInstallation + :members: +``` diff --git a/docs/api/phases/install/OffshoreSubstationInstall.md b/docs/api/phases/install/OffshoreSubstationInstall.md new file mode 100644 index 00000000..5192d02c --- /dev/null +++ b/docs/api/phases/install/OffshoreSubstationInstall.md @@ -0,0 +1,10 @@ +(oss-install-api)= +# Offshore Substation Installation + +For detailed methodology, please see the +[Offshore Substation Installation methodology documentation](#oss-install-methods) + +```{eval-rst} +.. autoclass:: ORBIT.phases.install.OffshoreSubstationInstallation + :members: +``` diff --git a/docs/api/phases/install/ScourProtectionInstall.md b/docs/api/phases/install/ScourProtectionInstall.md new file mode 100644 index 00000000..0c0b9aa1 --- /dev/null +++ b/docs/api/phases/install/ScourProtectionInstall.md @@ -0,0 +1,10 @@ +(scour-protection-install-api)= +# Scour Protection Installation + +For detailed methodology, please see the +[Scour Protection Installation methodology documentation](#scour-protection-install-methods) + +```{eval-rst} +.. autoclass:: ORBIT.phases.install.ScourProtectionInstallation + :members: +``` diff --git a/docs/api/phases/install/TurbineInstallation.md b/docs/api/phases/install/TurbineInstallation.md new file mode 100644 index 00000000..1e837be1 --- /dev/null +++ b/docs/api/phases/install/TurbineInstallation.md @@ -0,0 +1,14 @@ +(turbine-install-api)= +# Turbine Installation + +For detailed methodology, please see the +[Turbine Installation methodology documentation](#turbine-install-methods) + +```{eval-rst} +.. automodule:: ORBIT.phases.install.turbine_install.standard + :members: + :exclude-members: extract_vessel_specs + +.. automodule:: ORBIT.phases.install.turbine_install.common + :members: +``` diff --git a/docs/build_book.sh b/docs/build_book.sh new file mode 100644 index 00000000..e5c2d317 --- /dev/null +++ b/docs/build_book.sh @@ -0,0 +1,4 @@ +rm -rf _build +jupyter-book build . +cp -f _build/jupyter_execute/tutorials/*.ipynb ../examples/ +cp -f _build/jupyter_execute/topical_guides/*.ipynb ../examples/ diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index 508ba970..00000000 --- a/docs/conf.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -Configuration file for the Sphinx documentation builder. - -Jake Nunemaker -National Renewable Energy Lab -09/13/2019 -""" - -# -- Path setup -------------------------------------------------------------- - -import ORBIT - -# -- Project information ----------------------------------------------------- -project = "ORBIT" -copyright = "2020, National Renewable Energy Lab" # noqa: A001 -author = "Jake Nunemaker, Matt Shields, Rob Hammond" -release = ORBIT.__version__ - - -# -- General configuration --------------------------------------------------- -extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.coverage", - "sphinx.ext.napoleon", - "sphinx.ext.autosectionlabel", -] - -master_doc = "contents" -autodoc_member_order = "bysource" - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] - - -# -- Options for HTML output ------------------------------------------------- -html_theme = "sphinx_rtd_theme" -html_static_path = ["_static"] - -# Add any paths that contain custom themes here, relative to this directory. -html_theme_path = ["_themes"] - -html_theme_options = {"display_version": True, "body_max_width": "70%"} - -# Napoleon options -napoleon_google_docstring = False -napoleon_use_param = False -napoleon_use_ivar = True diff --git a/docs/contents.rst b/docs/contents.rst deleted file mode 100644 index 664d14a5..00000000 --- a/docs/contents.rst +++ /dev/null @@ -1,29 +0,0 @@ -.. _contents: - -======================= -Documentation for ORBIT -======================= - -Contents: -========= - -.. toctree:: - :maxdepth: 2 - - index - source/intro/index - source/installation/index - source/tutorial/index - source/examples - source/api - source/methods - source/publications/index - source/changelog - source/team - - -Indices and search page -======================= - -* :ref:`genindex` -* :ref:`search` diff --git a/docs/source/intro/bos.rst b/docs/getting_started/bos.md similarity index 59% rename from docs/source/intro/bos.rst rename to docs/getting_started/bos.md index 44806dbd..1e6f0b64 100644 --- a/docs/source/intro/bos.rst +++ b/docs/getting_started/bos.md @@ -1,5 +1,5 @@ -Offshore Wind Balance of System Cost Modeling -============================================= +(bos-intro)= +# Offshore Wind Balance of System Cost Modeling The balance-of-system (BOS) costs of an offshore wind plant include: @@ -10,20 +10,20 @@ The balance-of-system (BOS) costs of an offshore wind plant include: - Onshore construction costs required to connect the turbine to the grid - Port fees and commissioning costs -.. note:: - - ORBIT does not specify a dollar-year when calculating BOS cost and it does - not account for inflation. To provide a flexible and adaptable simulation - model, components of the wind plant may incorporate `default` cost values. - Please advise that these values are approximated using avaiable information - or best-guess. To improve the fidelity of this tool, users should consider - replacing the `default` values with better informed costs. +:::{note} +ORBIT does not specify a dollar-year when calculating BOS cost and it does +not account for inflation. To provide a flexible and adaptable simulation +model, components of the wind plant may incorporate `default` cost values. +Please advise that these values are approximated using avaiable information +or best-guess. To improve the fidelity of this tool, users should consider +replacing the `default` values with better informed costs. +::: Evaluating BOS costs is complicated by the large number of design choices for each component, the impact of weather delays on the installation processes, the challenge of transporting and hoisting large components at sea, the variation in geospatial characteristics between different projects, and the limited -number of vessels capable of conducting these operations. The broad design +number of vessels capable of conducting these operations. The broad design space associated with the BOS costs of an offshore wind project provides significant opportunities for project developers to reduce overall costs through innovative technologies and/or installation methodologies. @@ -31,4 +31,4 @@ through innovative technologies and/or installation methodologies. Some additional resources that describe offshore wind BOS in greater detail are listed below -- `Guide to an Offshore Wind Farm (BVG Associates) `_ +- [Guide to an Offshore Wind Farm (BVG Associates)](https://guidetoanoffshorewindfarm.com/) diff --git a/docs/getting_started/install.md b/docs/getting_started/install.md new file mode 100644 index 00000000..39be97aa --- /dev/null +++ b/docs/getting_started/install.md @@ -0,0 +1,65 @@ +(installation)= +# Installing ORBIT + +```console +pip install orbit-nrel +``` + +## Development Setup + +The steps below are for more advanced users that would like to modify and +and contribute to ORBIT. + +A couple of notes before you get started: + +- It is assumed that you will be using the terminal on MacOS/Linux or the + Anaconda Prompt on Windows. The instructions refer to both as the + "terminal", and unless otherwise noted the commands will be the same. +- To verify git is installed, run `git --version` in the terminal. If an error + occurs, install git using these [directions](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git). + +### Instructions + +1. Download the latest version of [Miniconda](https://docs.conda.io/en/latest/miniconda.html) + for the appropriate OS. Follow the remaining [steps](https://conda.io/projects/conda/en/latest/user-guide/install/index.html#regular-installation) + for the appropriate OS version. + +2. From the terminal, install pip by running: `conda install -c anaconda pip` + +3. Next, create a new environment for the project with the following. Change "orbit" to whatever + name you would like to give your environment, and "3.13" to whichever compatible version of + Python you prefer. + + ```console + conda create -n orbit python=3.13 -y + ``` + + To activate/deactivate the environment, use the following commands. + + ```console + conda activate orbit + conda deactivate orbit + ``` + +4. Clone the repository: + + ```bash + git clone https://github.com/NLRWindSystems/ORBIT.git + ``` + +5. Navigate to the top level of the repository (`/ORBIT/`) and install ORBIT as an + editable package with following command. + + ```console + # Note the "." at the end + pip install -e . + + # OR if you are you going to be contributing to the code or building documentation + pip install -e '.[dev]' + ``` + +6. (Development only) Install the pre-commit hooks to autoformat and lint code. + + ```console + pre-commit install + ``` diff --git a/docs/source/intro/overview.rst b/docs/getting_started/overview.md similarity index 69% rename from docs/source/intro/overview.rst rename to docs/getting_started/overview.md index d13247bb..d2373a47 100644 --- a/docs/source/intro/overview.rst +++ b/docs/getting_started/overview.md @@ -1,7 +1,7 @@ -ORBIT -===== +(orbit-intro)= +# Model Overview -ORBIT is a tool developed by the National Renewable Energy Lab (NREL) for +ORBIT is a tool developed by the National Laboratory of the Rockies (NLR) for performing process-based bottom up installation and cost modeling for the offshore wind balance of system process. It is intended to be used for tradeoff studies, installation logistics research, and for modeling overall balance of @@ -9,17 +9,18 @@ system costs. Each module captures the main drivers of installation time and cost in a highly customizable framework, allowing the user to override any default values if they wish. -The primary structure of ORBIT relies on the :ref:`Project Manager ` +The primary structure of ORBIT relies on the [Project Manager](#project-manager-methods) to intrepret the user specified configuration. Refer to the -`library/project/config `. -The ``ProjectManager`` calls ``DesignPhase`` to include all the wind farm components that -comprise the balance of system, then it calls ``InstallationPhase`` to schedule all the installation -processes for each component. Available design phases can be found :ref:`here ` and -installation phases can be found :ref:`here `. Details about the design -and installation phases are presented throughout this documents page. - -Design Phase ------------- +[library/project/config](https://github.com/NLRWindSystems/ORBIT/tree/main/library/project/config). +The `ProjectManager` calls `DesignPhase` to include all the wind farm components that +comprise the balance of system, then it calls `InstallationPhase` to schedule all the installation +processes for each component. Available design phases and their helper functionality can be found +in the [design phases methodology documentation](#design-methods) and +installation phases can be found in the [installation phases methodology documentation](#install-methods). +Details about the design and installation phases are presented throughout this documents page. + +## Design Phase + ORBIT is very modular by design to allow a user to define an offshore wind plant in many different configurations and simulate its design and installation. Modules in ORBIT represent the design of different components for @@ -29,9 +30,8 @@ installation, etc.). The modularity will allow for novel technologies or installation strategies to be easily introduced and compared with baseline methodologies as the industry develops. +## Installation Phases -Installation Phases -------------------- In ORBIT, each installation phase of an offshore wind project development is defined by a series of discrete processes that represent the installation of a component. Durations and respective costs of each of these processes are then @@ -44,10 +44,10 @@ framework, where each process must satisfy operational constraints (weather or vessel interactions) before it proceeds. In this way, weather delays can be accounted for in the installation process, and the associated impact on project risk and construction phase sequencing can be determined. The DES framework of -ORBIT is built using the python package `SimPy `_. +ORBIT is built using the python package [SimPy](https://simpy.readthedocs.io/en/latest/). + +## Library -Library -------- A library directory includes data/specifications on cables, vessels, ports, turbines, and example projects in the form of yaml files. Users can copy and modify or create entirely new yaml files to customize their project. By sticking to the same file structure, users simply have to update the diff --git a/docs/source/images/ElectricalDesignConfig.png b/docs/images/ElectricalDesignConfig.png similarity index 100% rename from docs/source/images/ElectricalDesignConfig.png rename to docs/images/ElectricalDesignConfig.png diff --git a/docs/source/images/cost_by_procurement.png b/docs/images/cost_by_procurement.png similarity index 100% rename from docs/source/images/cost_by_procurement.png rename to docs/images/cost_by_procurement.png diff --git a/docs/source/images/examples/full_grid_example.png b/docs/images/examples/full_grid_example.png similarity index 100% rename from docs/source/images/examples/full_grid_example.png rename to docs/images/examples/full_grid_example.png diff --git a/docs/source/images/examples/full_ring_example.png b/docs/images/examples/full_ring_example.png similarity index 100% rename from docs/source/images/examples/full_ring_example.png rename to 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docs/source/images/process_diagrams/turbine_install.png rename to docs/images/process_diagrams/turbine_install.png diff --git a/docs/source/images/process_diagrams/turbine_single_wtiv.png b/docs/images/process_diagrams/turbine_single_wtiv.png similarity index 100% rename from docs/source/images/process_diagrams/turbine_single_wtiv.png rename to docs/images/process_diagrams/turbine_single_wtiv.png diff --git a/docs/source/images/process_diagrams/turbine_wtiv_with_feeders.png b/docs/images/process_diagrams/turbine_wtiv_with_feeders.png similarity index 100% rename from docs/source/images/process_diagrams/turbine_wtiv_with_feeders.png rename to docs/images/process_diagrams/turbine_wtiv_with_feeders.png diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..af5ea58c --- /dev/null +++ b/docs/index.md @@ -0,0 +1,28 @@ +# ORBIT + +## Overview + +The Offshore Renewables Balance of system and Installation Tool (ORBIT) is a +model developed by the National Laboratory of the Rockies (NLR) to study +the cost and times associated with Offshore Wind Balance of System (BOS) +processes. + +ORBIT includes many different modules that can be used to model phases within +the BOS process, split into {ref}`design ` and +{ref}`installation `. It is highly flexible and allows the user +to define which phases are needed to model their project or scenario using +[`ProjectManager`](project-manager-methods) for a single analysis, or the +[`ParametricManager`](#parametric-manager-methods) for a parameter sweep of +analysis setttings. + +ORBIT is written in Python 3.10 and utilizes +[SimPy](https://simpy.readthedocs.io/en/latest/)'s discrete event simulation +framework to model individual processes during the installation phases, +allowing for the effects of weather delays and vessel interactions to be +studied. + +## License + +Apache 2.0. Please see the +[repository](https://github.com/NLRWindSystems/ORBIT/blob/main/LICENSE) +for license information. diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index ffeab886..00000000 --- a/docs/index.rst +++ /dev/null @@ -1,58 +0,0 @@ -.. sidebar:: Documentation - - :ref:`Introduction ` - A quick introduction to the project. - - :ref: `Installation and Contribution ` - How to install and contribute to ORBIT. - - :ref:`Tutorial ` - Basic tutorial for working with ORBIT. - - :ref:`Examples ` - Advanced examples and real world validation cases. - - :ref:`API Reference ` - Detailed description of ORBIT's API. - - :ref:`Methodology ` - References and descriptions of the underlying engineering models. - - :ref:`Publications ` - Publications related to ORBIT. - - :ref:`Changelog ` - ORBIT Changelog - - :ref:`Team ` - List of authors and contributors. - -ORBIT -===== - -Overview --------- - -The Offshore Renewables Balance of system and Installation Tool (ORBIT) is a -model developed by the National Renewable Energy Lab (NREL) to study -the cost and times associated with Offshore Wind Balance of System (BOS) -processes. - -ORBIT includes many different modules that can be used to model phases within -the BOS process, split into :ref:`design ` and -:ref:`installation `. It is highly flexible and allows the user to -define which phases are needed to model their project or scenario using -:ref:`ProjectManager `. - -ORBIT is written in Python 3.10 and utilizes -`SimPy `_'s discrete event simulation -framework to model individual processes during the installation phases, -allowing for the effects of weather delays and vessel interactions to be -studied. - -License -------- - -Apache 2.0. Please see the -`repository `_ for -license information. diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 27f573b8..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd diff --git a/docs/source/doc_CommonCost.rst b/docs/methods/CommonCost.md similarity index 52% rename from docs/source/doc_CommonCost.rst rename to docs/methods/CommonCost.md index 5cdcc9a8..28c6b730 100644 --- a/docs/source/doc_CommonCost.rst +++ b/docs/methods/CommonCost.md @@ -1,19 +1,16 @@ -.. _coststoc: - -Common Costs -============= +(common-costs-methods)= +# Common Costs Establishing cost values for each installation process, component design, and project parameter is essential to ORBIT's ability to calculate CapEx. Common costs and cost rates were specified as default values in each module and thus spread out across multiple files. Further, as modules were added or updated over time, these common costs were either updated -or were simply left as an older value. As of v1.2 (see :doc:`changelog`), all the common costs in the design modules -were centralized under ``common_cost.yaml``. That way, users can access a single file and +or were simply left as an older value. As of v1.2 (see {doc}`../../CHANGELOG`), all the common costs in the design modules +were centralized under `common_cost.yaml`. That way, users can access a single file and update any number of costs they wish to change. +## Cost by Procurement Year -Cost by Procurement Year -~~~~~~~~~~~~~~~~~~~~~~~~ Several users and industries partners have noted that some costs are out of date and therefore may not fully represent a project. We applied method to adjust to account for commodity, consumer, and labor indices as well as inflation to update the common cost of @@ -22,27 +19,26 @@ and it shows the `Class`, `attribute_name`, `units`, and the cost value in 2024 future releases, these costs will adjust based on the market indices so any user can be sure that the common costs in their model is not outdated. -.. figure:: images/cost_by_procurement.png - :align: center - - Cost by Procurement Year. Note that values are inflated from the procurement year USD to 2024 USD using CPI +:::{figure} ../imagesimages/cost_by_procurement.png +:align: center +Cost by Procurement Year. Note that values are inflated from the procurement year USD to 2024 USD using CPI +::: The spreadsheet above is for tracking purposes, and the common cost values are added to the -files shown below. Costs for all the ``DesignPhases`` are stored in ``common_cost.yaml``; cable costs -are stored in each cable file in the ``library/cables`` folder; vessel costs are stored -in each vessel file in the ``library/vessels`` folder; and project costs are stored in ``manager.py``. - -.. code-block:: - - /path/to/orbit/ORBIT/core/defaults/common_cost.yaml - /path/to/orbit/library/cables/*.yaml - /path/to/orbit/library/vessels/*.yaml - /path/to/orbit/ORBIT/manager.py - - -Questions regarding the methodology or organization of the common costs? Reach out to the :doc:`team` - -.. note:: - - This page is under construction and may receive an example in future releases. +files shown below. Costs for all the `DesignPhases` are stored in `common_cost.yaml`; cable costs +are stored in each cable file in the `library/cables` folder; vessel costs are stored +in each vessel file in the `library/vessels` folder; and project costs are stored in `manager.py`. + +```shell +/path/to/orbit/ORBIT/core/defaults/common_cost.yaml +/path/to/orbit/library/cables/*.yaml +/path/to/orbit/library/vessels/*.yaml +/path/to/orbit/ORBIT/manager.py +``` + +Questions regarding the methodology or organization of the common costs? Reach out to the {doc}`../team` + +:::{note} +This page is under construction and may receive an example in future releases. +::: diff --git a/docs/methods/DesignPhase.md b/docs/methods/DesignPhase.md new file mode 100644 index 00000000..11e5e8f3 --- /dev/null +++ b/docs/methods/DesignPhase.md @@ -0,0 +1,30 @@ +(design-methods)= +# Design Phases + +The following pages cover the methodology behind the design phases available in +the model. + +## Substructures + +### Fixed-Bottom + +- [Monopile](#monopile-design-methods) + +- [Scouring Protection](#scour-protection-design-methods) + +### Floating + +- [Semi-Submersible](#semi-submersible-design-methods) +- [Spar](#spar-design-methods) +- [Mooring System](#mooring-design-methods) + +## Cabling + +- [Cable Helpers](#cable-helpers-methods) +- [Array System](#array-design-methods) +- [Electrical System](#electrical-design-methods) +- [Export System](#export-design-methods) + +## Substation + +- [Offshore Substation](#oss-design-methods) diff --git a/docs/methods/InstallPhase.md b/docs/methods/InstallPhase.md new file mode 100644 index 00000000..dee53647 --- /dev/null +++ b/docs/methods/InstallPhase.md @@ -0,0 +1,29 @@ +(install-methods)= +# Install Phases + +The following pages cover the methodology behind the installation phases +available in the model. + +## Substructures + +### Fixed-Bottom + +- [Monopile](#monopile-install-methods) +- [Jacket](#jacket-install-methods) +- [Scouring Protection](#scour-protection-install-methods) + +### Floating + +- [Moored Substructure](#moored-sub-install-methods) +- [Gravity-Based Substructure](#gravity-install-methods) +- [Mooring System](#mooring-install-methods) + +## Cabling + +- [Array System](#array-install-methods) +- [Export System](#export-install-methods) + +## Substation & Turbine + +- [Turbine](#turbine-install-methods) +- [Offshore Substation](#oss-install-methods) diff --git a/docs/methods/ParametricManager.md b/docs/methods/ParametricManager.md new file mode 100644 index 00000000..42b66327 --- /dev/null +++ b/docs/methods/ParametricManager.md @@ -0,0 +1,7 @@ +(parametric-manager-methods)= +# Parametric Manager + +The following pages cover the methodology behind the parametric manager. For +more details of the code implementation, please see [`Parametric Manager API`](#parametric-manager-api). + +More info coming soon.... diff --git a/docs/methods/ProjectManager.md b/docs/methods/ProjectManager.md new file mode 100644 index 00000000..d37949e1 --- /dev/null +++ b/docs/methods/ProjectManager.md @@ -0,0 +1,154 @@ +(project-manager-methods)= +# Project Manager + +The following pages cover the methodology behind the project manager. + +## Overview + +The `ProjectManager` is the primary system for interacting with ORBIT to simulate +a wind project. Users can customize their project by specifying a a wide variety of +parameters as a dictionary (see the [`ProjectManager` tutorial](#project-manager-tutorial) for +further details). For more details of the code implementation, please see the +[`ProjectManager` API](#project-manager-api). + +It instantiates a class aggregates project parameters, specifies a start date, and interprets a weather +profile, and it employs a collection of decorators, `methods`, and `classmethods` to run the simulation. +Among these methods are `design_phases` and `install_phases` that serve as components to the simulation. +Additionally, some methods search and catch key errors to avoid simulation issues, export progress logs, +and save the outputs. + +## Run + +This method checks to see if a design or install phase is instatiated prior to running them. Depending on +which design phases are specified, each phase is run in no particular order and the results are added to +`design_results` dictionary. Conversely, the install phases can be run sequentially or as overlapped +processes (see example: {doc}`Overlapping install <../examples>`). It is worth noting, that ORBIT +has built in logic to determine any dependency between install phases. + +## Properties + +The `@property` decorators allow the `ProjectManager` to access and manipulate the attributes of certain classes. Of the +several properties some important ones are: + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + +- `capex_categories`: CapEx Categories +- `npv`: Net Present Value +- `turbine_capex`: CapEx of the Wind Turbine. +- `bos_capex`: BOS CapEx includes the System CapEx and Installation CapEx. +- `system_capex`: Total system procurement cost. +- `installation_capex`: Total installation cost. +- `project_capex`: Project Capex includes, site auction, site assessment, construction plan, and installation plan costs. +- `soft_capex_breakdown`: Soft CapEx Categories + +Finally, these attributes are collected in an `output` dictionary. + +## Class Methods + +The `@classmethod` decorator allows the `ProjectManager` to access and modify class-level attributes. + +- `register_design_phase`: Add a custom design phase to the `ProjectManager` class. +- `register_install_phase`: Add a custom install phase to the `ProjectManager` class. + +## Soft CapEx Methodology + +The methodology outlined in {cite:t}`beiter2016spatial` applies multipliers +(or assumed factors) to the magnitude of capital expenditure (CapEx) +components in order to derive the Soft CapEx components. The factors used are +consistent with those used in {cite:t}`cower2024`, enabling the soft costs to +scale in proportion to the other costs calculated within ORBIT. Soft Capex is +calculated using the default multipliers and parameters from {cite:t}`cower2024`. +Users can specify any of the :py:attr:`soft_capex_factors` below if they prefer to +override the default values. Additionally, users can assign $/kW values for +any calculated Soft CapEx component, ending with :math:`\_capex`, for +simplicity. The soft CapEx component's definitions and their calculations +are provided below. + +### Construction Insurance + +All risk property, delays in start-up, third party liability, and broker's fees. Unless otherwise +specified, a `construction_insurance_factor` of 0.0207 is applied to following calculation. See the +[{py:meth}`ProjectManager.construction_insurance_capex` documentation](#project-manager-api) for +further details. + +`construction_insurance_capex` = `construction_insurance_factor` $\times$ (`turbine_capex` + `bos_capex` + `project_capex`) + +### Commissioning + +Cost to integrate and commission the project where the `commissioning_factor` is assumed to be +0.0115 unless otherwise specified. Please see the +[{py:meth}`ProjectManager.commissioning_capex` documentation](#project-manager-api) for further +details. + +`commissioning_capex` = `commissioning_factor` $\times$ (`turbine_capex` + `bos_capex` + `project_capex`) + +### Decommissioning + +Surety bond lease to ensure that the burden for removing offshore structures +at the end of their useful life does not fall on taxpayers where the `decommissioning_factor` +is assumed to be 0.2 unless specified otherwise. Please see the +[{py:meth}`ProjectManager.decommissioning_capex` documentation](#project-manager-api) for further +details. + +`decommissioning_capex` = `decommissioning_factor` $\times$ `installation_capex` + +### Procurement Contingency + +Provision for an unforeseen event or circumstance during the procurement process where the +`procurement_contingency_factor` is assumed to be 0.0575 unless specified otherwise. Please see the +[{py:meth}`ProjectManager.procurement_contingency` documentation](#project-manager-api) for further +details. + +`procurement_contingency_capex` = `procurement_contingency_factor` $\times$ (`turbine\_capex` + `bos_capex` + `project_capex` - `installation_capex`) + +### Installation Contingency + +Provision for an unforeseen event or circumstance during the installation process where the +`installation_contingency_factor` is assumed to be 0.345 unless specified otherwise. Please see the +[{py:meth}`ProjectManager.installation_contingency` documentation](#project-manager-api) for further +details. + +`installation_contingency_capex` = `installation_contingency_factor` $\times$ `installation_capex` + +### Construction Financing + +Additional expenses incurred from interest on loans used to fund a construction +project, calculated based on the borrowing period and the project's spending schedule. + +The `spend_schedule` is based on industry data from a U.S. project with the following default +payment schedule unless specified otherwise. Please see the +[{py:meth}`ProjectManager.construction_financing_factor` documentation](#project-manager-api) for +further details. + +| Year | Amount | Cumulative | +| ----: | ------: | ----------: | +| 0 | 0.25 | 0.25 | +| 1 | 0.25 | 0.5 | +| 2 | 0.30 | 0.8 | +| 3 | 0.10 | 0.9 | +| 4 | 0.10 | 1.0 | +| 5 | 0.00 | 1.0 | + +The following default values also apply unless configured otherwise: + +- `interest_during_construction` = 0.044 +- `tax_rate` = 0.26 + +`construction_financing_factor` = + +$\sum_{k=0}^{n-1} spend\_schedule_k \times (1 + (1 - tax\_rate) \times ((1+ interest\_during\_construction)^{k+0.5} - 1)$ + +where *k* is the current year and *n* is the total number of years in `spend_schedule`. + +`construction_financing_capex` = (`construction_financing_factor` - 1) $\times$ +(`construction_insurance_capex` + `commissioning_capex` + `decommissioning_capex`+ +`procurement_contingency_capex` + `installation_contingency_capex` + `turbine_capex` + `bos_capex`) + +## References + +```{bibliography} +:style: unsrtalpha +:filter: docname in docnames +``` diff --git a/docs/methods/design/ArraySystemDesign.md b/docs/methods/design/ArraySystemDesign.md new file mode 100644 index 00000000..c27366a8 --- /dev/null +++ b/docs/methods/design/ArraySystemDesign.md @@ -0,0 +1,163 @@ +(array-design-methods)= +# Array Cabling System Design Methodology + +For details of the code implementation, please see the [Array System Design API](#array-design-api) +documentation. + +## Overview + +Below is an overview of the process used to design an array cable system in ORBIT. +For more details on the helper classes used to support this design please see +[Cabling Helper Classes](#cable-helpers-methods). + +As of the current version of the code there are three array cabling layouts +that can be configured in ORBIT: grid, ring and custom. [Fig. 1](#grid-no-partial) is an example +of a grid layout featuring 7 "full-strings" and configured distances between +turbines on a string and each row. [Fig. 2](#ring-partial) is an example of a ring layout +where the there is a predetermined distance between the first turbines on a +string and the substation. This figure is also an example of a +"partial string" that is needed to complete the layout. The next sections will +go into more detail of the key steps in building out the array cabling system. + +(grid-no-partial)= +:::{figure} ../../images/examples/full_grid_example.png +:align: left +:width: 35% + +Grid layout with no partial strings. +::: + +(ring-partial)= +:::{figure} ../../images/examples/partial_ring_example.png +:align: right +:width: 35% + +Ring layout with 1 partial string +::: + + + +## Determining the Total Number of Strings + +In order to create the minimum number of strings required to complete a +"standardized" array cable layout we must first determine how many turbines +can fit on a given set of cable types without overloading them. + +### Maximum Turbines per Cable + +The maximum number of turbines that can fit on each cable is determined by +dividing each cable type's power rating by the rated capacity of the turbine +and rounding down to the nearest integer. + +{py:attr}`Cable.max_turbines` = $\lfloor\frac{P}{turbine\_rating}\rfloor$, +where + +$P$ = {py:attr}`Cable.power`, and \\ +{py:attr}`turbine_rating` = rated capacity of turbine + +### Calculating a Complete String + +The number of turbines that can fit on a string is determined by the user +configured cable types of the system. Starting from the smallest capacity cable +available, turbines are added to the string until that cable's maximum power +capacity is reached. This process is repeated for each of the next largest +capacity cables until all cable types have reached their maximum capacity. The +number of cable sections added to the string in this process represents the +maximum number of cables that can be added to each string. + +```{code-block} py +:name: string-computation + +# Assume that we are using the Fig 1. example so there can only be 6 +# turbines contained in a single string of cables +max_turbines_per_string = 6 + +# Keeping with the Fig 1. example, assume cable1 is a Cable object that +# represents the "XLPE_400mm_36kV" cable from and cable2 represents the +# "XLPE_630mm_36kV" cable. Note that this is sorted from smallest to largest. +cable_list = [cable1, cable2] + +# Start with an empty string +cable_layout = [] +n = len(cable_layout) + +# Loop through the cables as long as we haven't reached the string maximum +# and there are cables in cable_list +while n < max_turbines_per_string and cables: + cable = cable_list.pop(0) # remove the first cable in the list + + # Ensure that the most turbines in a string is is lower than the + # string maximum and the maximum the individual cable can support, + # then add another cable. + while max_turbines_per_string > n < cable.max_turbines: + cable_layout.append(cable.name) + n = len(cable_layout) +``` + +After the above calculation is performed, {py:func}`cable_layout` will contain +a list of cable sections starting from the offshore substation and ending at +the last turbine on a string and will look like the following: + +{py:attr}`full_string` = `["XLPE_630mm_36kV", "XLPE_630mm_36kV", "XLPE_400mm_36kV",` +`"XLPE_400mm_36kV", "XLPE_400mm_36kV", "XLPE_400mm_36kV"]` + +In [Fig. 1](#grid-no-partial), there are 7 of full strings. In the [Fig. 2](#ring-partial) there are 7 full +strings and 1 partial string: + +{py:attr}`partial_string` = `["XLPE_400mm_36kV", "XLPE_400mm_36kV", "XLPE_400mm_36kV"]` + +### Number of Full and Partial Strings + +The number of full strings is calculated using the equation below, + +{py:attr}`num_full_strings` = $\lfloor \frac{Plant.num\_turbines}{num\_turbines\_full\_string} \rfloor$ + +and the number of partial strings (containing any remaining turbines) is +calculated with the following equation. + +{py:attr}`num_partial_strings` = $Plant.num\_turbines \ \% \ num\_turbines\_full\_string$ + +## Layouts + +### Ring + +For a ring layout, the {py:attr}`substation_distance` is used as the radius of +the first row of turbines, spaced evenly around the ring. Subsequent turbines +on a string are spaced using the {py:attr}`turbine_distance` attribute. An +example of this layout can be seen above in [Fig. 2](#ring-partial). + +### Grid + +For the grid layout, an evenly spaced grid of (x, y) coordinates for each +turbine is calculated based off the {py:attr}`turbine_distance`, +{py:attr}`row_distance`, and {py:attr}`substation_distance` with the offshore +substation being located at (0, ({py:attr}`num_strings` - 1) * {py:attr}`row_distance` / {py:attr}`num_strings`) + +### Custom + +Coming soon! + +## Section Lengths + +The distance between a turbine and it's subsequent connection determines the +cable length that is required for the array system. These lengths are summed up +and stored in the {py:attr}`design_result`, which can be utilized by the +[array cable installation module](#array-install-methods). + +## Process Diagrams + +![Array cable design process diagram](../../images/process_diagrams/ArraySystemDesign.png) diff --git a/docs/methods/design/CableHelpers.md b/docs/methods/design/CableHelpers.md new file mode 100644 index 00000000..49370094 --- /dev/null +++ b/docs/methods/design/CableHelpers.md @@ -0,0 +1,93 @@ +(cable-helpers-methods)= +# Cabling Design Helpers + +For details of the code implementation, please see the +[Cabling Helpers API documentation](#cable-helpers-api). + +## Overview + +This overview provides the {class}`Cable` class, {class}`Plant` class, and +{class}`CableSystem` parent class. + +## Cable + +The cable class calculates a provided cable's power rating for determining the +maximum number of turbines that can be supported by a string of cable. + +### Character Impedance ($\Omega$) + +$$ +Z_0 = \sqrt{\frac{R + 2 \pi f L}{G + j 2 \pi f C}} +$$ + +$R=$ {py:attr}`ac_resistance` \ +$j=$ the imaginary unit \ +$f=$ {py:attr}`line_frequency` \ +$L=$ {py:attr}`inductance` \ +$G=$ \frac{1}{R} =${py:attr}`conductance` \ +$C=$ {py:attr}`capacitance` + +### Power Factor + +$$ +|P| &= \cos(\theta) \\ + &= \cos(\arctan(\frac{j Z_0}{Z_0})) +$$ + +$\theta=$ the phase angle \ +$jZ_0=$ the imaginary portion of {py:attr}`character_impedance` \ +$Z_0=$ the real portion of {py:attr}`character_impedance` + +### Cable Power ($MW$) + +$P = \sqrt{3} * V * I * |P|$ \ +$V=$ {py:attr}`rated_voltage` \ +$I=$ {py:attr}`current_capacity` \ +$|P|=$ {py:attr}`power_factor` + +## Plant + +Calculates the wind farm specifications to be used for +[array cable design phase](#array-design-methods). The "data class" +accepts either set distances between turbines and rows or calculates them +based off of the number of rotor diameters specified, for example: + +```python +# First see if there is a distance defined +self.turbine_distance = config["plant"].get("turbine_distance", None) + +# If not, then multiply the rotor diameter by the turbine spacing, +# an integer representation of the number of rotor diameters and covert +# to kilometers +if self.turbine_distance is None: + self.turbine_distance = ( + rotor_diameter * config["plant"]["turbine_spacing"] / 1000.0 + ) + +# Repeat the same process for row distance. +self.row_distance = config["plant"].get("row_distance", None) + if self.row_distance is None: + self.row_distance = ( + rotor_diameter * config["plant"]["row_spacing"] / 1000.0 + ) +``` + +where {py:attr}`config` is the configuration dictionary passed to the +[array cable design phase](#array-design-api) + +The cable section length for the first turbine in each string is calculated as +the distance to the substation, `substation_distance`. + +## CableSystem + +{py:class}`CableSystem` acts as the parent class for both +{py:class}`ArrayDesignSystem` and {py:class}`ExportDesignSystem`. As such, it +is not intended to be invoked on its own, however it provides the shared +frameworks for both cabling system. + +In particular, the {py:class}`CableSystem` offers the cabling initialization and most of +the output properties such as {py:attr}`cable_lengths_by_type`, +{py:attr}`total_cable_lengths_by_type`, {py:attr}`cost_by_type`, +{py:attr}`total_phase_cost`, {py:attr}`total_phase_time`, +{py:attr}`detailed_output`, and most importantly {py:attr}`design_result` to avoid +redefinition of multiple core calculations. diff --git a/docs/source/phases/design/doc_ElectricalDesign.rst b/docs/methods/design/ElectricalDesign.md similarity index 56% rename from docs/source/phases/design/doc_ElectricalDesign.rst rename to docs/methods/design/ElectricalDesign.md index fcc040aa..698a265b 100644 --- a/docs/source/phases/design/doc_ElectricalDesign.rst +++ b/docs/methods/design/ElectricalDesign.md @@ -1,135 +1,134 @@ -Electrical System Design Methodology -==================================== +(electrical-design-methods)= +# Electrical System Design Methodology For details of the code implementation, please see -:doc:`Electrical System Design API `. +[Electrical System Design API documentation](#electrical-design-api). -Overview --------- +## Overview Below is an overview of the process used to design an export cable system and -offshore substation in ORBIT using the ElectricalDesign module. This module is to be -used in place of both the ExportSystemDesign module and the OffshoreSubstationDesign +offshore substation in ORBIT using the [`ElectricalDesign`](#electrical-design-api) module. +This module is to be used in place of both the [`ExportSystemDesign`](#export-design-methods) +and the [`OffshoreSubstationDesign`](#oss-design-methods) module as it codesigns the export cables and offshore substation. Depending on whether HVAC or HVDC cables are selected, different components will contribute to the final BOS. -For more detail on the helper classes used to support this design please see :doc:`Cabling Helper Classes -`, specifically :class:`Cable` and :class:`CableSystem`. +For more detail on the helper classes used to support this design please see +[Cabling Helper Classes](#cable-helpers-methods), specifically {class}`Cable` and {class}`CableSystem`. +## Number of Required Cables -Number of Required Cables ---------- The number of export cables required for HVAC is calculated by dividing the windfarm's capacity by the configured export cable's power rating and adding any user defined redundnacy as seen below. -:math:`num\_cables = \lceil\frac{plant\_capacity}{cable\_power}\rceil + num\_redundant` +$num\_cables = \lceil\frac{plant\_capacity}{cable\_power}\rceil + num\_redundant$ For HVDC cables (both monopole and bipole), the number of cables is twice the number as calculated abpve because HVDC systems require a pair of cables per implementation. The equation for this calculation is shown below. -:math:`num\_cables = 2 * \lceil\frac{plant\_capacity}{cable\_power}\rceil + num\_redundant` +$num\_cables = 2 * \lceil\frac{plant\_capacity}{cable\_power}\rceil + num\_redundant$ + +## Export Cable Length -Export Cable Length ---------- The total length of the export cables is calculated as the sum of the site depth, distance to landfall and distance to interconnection multiplied by the -user defined :py:attr`percent_added_length` to account for any exclusions or +user defined `percent_added_length` to account for any exclusions or geotechnical design considerations that make a straight line cable route impractical. -:math:`length = (d + distance_\text{landfall} + distance_\text{interconnection}) * (1 + length_\text{percent_added})` +$length = (d + distance_\text{landfall} + distance_\text{interconnection}) * (1 + length_\text{percent_added})$ + +## Cable Crossing Cost -Cable Crossing Cost ---------- Optional inputs for both number of cable crossings and unit cost per cable -crossing. The default number of cable crossings is 0 and cost per cable -crossing is $500,000. This cost includes materials, installation, etc. Crossing +crossing. The default number of cable crossings is 0 and cost per cable +crossing is \$500,000. This cost includes materials, installation, etc. Crossing cost is calculated as product of number of crossings and unit cost. -Number of Required Power Transformer, Tranformer Rating, and Cost ---------- +## Number of Required Power Transformer, Tranformer Rating, and Cost + The number of main power transformers (MPT) required is assumed to be equal to the number of required export cables. The transformer rating is calculated by dividing the windfarm's capacity by the number of MPTs. MPTs are only required if the -export cables are HVAC. The default cost of the MPT is $2.87m per HVAC cable. Therefore, the total MPT cost is +export cables are HVAC. The default cost of the MPT is \$2.87m per HVAC cable. Therefore, the total MPT cost is proportional to the number of cables. Note: Previous versions may have used curve-fits to -calculate total MPT cost based on the windfarm's capacity. The MPT unit cost ($/cable) can -be ovewritten by the user by setting (``mpt_unit_cost``) to the desired cost. If the export cables -are HVDC, then the cost of power transformers will be $0. +calculate total MPT cost based on the windfarm's capacity. The MPT unit cost (\$/cable) can +be ovewritten by the user by setting (`mpt_unit_cost`) to the desired cost. If the export cables +are HVDC, then the cost of power transformers will be \$0. + +## Number of Shunt Reactors, Reactive Power Compensation, and Cost -Number of Shunt Reactors, Reactive Power Compensation, and Cost ---------- The shunt reactor cost is dependent on the amount of reactive power compensation required based on the distance of the substation to shore. This model assumes one shunt reactor for each HVAC export cable. An HVDC export systems do not require -reactive power compensation. The default cost rate of the shunt reactors is $10k per HVAC cable. The total cost is proportional +reactive power compensation. The default cost rate of the shunt reactors is \$10k per HVAC cable. The total cost is proportional to the number of cables multipled by a cable-specific compensation factor. The default cost rate -can be overwritten by the user by setting (``shunt_unit_cost``) to the desired cost. The shunt -reactor cost is $0 for HVDC systems. +can be overwritten by the user by setting (`shunt_unit_cost`) to the desired cost. The shunt +reactor cost is \$0 for HVDC systems. + +## Number of Required Switchgears and Cost -Number of Required Switchgears and Cost ---------- The number of switchgear relays required is assumed to be equal to the number of required export cables. Switchgear cost is only necessary if HVAC export cables -are chosen. The default cost is $4m per cable for HVAC. The default cost can be overwritten by the user by -setting (``switchgear_cost``) to the desired cost. Switchgear cost is equal to $0 for HVDC export +are chosen. The default cost is \$4m per cable for HVAC. The default cost can be overwritten by the user by +setting (`switchgear_cost`) to the desired cost. Switchgear cost is equal to \$0 for HVDC export cables. -Number of Circuit Breakers and Cost ---------- +## Number of Circuit Breakers and Cost + The number of circuit breakers required is assumed to be equal to the number of required export cables. Breakers are only necssary if HVDC export cables are chosen. The default cost is -$10.6m per HVDC cable. The default cost can be overwritten by the user by setting (``dc_breaker_cost``) -to the desired cost. Breaker cost is $0 for HVAC cables. +\$10.6m per HVDC cable. The default cost can be overwritten by the user by setting (`dc_breaker_cost`) +to the desired cost. Breaker cost is \$0 for HVAC cables. + +## Number of Required ACDC Converters and Cost -Number of Required AC\DC Converters and Cost ---------- -AC\DC converters are only required for HVDC export cables. The number of converters +ACDC converters are only required for HVDC export cables. The number of converters is assumed to be equal to the number of HVDC export cables. -Ancillary System Cost ---------- +## Ancillary System Cost + Costs are included such as a backup generator, workspace cost, and miscellous to capture any additional features outside the main components. The user can define each -variable by setting (``backup_gen_cost``), (``workspace_cost``), and (``other_ancillary_cost``). +variable by setting (`backup_gen_cost`), (`workspace_cost`), and (`other_ancillary_cost`). + +## Assembly Cost (On Land) -Assembly Cost (On Land) ----------- The majority of the electrical components are located on the offshore substation platform, but they must be assembled on land. Therefore, an assembly factor of 7.5% is added to the components cost. Those components include switchgear, shut reactors, and ancillary costs. The user can change the -factor by setting (``topside_assembly_factor``) to the desired percentage. +factor by setting (`topside_assembly_factor`) to the desired percentage. + +## Substation Topside Mass and Cost -Substation Topside Mass and Cost ----------- We assume that the topside design cost is a fixed amount based on the export cables (either HVDC or HVAC). -The user can specify the topside cost by setting (``topside_design_cost``). The mass of the topside is +The user can specify the topside cost by setting (`topside_design_cost`). The mass of the topside is determined by a curve fit. -Substation Substructure Mass and Cost ----------- +## Substation Substructure Mass and Cost + The mass and cost associated with the substructure of the offshore substation are based on curve fits. The topside mass will drive the mass/size of the substructure. Then, the cost of the -substructure is determined by its mass. The substructure has a default cost rate of $3000 per ton of -steel. The value can be overwritten by setting (``oss_substructure_cost_rate``) to the desired cost rate. +substructure is determined by its mass. The substructure has a default cost rate of \$3000 per ton of +steel. The value can be overwritten by setting (`oss_substructure_cost_rate`) to the desired cost rate. + +## Onshore Cost -Onshore Cost ---------- The onshore cost is considered to be the minimum cost of interconnection. This includes the major required hardware for a cable connection onshore. For HVDC cables, it includes the converter cost, DC breaker cost, and transformer cost. For HVAC, it includes the transformer cost and switchgear cost. The onshore costs may or may not be included in the BOS -of the wind farm. Therefore, this cost is not included in the total ``system_capex`` +of the wind farm. Therefore, this cost is not included in the total `system_capex` calculated by ProjectManager. -Design Result ---------- -The result of this design module (:py:attr:`design_result`) includes the +## Design Result + +The result of this design module ({py:attr}`design_result`) includes the specifications for both the export cables and offshore substation. This includes a list of cable sections and their lengths and masses that represent the export cable system, as well as the offshore substation substructure and topside mass and cost, and number of substations. This result can then be passed to the -:doc:`export cable installation module <../install/export/doc_ExportCableInstall>` and -:doc:`offshore substation installation module <../install/oss/doc_OffshoreSubstationInstall>` +[export cable installation module](#export-install-methods) and +[offshore substation installation module](#oss-install-methods) to simulate the installation of the export system. diff --git a/docs/methods/design/ExportSystemDesign.md b/docs/methods/design/ExportSystemDesign.md new file mode 100644 index 00000000..a0f2cf1a --- /dev/null +++ b/docs/methods/design/ExportSystemDesign.md @@ -0,0 +1,42 @@ +(export-design-methods)= +# Export System Design Methodology + +For details of the code implementation, please see +[Export System Design API documentation](#export-design-api). + +## Overview + +Below is an overview of the process used to design an export cable system in +ORBIT. For more detail on the helper classes used to support this design please +see the [Cabling Helper Classes documentation](#cable-helpers-methods), specifically +{class}`Cable` and {class}`CableSystem`. + +## Number of Required Cables + +The number of export cables required is calculated by dividing the windfarm's +capacity by the configured export cable's power rating and adding any user +defined redundnacy as seen below. + +$num\_cables = \lceil\frac{plant\_capacity}{cable\_power}\rceil + num\_redundant$ + +## Export Cable length + +The total length of the export cables is calculated as the sum of the site +depth, distance to landfall and distance to interconnection multiplied by the +user defined :py:attr\`percent_added_length\` to account for any exclusions or +geotechnical design considerations that make a straight line cable route +impractical. + +$length = (d + distance_\text{landfall} + distance_\text{interconnection} * (1 + length_\text{percent_added})$ + +## Design Result + +The result of this design module ({py:attr}`design_result`) is a list of cable +sections and their lengths and masses that represent the export cable system. +This result can then be passed to the +[export cable installation module](#export-install-methods) +to simulate the installation of the system. + +## Process Diagrams + +![Export cable design process diagram](../../images/process_diagrams/ExportSystemDesign.png) diff --git a/docs/methods/design/MonopileDesign.md b/docs/methods/design/MonopileDesign.md new file mode 100644 index 00000000..345ac006 --- /dev/null +++ b/docs/methods/design/MonopileDesign.md @@ -0,0 +1,43 @@ +(monopile-design-methods)= +# Monopile Design Methodology + +For details of the code implementation, please see the +[Monopile Design API documentation](#monopile-design-api). + +## Overview + +This module is based on initial pile dimension calculations from {cite:t}`arany2017design`. +Pile dimensions are chosen to withstand the bending moment from +the 50-year Extreme Operation Gust (EOG). This corresponds to wind scenario +U-3 in Section 2.2.1. This module is not intended to capture the complexities +of a full engineering design study for monopiles, but rather broadly capture +the scaling trends due to increased site depth, turbine size and material +parameters. + +The 50-year extreme wind speed can be calculated using the following cumulative +density function. + +$U_{10,50-year}=K(-\ln(1-0.98^\frac{1}{52596}))^\frac{1}{S}$ + +where $K$ and $S$ are the Weibull scale and shape factors +respectively. + +The mudline bending moment is calculated as: + +$M_{wind,EOG} = \gamma_LF_{wind,EOG}(S + z_{hub})$ + +where $\gamma_L$ is the load factor (defaults to 1.35), +$F_{wind,EOG}$ is the total wind load on the turbine, $S$ is the +water depth at site and $z_{hub}$ is the hub height of the turbine. The +derivation of $F_{wind,EOG}$ can be seen in detail in the +[ORBIT technical documentation](https://www.nlr.gov/docs/fy20osti/77081.pdf). + +Initial pile dimensions are then calculated using {cite:t}`arany2017design`, +{cite:t}`api2000`, and {cite:t}`poulos1980pile`. + +## References + +```{bibliography} +:style: unsrtalpha +:filter: docname in docnames +``` diff --git a/docs/methods/design/MooringSystemDesign.md b/docs/methods/design/MooringSystemDesign.md new file mode 100644 index 00000000..2366f0f0 --- /dev/null +++ b/docs/methods/design/MooringSystemDesign.md @@ -0,0 +1,18 @@ +(mooring-design-methods)= +# Mooring System Design Methodology + +For details of the code implementation, please see the +[Mooring System Design API documentation](#mooring-design-api). + +## Overview + +The mooring system design module in ORBIT is based on previous modeling +efforts undertaken by NLR, {cite:t}`maness2017BOS`. The technical documentation for +this tool can be found [here](https://www.nlr.gov/docs/fy17osti/66874.pdf)`. + +## References + +```{bibliography} +:style: unsrtalpha +:filter: docname in docnames +``` diff --git a/docs/methods/design/OffshoreSubstationDesign.md b/docs/methods/design/OffshoreSubstationDesign.md new file mode 100644 index 00000000..28d481b8 --- /dev/null +++ b/docs/methods/design/OffshoreSubstationDesign.md @@ -0,0 +1,19 @@ +(oss-design-methods)= +# Offshore Substation Design Methodology + +For details of the code implementation, please see the +[Offshore Substation Design API documentation](#oss-design-api). + +## Overview + +The offshore substation design module in ORBIT is based on previous modeling +efforts undertaken by NLR, {cite:t}`maness2017BOS`. A detailed description of the +model and its functionality can be found in the ORBIT technical +documentation (Coming soon!). + +## References + +```{bibliography} +:style: unsrtalpha +:filter: docname in docnames +``` diff --git a/docs/methods/design/ScourProtectionDesign.md b/docs/methods/design/ScourProtectionDesign.md new file mode 100644 index 00000000..f02ca98b --- /dev/null +++ b/docs/methods/design/ScourProtectionDesign.md @@ -0,0 +1,72 @@ +(scour-protection-design-methods)= +# Scour Protection Design Methodology + +For details of the code implementation, please see the +[Scour Protection Design API documentation](#scour-protection-design-api). + +## Scour Protection Design + +This module calculates the required scour protection material for a fixed +substructure to avoid seabed erosion around the installation. It is based on +a [DNV GL standard](https://rules.dnvgl.com/docs/pdf/DNV/codes/docs/2014-05/Os-J101.pdf) +{cite:p}`dnv2014osw` and geometric calculations {cite:p}`boem2018vineyard`. + +The potential depth of the a free forming scour pit is calculated using a +simplified version of the relationship presented in the DNV GL report (equation +J.5): + +$\frac{S}{D} = 1.3$ + +where $S$ is the calculated depth of the scour pit and $D$ is the +overall diameter of the substructure. The default value (1.3) is a conservative +assumption and may be overridden by the user as follows: + +```python +config = { + ... + + "scour_protection_design": { + "scour_depth_equilibrium": 1.2 + } + + ... +} +``` + +The radius of the scour pit is then calculated using the soil friction +angle ($\phi$) and a simple geometric relationship: + +$r = \frac{D}{2} + \frac{S}{tan(\phi)}$ + +The default value for $\phi$ is 33.5deg, representing the soil +friction angle for medium density sand. The total volume of scour +protection material is then calculated as follows, + +$V = \pi * t * r^2$ + +where $t$ represents the depth of the scour protection material. This +value defaults to 1m in the code, which represents an appropriate initial +assumption and not a complete design. For sites that exhibit greater seafloor +currents, the scour protection layer may be as thick as 2m, whereas calmer +sites may only need 0.3-0.5m of material. In the abscense of a geotechnical +study, this value is difficult to calculate and is instead presented to user as +a configurable input so the cost impacts of different thicknesses can be +investigated. + +Terms: +: - $S =$ Scour depth + - $D =$ Monopile diameter + - $r =$ Radius of scour protection from the center of the monopile + - $\phi =$ Soil friction angle + +Default Assumptions: +: - $\frac{S}{D} = 1.3$ + - $\phi = 33.5$ + \* Angle for medium density sand + +## References + +```{bibliography} +:style: unsrtalpha +:filter: docname in docnames +``` diff --git a/docs/methods/design/SemiSubmersibleDesign.md b/docs/methods/design/SemiSubmersibleDesign.md new file mode 100644 index 00000000..87e0da9c --- /dev/null +++ b/docs/methods/design/SemiSubmersibleDesign.md @@ -0,0 +1,18 @@ +(semi-submersible-design-methods)= +# Semi-Submersible Design Methodology + +For details of the code implementation, please see the +[Semi-Submersible Design API documentation](#semi-submersible-design-api). + +## Overview + +The semi-submersible design module in ORBIT is based on previous modeling +efforts undertaken by NLR, {cite:t}`maness2017BOS`. The technical documentation for +this tool can be found [here](https://www.nlr.gov/docs/fy17osti/66874.pdf). + +## References + +```{bibliography} +:style: unsrtalpha +:filter: docname in docnames +``` diff --git a/docs/methods/design/SparDesign.md b/docs/methods/design/SparDesign.md new file mode 100644 index 00000000..e62ecdda --- /dev/null +++ b/docs/methods/design/SparDesign.md @@ -0,0 +1,17 @@ +(spar-design-methods)= +# Spar Design Methodology + +For details of the code implementation, please see the +[Spar Design API documentation](#spar-design-api). + +## Overview + +The spar design module in ORBIT is based on previous modeling efforts +undertaken by NLR, {cite:t}`maness2017BOS`. + +## References + +```{bibliography} +:style: unsrtalpha +:filter: docname in docnames +``` diff --git a/docs/methods/install/ArrayCableInstall.md b/docs/methods/install/ArrayCableInstall.md new file mode 100644 index 00000000..2cbbd9b6 --- /dev/null +++ b/docs/methods/install/ArrayCableInstall.md @@ -0,0 +1,122 @@ +(array-install-methods)= +# Array Cabling System Installation Methodology + +For details of the code implementation, please see the +[Array Cable Installation API documentation](#array-install-api). + +## Overview + +The `ArrayCableInstallation` module simulates the installation of array cable +sections between turbines and the offshore substation at site. This process is +one of the critical installation phases in the construction of a wind farm as +testing and final commissioning of the turbines can't occur until it is +complete. + +The installation of cables offshore is a complex process that depends on the +geotechnical parameters of the seabed along the cable route. Detailed +geotechnical data is typically not publicly available for all potential +offshore wind sites in the U.S., and as such, ORBIT was designed to allow a +user to investigate the impact of seabed conditions indirectly without +requiring detailed descriptions of the seabed. This is primarily done by +adjusting the cable burial speed, with harder or rockier soils requiring a +slower (and ultimately more expensive) burial speed. + +## Input Structure + +The design of the input data structure for this module allows the user to +define site specific array cable configurations. For each cable type, a list +of cable sections can be defined. The installation vessel will install each +section individually and the time to complete this operation is dynamic based +on the length and the linear density of the cable, site depth, etc. A user can +also define multiple cable types that comprise an individual string of +turbines. + +For example, + +```python +{ + 'array_system': { + 'cables': {'XLPE_400mm_33kV': { + 'cable_sections': [ + (1.7958701547, 2), # There are two 1.79km sections, + (1.118, 16), # 16 1.118km sections + (1.2128290583, 2) # and two 1.213km sections + ], + 'linear_density': 35 + } + } +} +``` + +The installation of each section above will be modeled seperately. In the above +example, only one cable was used, though there could be additional defined +cables (with their own `cable_sections` key). + +:::{note} +The above data structure can be input directly by the user, or can be a +result of running the `ArraySystemDesign` module. +::: + +(cable-strategies)= + +## Configuration + +ORBIT considers two installation strategies: a simultaneous lay/bury operation +using modern cable installation vessels and a seperated operation where one +vessel lays the cable and another follows behind to bury it. A detailed +description of the applicability of each strategy is covered in the +[ORBIT technical report](https://www.nlr.gov/docs/fy20osti/77081.pdf). + +If the config contains the key `'array_cable_bury_vessel'` the separate +strategy will be used. If this key is not present, the +`'array_cable_install_vessel'` will perform a simultaneous lay/bury of the +cable. + +The key `'array_cable_trench_vessel'` is an optional configuration that will +simulate a separate trenching operation along all cable routes completed by +the above vessel. If this key is not present, this operation will not be +modeled. + +```python +{ + 'array_cable_install_vessel': 'example_vessel' + # 'array_cable_bury_vessel': 'example_vessel', + # 'array_cable_trench_vessel': 'example_vessel', + 'array_system': { + 'cables': {'XLPE_400mm_33kV': { + ... + } + } +} +``` + +## Processes + +The speed at which a vessel can perform the operations of each installation +strategy is determined by the vessel properties, passed kwargs or the +default speed for the process. This is the primary mechanism that a user can +adjust cable installation times for different seabed conditions. + +| Strategy | Key | Default | +| -------------- | ---------------------- | --------- | +| Lay/Bury Cable | `cable_lay_bury_speed` | 0.3 km/hr | +| Lay Cable | `cable_lay_speed` | 1 km/hr | +| Bury Cable | `cable_bury_speed` | 0.5 km/hr | + +Other operations in the installation process are determined by default values: + +| Process | Key | Default | +| --------------- | ------------------------ | ------- | +| Position Onsite | `site_position_time` | 2h | +| Prepare Cable | `cable_prep_time` | 1h | +| Lower Cable | `cable_lower_time` | 1h | +| Pull in Cable | `cable_pull_in_time` | 5.5h | +| Test Cable | `cable_termination_time` | 5.5h | + +### Configuration Examples + +Coming soon! + +## Process Diagrams + +![Array cable installation process diagram](../../images/process_diagrams/ArrayCableInstall.png) diff --git a/docs/methods/install/ExportCableInstall.md b/docs/methods/install/ExportCableInstall.md new file mode 100644 index 00000000..b0a55321 --- /dev/null +++ b/docs/methods/install/ExportCableInstall.md @@ -0,0 +1,87 @@ +(export-install-methods)= +# Export Cabling System Installation Methodology + +For details of the code implementation, please see the +[Export Cable Installation API documentation](#export-install-api) + +## Overview + +The `ExportCableInstallation` module simulates the installation of the cables +between the offshore substation and the grid connection. Many processes in this +installation phase are similar to those in the array cable installation module, +however the cable distances and process times are often much greater. + +## Input Structure + +The design of the input data structure for this module is the same as the array +cable installation module. An example export system can be seen in the code +block below. + +```python +{ + 'export_system': { + 'cables': {'XLPE_500mm_33kV': { + 'cable_sections': [ + (35, 2), # There are two 35km export cables to install + ], + 'linear_density': 35 + } + } +} +``` + +:::{note} +The above data structure can be input directly by the user, or can be a +result of running the `ExportSystemDesign` module. +::: + +## Configuration + +ORBIT considers the same possible installation strategies for array and export +cable systems. Please see the [array cable installation configuration](#cable-strategies) +for the different installation methods available. + +## Processes + +### Onshore Processes + +At landfall, the cable must be pulled up onto shore, tested and terminated at +the grid connection point and buried. These processes utilize the following +inputs. + +| Process | Key | Default | +| ------------- | ------------------------ | --------- | +| Dig Trench | `trench_dig_speed` | 0.1 km/hr | +| Tow Plow | `tow_plow_speed` | 5 km/hr | +| Pull in Winch | `pull_winch_speed` | 5 km/hr | +| Pull in Cable | `cable_pull_in_time` | 5.5h | +| Test Cable | `cable_termination_time` | 5.5h | + +A detailed description of these processes is provided in the ORBIT technical +documentation. + +### Offshore Processes + +The offshore installation process utilize the same configurable speeds as the +array system installation: + +| Strategy | Key | Default | +| -------------- | ---------------------- | --------- | +| Lay/Bury Cable | `cable_lay_bury_speed` | 0.3 km/hr | +| Lay Cable | `cable_lay_speed` | 1 km/hr | +| Bury Cable | `cable_bury_speed` | 0.5 km/hr | + +:::{note} +The cable lengths for an export system are typically much longer than the +array system and thhere is the possibility that a cable splice will be +needed. The time for splicing a cable can be configured by the user using +the `cable_splice_time` key, which defaults to 48h. +::: + +### Configuration Examples + +Coming soon! + +## Process Diagrams + +![Export cable installation process diagram](../../images/process_diagrams/ExportCableInstall.png) diff --git a/docs/methods/install/GravityBasedInstallation.md b/docs/methods/install/GravityBasedInstallation.md new file mode 100644 index 00000000..84954aaa --- /dev/null +++ b/docs/methods/install/GravityBasedInstallation.md @@ -0,0 +1,9 @@ +(gravity-install-methods)= +# Gravity-Based Foundation Installation Methodology + +For details of the code implementation, please see the +[Gravity Based Substructure Installation API documentation](#gravity-install-api) + +## Overview + +This module will be expanded in a future release. diff --git a/docs/methods/install/JacketInstall.md b/docs/methods/install/JacketInstall.md new file mode 100644 index 00000000..d5c18602 --- /dev/null +++ b/docs/methods/install/JacketInstall.md @@ -0,0 +1,9 @@ +(jacket-install-methods)= +# Jacket Installation Methodology + +For details of the code implementation, please see the +[Jacket Installation API documentation](#jacket-install-api) + +## Overview + +Coming soon! diff --git a/docs/methods/install/MonopileInstall.md b/docs/methods/install/MonopileInstall.md new file mode 100644 index 00000000..8bbca41e --- /dev/null +++ b/docs/methods/install/MonopileInstall.md @@ -0,0 +1,142 @@ +(monopile-install-methods)= +# Monopile Installation Methodology + +For details of the code implementation, please see the +[Monopile Installation API documentation](#monopile-install-api) + +## Overview + +The `MonopileInstallation` module simulates the installation of monopile +substructures at site. Monopile substructures encompass the monopile itself, a +large steel cylindrical pile that is driven into the seabed, and a steel +transition piece that is placed on top of the pile to provide a level +attachment point for the turbine. The module can be configured such that a +single wind turbine installation vessel (WTIV) transports and installs all of +the substructure components or it can be configured to include feeder barges +that transport the components to site. Process diagrams detailing the vessel +logistics for these two installation strategies can be seen below. + +:::{note} +For both installation strategies the WTIV performs all of the on site +operations with its onboard crane, either picking components from its own +deck or a neighboring feeder barge. +::: + +## Configuration + +To configure `MonopileInstallation` to utilize feeder barges for transit of +the monopile components from port, add the following configuration to the +project configuration. + +```python +config = { + ... + + "feeder": "example_feeder", # name of vessel configuration file without extension + "num_feeders": 2, + + ... +} +``` + +## Processes + +### Port Operations + +Vessels load and fasten items on their deck at port using the port crane. +Ports are configured with one crane by default, which limits multiple vessels +from accessing port resources at a time. This can be overridden by configuring +a port with additional cranes in a project configuration: + +```python +"port": { + "num_cranes": 2 # Two vessels can access port resource simultaneously. +} +``` + +The default times for fastening each component to deck are listed below. + +| Component | Inputs | Default | +| ---------------- | ------------------ | ------- | +| Monopile | `mono_fasten_time` | 12h | +| Transition Piece | `tp_fasten_time` | 8h | + +Currently, all vessels are only able to load multiples of complete sets of +components (monopile and transition piece). + +### Site Preperation + +Once the WTIV and a set of components are at site (either stored on the WTIV or +a feeder barge), the WTIV initiates site preperation. The WTIV positions itself +onsite and jacks up. If installing a monopile, the WTIV surveys the seabed with +an ROV. The following table outlines the inputs and default times for these +tasks. + +| Action | Inputs | Default | +| --------------- | -------------------------------------------------------- | ---------- | +| Position Onsite | `site_position_time` | 2h | +| Jack-up | `depth, extension``speed_above_depth``speed_below_depth` | calculated | +| ROV Survey | `rov_survey_time` | 1h | + +### Monopile Installation + +After site preperation is complete, the WTIV then releases the monopile from +the fastenings (either on its own deck or neighboring feeder barge). When the +substructure is released, it is upended using the WTIV crane and lowered to the +seabed. The crane is then equiped with the driving equipment, and the monopile +is driven into the seabed. ORBIT currently only supports simple drive +logistics, but a drive-drill-drive installation strategy is planned for future +version. Inputs and process times are summarized in the following table. + +| Action | Inputs | Default | +| ---------------- | ------------------------------------------ | ---------- | +| Release Monopile | `mono_release_time` | 3 | +| Upend Monopile | `monopile.length``crane_rate``wave_height` | calculated | +| Lower Monopile | `site_depth``crane_rate` | calculated | +| Reequip Crane | `crane_reequip_time` | 1h | +| Drive Monopile | `mono_drive_rate``mono_embed_len` | calculated | + +### Transition Piece Installation + +After the monopile is installed, the WTIV lowers and attaches the +transition piece onto the top of the monopile using the following processes: + +| Action | Inputs | Default | +| ------------- | ---------------------------------- | ---------- | +| Reequip Crane | `crane_reequip_time` | 1h | +| Lower TP | `air_gap``crane_rate``wave_height` | calculated | + +The transition piece can be attached with either a bolted or a grouted +connection. The bolted connection is selected by default. To configure the WTIV +to use a grouted connection, pass `tp_connection_type="grouted"` into the +installation module. + +For bolted connections, the WTIV performs these tasks: + +| Action | Inputs | Default | +| --------- | -------------------------------------------------------- | ---------- | +| Bolt TP | `tp_bolt_time` | 4h | +| Jack-down | `depth, extension``speed_above_depth``speed_below_depth` | calculated | + +For grouted connections, the WTIV performs these tasks: + +| Action | Inputs | Default | +| ---------- | -------------------------------------------------------- | ---------- | +| Pump Grout | `grout_pump_time` | 2h | +| Cure Grout | `grout_cure_time` | 24h | +| Jack-down | `depth, extension``speed_above_depth``speed_below_depth` | calculated | + +## Process Diagrams + +### Single WTIV Installation + +![Single WTIV monopile installation process diagrame](../../images/process_diagrams/monopile_single_wtiv.png) + +(monopile-install-feeders-process)= +### WTIV with Feeder Barges Installation + +![WTIV with feeders monopile installation process diagrame](../../images/process_diagrams/monopile_wtiv_with_feeders.png) + +### Component Installation + +![Monopile component installation process diagrame](../../images/process_diagrams/monopile_install.png) diff --git a/docs/methods/install/MooredSubInstallation.md b/docs/methods/install/MooredSubInstallation.md new file mode 100644 index 00000000..598dab7c --- /dev/null +++ b/docs/methods/install/MooredSubInstallation.md @@ -0,0 +1,73 @@ +(moored-sub-install-methods)= +# Moored Substructure Installation Methodology + +For details of the code implementation, please see the +[Moored Substructure Installation API documentation](#moored-sub-install-api) + +## Overview + +The `MooredSubInstallation` module simulates the manufacture and installation +of moored substuctures for a floating offshore wind project. The installation +procedures include the time required to manufacture a substructure at quayside, +assemble a turbine on the substructure, ballast the completed assembly, tow +the completed assembly to site and hook up the pre-installed moooring lines. + +## Configuration + +The primary configuration parameters available for this module are related to +the quayside assembly process and the vessels used to tow the completed +assemblies to site and complete the installation. The code block highlights +the key parameters available. + +```python +config = { + + ... + + "support_vessel": "example_support_vessel", # Will perform onsite installation procedures. + "ahts_vessel": "example_ahts_vessel", # Anchor handling tug supply vessel associated with each tow group. + "towing_vessel": "example_towing_vessel", # Towing groups will contain multiple of this vessel. + "towing_groups": { + "towing_vessel": 1, # Vessels used to tow the substructure to site. + "station_keeping_vessels": 3, # Vessels used for station keeping during mooring line hookups. + "num_groups": 1 # Number of independent groups. Optional, defualt: 1. + }, + + "port": { + "sub_assembly_lines": 2, # Independent substructure assembly lines. + "sub_storage": 8, # Available storage berths at port for completed substructures. + "turbine_assembly_cranes": 2, # Independent turbine assembly cranes. + "assembly_storage": 8, # Available storage berths at port for completed turbine/substructure assemblies. + }, + + "substructure": { + "takt_time": 168, # h, time to manufacture one substructure. + "towing_speed": 6, # km/h. + }, + + ... +} +``` + +## Processes + +### Quayside Assembly + +| Process | Default | +| ----------------------------------------------------- | ------- | +| Substructure Assembly | 168h | +| Prepare Substructure for Turbine Assembly | 12h | +| Lift and Fasten Tower Section (repeated if necessary) | 12h | +| Lift and Fasten Nacelle | 7h | +| Lift and Fasten Blade (repeated) | 3.5h | +| Mechanical Completion and Verification | 24h | + +### Substructure Tow-out and Assembly + +| Process | Default | +| ----------------------------------- | ---------- | +| Ballast to Towing Draft | 6h | +| Tow-out | calculated | +| Ballast to Operational Draft | 6h | +| Connect Mooring Lines | 22h | +| Check Mooring Lines and Connections | 12h | diff --git a/docs/methods/install/MooringSystemInstallation.md b/docs/methods/install/MooringSystemInstallation.md new file mode 100644 index 00000000..188ee7b2 --- /dev/null +++ b/docs/methods/install/MooringSystemInstallation.md @@ -0,0 +1,48 @@ +(mooring-install-methods)= +# Mooring System Installation Methodology + +For details of the code implementation, please see the +[Mooring System Installation API documentation](#mooring-install-api) + +## Overview + +The `MooringSystemInstallation` module simulates the installation of mooring +lines and anchors at site for a floating offshore wind project. The mooring +system installation is simulated using a multi-purpose support vessel that +transports the components to site and performs the onsite installation +procedures. + +## Configuration + +The primary configuration parameters available for this module are the +installation vessel and the mooring system configuration. An example of these +parameters is presented below. + +```python +config = { + + + "mooring_install_vessel": "example_support_vessel", + "mooring_system": { + "num_lines": 4, # per substructure + "line_mass": 500, # t + "anchor_mass": 500, # t + "anchor_type": "Drag Embedment", # or "Suction Pile" + } + ... +} +``` + +## Processes + +The default times associated with the installation procedure are listed in the +table below. + +| Process | Inputs | Default | +| ------------------------- | ---------------------------------------------------- | ---------- | +| Loadout | `mooring_system_load_time` | 5h | +| Transit | `vessel.transit_speed` | calculated | +| Survey | `mooring_site_survey_time` | 3h | +| Install Anchor (repeated) | `suction_pile_install_time``drag_embed_install_time` | calculated | +| Install Line (repeated) | NA | calculated | +| Transit | `vessel.transit_speed` | calculated | diff --git a/docs/methods/install/OffshoreSubstationInstall.md b/docs/methods/install/OffshoreSubstationInstall.md new file mode 100644 index 00000000..77f21b3e --- /dev/null +++ b/docs/methods/install/OffshoreSubstationInstall.md @@ -0,0 +1,61 @@ +(oss-install-methods)= +# Offshore Substation Installation Methodology + +For details of the code implementation, please see the +[Offshore Substation Installation API documentation](#oss-install-api) + +## Overview + +The `OffshoreSubstationInstallation` module simulates the installation of +offshore substations and their associated substructures. ORBIT currently only +considers monopile substructures, though future release will extend this module +to include an option for jacket substructures. The installation of the +substructure and substation topside is completed with an installation vessel +while components are delivered to site using a feeder barge. + +% The :ref:`process-diagram` outlining the vessel logistics involved can be seen below. + +## Processes + +### Port Operations + +The feeder barge loads and fastens components on deck at port using the port +crane. The default times for fastening each component to deck are listed below. + +| Component | Inputs | Default | +| --------- | --------------------- | ------- | +| Monopile | `mono_fasten_time` | 12h | +| Topside | `topside_fasten_time` | 2h | + +Currently, all vessels are only able to load multiples of complete sets of +components (monopile and topside). + +### Monopile Installation + +Monopile substructures are installed using the installation processes outlined +in the [monopile installation with feeders process diagram](#monopile-install-feeders-process). + +### Topside Installation + +Once the monopile is installed on-site, the topside can be released from deck +storage and attached to the substructure. The following processes outline the +code the processes the installation vessel takes to complete this task: + +| Action | Inputs | Default | +| --------------- | -------------------------------------------------------- | ---------- | +| Reequip Crane | `crane_reequip_time` | 1h | +| Release Topside | `topside_release_time` | 2h | +| Lift Topside | `wtiv.crane_rate``wave_height` | calculated | +| Pump Grout | `grout_pump_time` | 2h | +| Cure Grout | `grout_cure_time` | 24h | +| Jack-down | `depth, extension``speed_above_depth``speed_below_depth` | calculated | + +% Process Diagram + +% ---------------- + +% Offshore Substation Installation + +% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +% Coming soon! diff --git a/docs/methods/install/ScourProtectionInstall.md b/docs/methods/install/ScourProtectionInstall.md new file mode 100644 index 00000000..c02fb87b --- /dev/null +++ b/docs/methods/install/ScourProtectionInstall.md @@ -0,0 +1,62 @@ +(scour-protection-install-methods)= +# Scour Protection Installation Methodology + +For details of the code implementation, please see the +[Scour Protection Installation API documentation](#scour-protection-install-api) + +## Overview + +The `ScourProtectionInstallation` modules simulates the installation of scour +protection around the base of a offshore substructure. In many offshore site +conditions, scour protection is a necessary step to reduce the effects of +hydrodynamic scour development around the substructure. ORBIT models the +installation of a rock layer installed at a diameter surrounding the +substructure base. This process is not typically a significant cost driver for +the project, but the installation time and associated costs are significant +enough that they should not be ignored when computing BOS costs. + +## Configuration + +This module is simple to configure, as the main parameter considered is the +`tons_per_substructure` to install. Currently ORBIT models the simplest +installation method, involving "Side Stone Installation Vessels" that dump +loads of rocks next to the substructure without much ability to ensure that +their payload is distributed evenly. A future version of ORBIT may expand this +module to include more modern installation approaches using a "Fall Pipe +Vessel" that allow for an even distrubution of scour protection material. + +### Example + +```python +{ + 'scour_protection_install_vessel': 'example_vessel' + 'site': {'distance': 20}, + + ... + + 'scour_protection': { + 'tons_per_substructure': 1200 + } + } +} +``` + +## Processes + +The scour protection installation vessel loads rock at port, transits to site +and installs the required amount at each substructure until empty. At this +point, it will return to port to load additional rock and repeat the above +steps until all substructures have had scour protection installed. The process +times for this operation are outlined below. + +| Process | Inputs | Default | +| --------------- | -------------------------------- | ---------- | +| Load Rocks | `load_rocks_time` | 4h | +| Transit to Site | `site_distance`, `transit_speed` | calculated | +| Drop Rocks | `drop_rocks_time` | 10h | + +% Process Diagrams + +% ---------------- + +% Coming soon! diff --git a/docs/methods/install/TurbineInstall.md b/docs/methods/install/TurbineInstall.md new file mode 100644 index 00000000..6db8aba8 --- /dev/null +++ b/docs/methods/install/TurbineInstall.md @@ -0,0 +1,114 @@ +(turbine-install-methods)= +# Turbine Installation Methodology + +For details of the code implementation, please see the +[Turbine Installation API documentation](#turbine-install-api) + +## Overview + +The `TurbineInstallation` module simulates the installation of turbines at +site. For the purpose of this module, the turbine is discretized into five +different components: a tower, nacelle and three turbine blades. The module can +be configured such that a single wind turbine installation vessel (WTIV) +transports and installs all of the turbine components or it can be configured +to include feeder barges that transport the components to site. +{ref}`process-diagrams` detailing the vessel logistics for these two installation +can be seen below. + +:::{note} +For both installation strategies the WTIV performs all of the on site +operations with its onboard crane, either picking components from its own +deck or a neighboring feeder barge. +::: + +## Configuration + +To configure `TurbineInstallation` to utilize feeder barges to transit the +turbine components from port, add the following configuration to the project +configuration. + +```python +config = { + ... + + "feeder": "example_feeder", # name of vessel configuration file without extension + "num_feeders": 2, + + ... +} +``` + +## Processes + +### Port Operations + +Vessels load items and fasten them on their deck at port using the port crane. +Ports are configured with one crane by default, which limits multiple vessels +from accessing port resources at a time. This can be overridden by configuring +a port with additional cranes in a project configuration: + +```python +"port": { + "num_cranes": 2 # Two vessels can access port resource simultaneously. +} +``` + +The default times for fastening each component to deck are listed below. + +| Component | Inputs | Default | +| --------- | --------------------- | ------- | +| Tower | `tower_fasten_time` | 4h | +| Nacelle | `nacelle_fasten_time` | 4h | +| Blade | `blade_fasten_time` | 1.5h | + +Currently, all vessels are only able to load multiples of complete sets of +components (tower, nacelle and three blades). + +### Site Preperation + +Once the WTIV and a set of components are at site (either on the WTIV or a +feeder barge), the WTIV positions itself onsite and jacks up. The following +table outlines the inputs and default times for these tasks. + +| Action | Inputs | Default | +| --------------- | -------------------------------------------------------- | ---------- | +| Position Onsite | `site_position_time` | 2h | +| Jack-up | `depth, extension``speed_above_depth``speed_below_depth` | calculated | + +### Turbine Installation + +After site preperation is complete, the WTIV begins installation by releasing a +turbine from its fastening (either on its own deck or neighboring feeder +barge). The tower is then lifted into place using the WTIV crane and attached +to the substructure. The nacelle is then released from its fastenings, lifted +into place and attached to the tower. The same process is repeated for each of +the three turbine blades. Inputs and process times are summarized in the +following table. + +| Action | Inputs | Default | +| --------------- | -------------------------------------------------- | ---------- | +| Reequip Crane | `crane_reequip_time` | 1h | +| Release Tower | `tower_release_time` | 3h | +| Lift Tower | `turbine.hub_height``wtiv.crane_rate``wave_height` | calculated | +| Attach Tower | `tower_attach_time` | 6h | +| Release Nacelle | `nacelle_release_time` | 3h | +| Lift Nacelle | `turbine.hub_height``wtiv.crane_rate``wave_height` | calculated | +| Attach Nacelle | `nacelle_attach_time` | 6h | +| Release Blade | `blade_release_time` | 1h | +| Lift Blade | `turbine.hub_height``wtiv.crane_rate``wave_height` | calculated | +| Attach Blade | `blade_attach_time` | 3.5h | + +(process-diagrams)= +## Process Diagrams + +### Single WTIV Installation + +![Single WTIV turbine installation proceess diagrame](../../images/process_diagrams/turbine_single_wtiv.png) + +### WTIV with Feeder Barges Installation + +![WTIV with feeder barges turbine installation proceess diagrame](../../images/process_diagrams/turbine_wtiv_with_feeders.png) + +### Component Installation + +![Component installation proceess diagrame](../../images/process_diagrams/turbine_install.png) diff --git a/docs/publications.md b/docs/publications.md new file mode 100644 index 00000000..1379278a --- /dev/null +++ b/docs/publications.md @@ -0,0 +1,45 @@ +# Publications + +## ORBIT Technical Report + +Please cite this repository using the following citation, or with proceeding Bibtex. + +> Nunemaker, Jacob, Shields, Matthew, Hammond, Robert, et al., +> "ORBIT: Offshore Renewables Balance-of-System and Installation Tool," (2020), +> https://doi.org/10.2172/1660132 + +```bibtex +@techreport{orbit_tech_report, + author={Nunemaker, Jacob and Shields, Matthew and Hammond, Robert and Duffy, Patrick}, + title={ORBIT: Offshore Renewables Balance-of-System and Installation Tool}, + institution={National Renewable Energy Laboratory (NREL), Golden, CO (United States)}, + doi={10.2172/1660132}, + url={https://www.osti.gov/biblio/1660132}, + place={United States}, + year={2020}, + month={08} +} +``` + +## Select Publications using ORBIT + +For a mostly complete listing of papers using ORBIT, please see +[Google Scholar](https://scholar.google.com/scholar?start=0&hl=en&as_sdt=4005&sciodt=0,6&cites=5942976121239895064&scipsc=) + +[Beyond 15 MW: A cost of energy perspective on the next generation of drivetrain technologies for offshore wind turbines](https://doi.org/10.1016/j.apenergy.2023.121272) + +> Barter, Garrett E., Latha Sethuraman, Pietro Bortolotti, Jonathan Keller, and David A. Torrey. +> "Beyond 15 MW: A cost of energy perspective on the next generation of drivetrain technologies for offshore wind turbines." +> Applied Energy 344 (2023): 121272. + +[Impacts of Turbine and Plant Upsizing on the Levelized Cost of Energy for Offshore Wind](https://doi.org/10.1016/j.apenergy.2021.117189) + +> Shields, Matt, Philipp Beiter, Jake Nunemaker, Aubryn Cooperman, and Patrick Duffy. +> "Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind." +> Applied Energy 298 (2021): 117189. + +[Process-Based Balance-of-System Cost Modeling for Offshore Wind Power Plants in the United States](https://iopscience.iop.org/article/10.1088/1742-6596/1452/1/012039/meta) + +> Shields, Matt, and Jake Nunemaker. +> "Process-based balance of system cost modeling for offshore wind power plants in the United States." +> In Journal of Physics: Conference Series, vol. 1452, no. 1, p. 012039. IOP Publishing, 2020. diff --git a/docs/refs.bib b/docs/refs.bib new file mode 100644 index 00000000..67096db9 --- /dev/null +++ b/docs/refs.bib @@ -0,0 +1,69 @@ +@techreport{cower2024, + title={Cost of Wind Energy Review: 2024 Edition [Slides]}, + author={Stehly, Tyler and Duffy, Patrick and Mulas Hernando, Daniel}, + year={2024}, + institution={National Renewable Energy Laboratory (NREL), Golden, CO (United States)}, + doi={10.2172/2479271} +} + +@techreport{boem2018vineyard, + title={Draft Construction and Operations Plan for Vineyard Wind Project Appendices}, + year={2018}, + month={10}, + author={Vineyard Wind LLC and Epsilon Associates Inc.}, + institution={Vineyard Wind LLC and Epsilon Associates Inc.}, + url={https://www.boem.gov/sites/default/files/renewable-energy-program/State-Activities/MA/Vineyard-Wind/Vineyard-Wind-COP-Volume-III-Appendix-III-K.pdf} +} + +@article{arany2017design, + title={Design of monopiles for offshore wind turbines in 10 steps}, + author={Arany, Laszlo and Bhattacharya, Subhamoy and Macdonald, John and Hogan, Stephen J}, + journal={Soil Dynamics and Earthquake Engineering}, + volume={92}, + pages={126--152}, + year={2017}, + publisher={Elsevier}, + doi={10.1016/j.soildyn.2016.09.024} +} + +@article{maness2017BOS, + title={NREL offshore balance-of-system model}, + author={Maness, Michael and Maples, Benjamin and Smith, Aaron}, + journal={National Renewable Energy Lab (NREL)}, + year={2017}, + url={https://www.nlr.gov/docs/fy17osti/66874.pdf}, + doi={10.2172/1339522} +} + +@techreport{beiter2016spatial, + title={A spatial-economic cost-reduction pathway analysis for us offshore wind energy development from 2015-2030}, + author={Beiter, Philipp and Stehly, Tyler}, + year={2016}, + institution={NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))}, + doi={10.2172/1324526} +} + +@techreport{dnv2014osw, + title={Design of Offshore Wind Turbine Structures}, + author={Det Norske Veritas AS}, + institution={Det Norske Veritas AS}, + year={2014}, + month={5}, + url={https://rules.dnvgl.com/docs/pdf/DNV/codes/docs/2014-05/Os-J101.pdf} +} + +@inbook{api2000, + title={Recommended practice for planning, designing and constructing fixed offshore platforms-working stress design}, + author={API}, + year={2005}, + publisher={American Petroleum Institute}, + pages={68-81} +} + +@book{poulos1980pile, + title={Pile foundation analysis and design}, + author={Poulos, Harry George and Davis, Edward Hughesdon}, + number={Monograph}, + publisher={Rainbow-Bridge Book Co.}, + year={1980} +} diff --git a/docs/source/api.rst b/docs/source/api.rst deleted file mode 100644 index 2f01ee1b..00000000 --- a/docs/source/api.rst +++ /dev/null @@ -1,12 +0,0 @@ -.. _api: - -API Reference -============= - -.. toctree:: - :maxdepth: 3 - - api_ProjectManager - api_ParametricManager - api_DesignPhase - api_InstallPhase diff --git a/docs/source/api_DesignPhase.rst b/docs/source/api_DesignPhase.rst deleted file mode 100644 index 0046bb73..00000000 --- a/docs/source/api_DesignPhase.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. _design_phases: - -Design Phases - ``ORBIT.phases.design`` -============================================= - -ORBIT includes the following design modules that can be used within -ProjectManager. These design processes are intended to broadly capture scaling -trends but are not intended to be used for actual designs. - -.. toctree:: - :maxdepth: 2 - - phases/design/api_MonopileDesign - phases/design/api_ScourProtectionDesign - phases/design/api_ArraySystemDesign - phases/design/api_ExportSystemDesign - phases/design/api_ElectricalDesign - phases/design/api_OffshoreSubstationDesign - phases/design/api_SemiSubmersibleDesign - phases/design/api_SparDesign - phases/design/api_MooringSystemDesign diff --git a/docs/source/api_InstallPhase.rst b/docs/source/api_InstallPhase.rst deleted file mode 100644 index 8902c390..00000000 --- a/docs/source/api_InstallPhase.rst +++ /dev/null @@ -1,23 +0,0 @@ -.. _install: - -Install Phases - ``ORBIT.phases.install`` -=============================================== - -ORBIT includes the following installation modules that can be used within -ProjectManager. These modules utilize SimPy to model individual processes and -their constraints due to weather and vessel interactions. For a more detailed -description of vessel scheduling within ORBIT, please see `add link`. - -.. toctree:: - :maxdepth: 1 - - phases/install/monopile/api_MonopileInstallation - phases/install/scour/api_ScourProtectionInstall - phases/install/turbine/api_TurbineInstallation - phases/install/array/api_ArrayCableInstall - phases/install/export/api_ExportCableInstall - phases/install/oss/api_OffshoreSubstationInstall - phases/install/quayside_towout/api_MooredSubInstallation - phases/install/mooring/api_MooringSystemInstallation - -.. phases/install/jacket/api_JacketInstall diff --git a/docs/source/api_ParametricManager.rst b/docs/source/api_ParametricManager.rst deleted file mode 100644 index ca4b1909..00000000 --- a/docs/source/api_ParametricManager.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _manager: - -Parametric Configurations - ``ORBIT.ParametricManager`` -======================================================= - -.. autoclass:: ORBIT.ParametricManager - :members: diff --git a/docs/source/api_ProjectManager.rst b/docs/source/api_ProjectManager.rst deleted file mode 100644 index 7f9170b0..00000000 --- a/docs/source/api_ProjectManager.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _manager: - -Project Configuration and Management - ``ORBIT.manager`` -============================================================== - -.. autoclass:: ORBIT.manager.ProjectManager - :members: diff --git a/docs/source/changelog.rst b/docs/source/changelog.rst deleted file mode 100644 index d69e45ee..00000000 --- a/docs/source/changelog.rst +++ /dev/null @@ -1,373 +0,0 @@ -.. _changelog: - -ORBIT Changelog -=============== - -1.2.6 ------ -- Implements `create_layout_df` for the `CustomArraySystemDesign` model to ensure - compatibility with workflows relying on the layout generation tools. - -1.2.5 ------ -- Allow for a Pandas DataFrame to be passed directly to the ``CustomArraySystemDesign.layout_data`` - configuration input. -- Move the matplotlib import from the import section of ``/ORBIT/phases/design/array_system_design.py`` - to the ``CustomArraySystemDesign.plot_array_system`` for missing module error handling. -- Adds a general layout ``DataFrame`` creation method as ``ArraySystemDesign.create_layout_df()`` that - is called by the ``save_layout`` method to maintain backwards compatibility, but opens up the ability - gather the layout without saving it to a file. -- Updated default `soft_capex` factors. `PR #201 `_ - - `construction_insurance_factor` updated from 0.115 to 0.0207 based on industry benchmarking, resulting in higher construction insurance costs. - - `interest_during_construction` updated from 4.4% to 6.5% based on financial assumptions from the 2025 Annual Technology Baseline (ATB), increasing construction financing costs. - - `decommissioning_factor` updated from 0.175 to 0.2 based on industry benchmarking, leading to higher decommissioning costs than in previous versions. -- Updated default `project_capex` values. `PR #201 `_ - - `site_auction_price` increased from 100M to 105M USD to account for rent fees before operation. - - `site_assessment_cost`, `construction_plan_cost`, and `installation_plan_cost` increased from 50M, 1M, and 0.25M USD to 200M, 25M, and 25M USD, respectively. - - Total `project_capex` excluding `site_auction_price` now sums to 250M USD, aligning with DevEx recommendations based on industry benchmarking. - - These updates lead to higher default total project costs than in previous versions. -- Included onshore substation costs in BOS CapEx and project breakdown. `PR #201 `_ - - The `ElectricalDesign` module previously calculated onshore substation costs but did not include them in `capex_breakdown` or `bos_capex`. - - These costs are now incorporated when `ElectricalDesign` is used, resulting in higher `bos_capex`, `soft_capex`, and `total_capex` than in prior versions. -- Cable configuration file updates. `PR #201 `_ - - Added a new dynamic cable configuration file for floating cases: `library/cables/XLPE_1200mm_220kV_dynamic.yaml`. - - Updated cost values for `library/cables/XLPE_630mm_66kV.yaml` and `library/cables/XLPE_630mm_66kV_dynamic.yaml` based on industry benchmarking. - - All cable cost updates are expressed in 2024 USD for consistency with other library configuration files. - -1.2.4 ------ -- Support Python 3.14 - -1.2.3 ------ -- Adjusted the recent np.trapz fix to be compatible with numpy < 2.0 - -1.2.2 ------ -- Replaced the deprecated `numpy.trapz` with `numpy.trapezoid`. -- Deprecates Python 3.9 support in preparation for EOL and seamless benedict compatibility. - -1.2.1 ------ -- Removed `wisdem_api.py` because WISDEM now uses orbit as a pip installed package. -- Added Python 3.12 and 3.13 to the workflow files. -- Moved matplotlib as an optional dependency - -1.2 ---- -- New cable ``library/cables/XLPE_1200mm_220kV.yaml`` Is a 220kV cable that can carry ~400MW of HVAC power. -- Fixed frozen python-benedict version - - ``ParametricManager`` can still use '.' as a keypath separator (no change to user inputs) and is compatible with latest python-benedict -- Updated various default costs to 2024 USD. `PR #187 `_ - - Cost rates for different models were determined by benchmarking the costs through industry outreach, - along with adjustments based on commodity prices, inflation, and labor indices. - - ORBIT assumes a procurement year of 2024 in the files: - - ``defaults/common_costs.yaml`` represents all the design related costs. - - ``ORBIT/manager.py`` includes project related costs - - ``library/cables/*`` shows all the cables with updated `cost_per_km` - - ``library/vessels/*`` shows all the vessels with updated `day_rate` - - Added ``defaults/costs_by_procurement_year.csv`` which provides the default costs for other procurement year, - but in 2024 USD. -- Bug Fix: Characteristic Impedance calculation correction. `Issue #186 `_ - - There were some documentation typos and a units error in the calculation, where mH (10^-3) was divided by nF (10^-9) - - Updated several tests with new values that correlate to the latest cable power capacity -- Updated WISDEM API (`wisdem_api.py`) - - Match some variable names and inputs that have diverged over time. - - Caught turbine_capex double count in WISDEM when using `total_capex` from ORBIT. - - Updated some tests. -- Enhanced ``ProjectManager``: `PR #177 `_ - - Improvements made to `soft_capex` calculations because previous versions - used default `$/kW` values from the 2018 Cost of Wind Energy Review unless provided by - the user. Those default values are out of date and do not scale with the size of the - project which is not entirely accurate. - - `soft_capex` is now calculated as sum of `construction_insurance_capex`, `decommissioning_capex`, - `commissioning_capex`, `procurement_contingency_capex`, `installation_contingency_capex`, - `construction_financing_capex`. NOTE: user can still specify the same `$/kW` values if they choose. - - New factors were implemented to calculated updated project_parameters if the user does not specify - any inputs. - -1.1 ---- - -New features -~~~~~~~~~~~~ -- Enhanced ``MooringSystemDesign``: - - Can specify catenary or semitaut mooring systems. (use `mooring_type`) - - Can specify drag embedment or suction pile anchors. (use `anchor_type`) - - Description: This class received some new options that the user can - specify to customize the mooring system. By default, this design uses - catenary mooring lines and suction pile anchors. The new semitaut mooring - lines use interpolation to calculate the geometry and cost based on - (Cooperman et al. 2022, https://www.nrel.gov/docs/fy22osti/82341.pdf). - - See ``5. Example Floating Project`` for more details. -- New ``ElectricalDesign``: - - Now has HVDC or HVAC transmission capabilities. - - New tests created ``test_electrical_export.py`` - - Description: This class combines the elements of ``ExportSystemDesign`` and the - ``OffshoreSubstationDesign`` modules. Its purpose is to represent the - entire export system more accurately by linking the type of cable - (AC versus DC) and substation’s components (i.e. transformers versus converters). - Most export and substation component costs were updated to include a per-unit cost - rather than a per-MW cost rate and they can be added to the project config file too. - Otherwise, those per-unit costs use default and were determined with the help of - industry experts. - - This module’s components’ cost scales with number of cables and - substations rather than plant capacity. - - The offshore substation cost is calculated based on the cable type - and number of cables, rather than scaling function based on plant capacity. - - The mass of an HVDC and HVAC substation are assumed to be the same. - Therefore, the substructure mass and cost functions did not change. - - An experimental onshore cost function was also added to account for - the duplicated interconnection components. Costs will vary depending - on the cable type. - - See new example ``Example - Using HVDC or HVAC`` for more details. -- Enhanced ``FloatingOffshoreSubStation``: - - Fixed the output substructure type from Monopile to Floating. (use `oss_substructure_type`) - - Removes any pile or fixed-bottom substructure geometry. - - See ``Example 5. Example Floating Project`` for more details. -- Updated ``MoredSubInstallation``: - - Uses an AHTS vessel which must be added to project config file. - - See ``example/example_floating_project.yaml`` (use `ahts_vessel`) -- New ``22MW_generic.yaml`` turbine. - - Based on the IEA - 22 MW reference wind turbine. - - See ``library/turbines`` for more details. -- New cables: - - Varying HVDC ratings - - Varying HVDC and HVAC "dynamic" cables for floating projects. - - See ``library/cables`` for all the cables and more details. - -Updated default values -~~~~~~~~~~~~~~~~~~~~~~ -- ``defaults/process_times.yaml`` - - `drag_embedment_install_time`` increased from 5 to 12 hours. -- ``phases/install/quayside_assembly_tow/common.py``: - - lift and attach tower section time changed from 12 to 4 hours per section, - - lift and attach nacelle time changed from 7 to 12 hours. -- ``library/cables/XLPE_500mm_132kV.yaml``: - - `cost_per_km` changed from $200k to $500k. -- ``library/vessels/example_cable_lay_vessel.yaml``: - - `min_draft` changed from 4.8m to 8.5m, - - `overall_length` changed from 99m to 171m, - - `max_mass` changed 4000t to 13000t, -- ``library/vessels/example_towing_vessel.yaml``: - - `max_waveheight` changed from 2.5m to 3.0m, - - `max_windspeed` changed 20m to 15m, - - `transit_speed` changed 6km/h to 14 km/h, - - `day_rate` changed $30k to $35k - -Improvements -~~~~~~~~~~~~ -- All design classes have new tests to track total cost to flag any changes that may - impact final project cost. -- Relocated all the get design costs in each design class to `common_cost.yaml`. -- Fully adopted `pyproject.toml` for managing all possible tool settings, and - removed the tool-specific files from the top-level of the directory. -- Replaced flake8 and pylint with ruff to adopt a cleaner, faster, and easier - to manage linting and autoformatting workflow. As a result, some of the more - onerous checks have been removed to discourage the use of - `git commit --no-verify`. This change has also added in other rules that - discourage Python anti-patterns and encourage modern Python usage. -- NOTE: Users may wish to run - `git config blame.ignoreRevsFile .git-blame-ignore-revs` to ignore the - reformatting edits in their blame. - -1.0.8 ------ - -- Added explicit methods for adding custom design or install phases to - ``ProjectManager``. -- Added WOMBAT compatibility for custom array system files. -- Fixed bug in custom array cable system design that breaks for plants with - more than two substations. - -1.0.7 ------ - -- Added ``SupplyChainManager``. -- Added ``JacketInstallation`` module. -- Added option to use dynamic supply chain in ``MonopileInstallation`` module. - -1.0.6 ------ - -- Expanded tutorial and examples. -- Added templates for design and install modules. -- Added ports to library pathing. -- Misc. bugfixes. - -1.0.5 ------ - -- Added initial floating offshore substation installation module. -- Added option to specific floating cable depth in cable design modules. -- Bugfix in ``project.total_capex``. - -1.0.4 ------ - -- Added ability to directly prescribe weather downtime through the - ``availability`` keyword -- Added support for generating linear models using ``ParametricManager`` - -1.0.2 ------ - -- Added ``ProjectManager.capex_breakdown``. - -1.0.1 ------ - -- Default behavior of ``ParametricManager`` has been changed. Input parameters - are now zipped together and ran as a discrete set of configs. To use the past - functionality (finding the product of all input parameters), use the option - ``product=True`` -- Bugfix: Added port costs to floating substructure installation modules. -- Revised docs for running the Example notebooks and added link to a tutorial - about working with jupyter notebooks. - -1.0.0 ------ - -- New feature: ``ParametricManager`` for running parametric studies. -- Added procurement cost inputs and total cost methods to installation phases. - Design phases are now only used to fill in the design and do not return a - cost associated with the design. -- Refactored aggregation project level outputs in ``ProjectManager``. -- Revised Net Present Value calculation to utilize new project outputs. -- Added ``load_config`` and ``save_config`` functions. -- Moved ``ORBIT.library`` to ``OBRIT.core.library``. -- Centralized model defaults to ``ORBIT.core.defaults``. -- ``ProjectManager.project_actions`` renamed to ``ProjectManager.actions`` -- ``ProjectManager.project_logs`` renamed to ``ProjectManager.logs`` -- ``ProjectManager.run_project()`` renamed to ``ProjectManager.run()`` -- Moved documentation hosting to gh-pages. - -0.5.1 ------ - -- Process time kwargs should now be passed through ``ProjectManager`` in a - dictionary named ``processes`` in the config. -- Revised ``prep_for_site_operations`` and related processes to allow for - dynamically positioned vessels. -- Updated WISDEM API to include floating functionality. - -0.5.0 ------ - -- Initial release of floating substructure functionality in ORBIT. -- New design modules: ``MooringSystemDesign``, ``SparDesign`` and - ``SemiSubmersibleDesign``. -- New installation modules: ``MooringSystemInstallation`` and - ``MooredSubInstallation`` -- Cable design and installation modules modified to calculate catenary lengths - of suspended cable at depths greater than 60m. - -0.4.3 ------ - -- New feature: Cash flow and net present value calculation within - ``ProjectManager``. -- Revised ``CustomArraySystemDesign`` module. -- Revised assumptions in ``MonopileDesign`` module to bring results in line - with industry numbers. - -0.4.2 ------ - -- New feature: Phase dependencies in ``ProjectManager``. -- New feature: Windspeed constraints at multiple heights, including automatic - interpolation/extrapolation of configured windspeed profiles. -- Added option to define ``mobilization_days`` and ``mobilization_mult`` in a - ``Vessel`` configuration file. -- Added option for pre-installation trenching operations to - ``ArrayCableInstallation`` and ``ExportCableInstallation``. -- Revised ``OffshoreSubstationDesign`` to scale the size of the substations - with the user-configured number of substations. -- Bugfix in the returned argument order of ``ProjectManager.run_install_phase`` - where the cost of a prior phase would be incorrectly applied as the elapsed - time. - -0.4.1 ------ - -- Modified installation to require version of marmot-agents that has an - internal copy of simpy. -- Added/expanded ``detailed_outputs`` for all modules. -- Standardized naming of weight/mass terms to mass throughout the model. -- Cleanup in ``ProjectManager``. - -0.4.0 ------ - -- Vessel mobilization added to all vessels in all installation modules. - Defaults to 7 days at 50% day-rate. -- Cable lay, bury and simulataneous lay/bury methods are not flagged as - suspendable to avoid unrealistic project delays. -- Cost of onshore transmission construction added to - ``ExportCableInstallation``. -- Simplified ``ArrayCableInstallation``, ``ExportCableInstallation`` modules. -- Removed `pandas` from the internals of the model, though it is still useful - for tabulating the project logs. -- Revised package structure. Functionally formerly in ORBIT.simulation or - ORBIT.vessels has been moved to ORBIT.core. -- ``InstallPhase`` cleaned up and slimmed down. -- ``Environment`` and associated functionality has been replaced with - ``marmot.Environment``. -- Logging functionality revised. No longer uses the base python logging module. -- ``Vessel`` now inherits from ``marmot.Agent``. -- Tasks that were in ``ORBIT.vessels.tasks`` have been moved to their - respective modules and restructured with ``marmot.process`` and - ``Agent.tasks``. -- Modules inputs cleaned up. ``type`` parameters are no longer required for - monopile, transition piece or turbine component definitions. -- Removed old/irrelevant tests. - -0.3.5 ------ - -- Added 'per kW' properties to ``ProjectManager`` CAPEX results. - -0.3.4 ------ - -- Added configuration to ``ProjectManager`` that allows exceptions to be caught - within individual modules and allows the project as a whole to continue. -- Fixed installation process when installing from GitHub. - -0.3.3 ------ - -- Added configuration for multiple tower sections in ``TurbineInstallation``. -- Added configuration for seperate lay/burial in ``ArrayCableInstallation`` and - ``ExportCableInstallation``. -- Overhauled test suite and associated library. -- Bugfix in ``CableCarousel``. -- Expanded WISDEM Fixed API. - -0.3.2 ------ - -- Initial release of fixed substructure WISDEM API -- Material cost for monopiles and transition pieces added to ``MonopileDesign`` -- Updated ``ProjectManager`` to allow user to override default ``DesignPhase`` - results -- Moved config validation to ``BasePhase`` and added call to - ``self.validate_config`` for all current modules -- Config validation logic reworked so dicts of optional values are not - required -- Added method to resolve project capacity in ``ProjectManager``. A user can - now input ``plant.num_turbines`` and ``turbine.turbine_rating`` and - ``plant.capacity`` will be added to the config. -- Added initial set of standardized inputs to ``ProjectManager``: - - - ``self.installation_capex`` - - ``self.installation_time`` - - ``self.project_days`` - - ``self.bos_capex`` - - ``self.turbine_capex`` - - ``self.total_capex`` - -0.3.1 ------ - -- Updated README diff --git a/docs/source/doc_DesignPhase.rst b/docs/source/doc_DesignPhase.rst deleted file mode 100644 index 1e5f3470..00000000 --- a/docs/source/doc_DesignPhase.rst +++ /dev/null @@ -1,20 +0,0 @@ -.. _designtoc: - -Design Phases -============= - -The following pages cover the methodology behind the design phases available in -the model. - -.. toctree:: - :maxdepth: 1 - - phases/design/doc_MonopileDesign - phases/design/doc_ScourProtectionDesign - phases/design/doc_ArraySystemDesign - phases/design/doc_ExportSystemDesign - phases/design/doc_ElectricalDesign - phases/design/doc_OffshoreSubstationDesign - phases/design/doc_SemiSubmersibleDesign - phases/design/doc_SparDesign - phases/design/doc_MooringSystemDesign diff --git a/docs/source/doc_InstallPhase.rst b/docs/source/doc_InstallPhase.rst deleted file mode 100644 index be568c3e..00000000 --- a/docs/source/doc_InstallPhase.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. _installtoc: - -Install Phases -============== - -The following pages cover the methodology behind the installation phases -available in the model. - -.. toctree:: - :maxdepth: 1 - - phases/install/monopile/doc_MonopileInstall - phases/install/scour/doc_ScourProtectionInstall - phases/install/turbine/doc_TurbineInstall - phases/install/array/doc_ArrayCableInstall - phases/install/export/doc_ExportCableInstall - phases/install/oss/doc_OffshoreSubstationInstall - phases/install/quayside_towout/doc_MooredSubInstallation - phases/install/mooring/doc_MooringSystemInstallation - -.. phases/install/jacket/doc_JacketInstall diff --git a/docs/source/doc_ParametricManager.rst b/docs/source/doc_ParametricManager.rst deleted file mode 100644 index ec825223..00000000 --- a/docs/source/doc_ParametricManager.rst +++ /dev/null @@ -1,11 +0,0 @@ -.. _managertoc: - -Parametric Manager -============= - -The following pages cover the methodology behind the parametric manager. For -more details of the code implementation, please see :doc:`Parametric Manager API `. - -.. note:: - - Page currently under construction. diff --git a/docs/source/doc_ProjectManager.rst b/docs/source/doc_ProjectManager.rst deleted file mode 100644 index bec87523..00000000 --- a/docs/source/doc_ProjectManager.rst +++ /dev/null @@ -1,167 +0,0 @@ -.. _managertoc: - -Project Manager -============= - -The following pages cover the methodology behind the project manager. - -.. note:: - - Page currently under construction. - -Overview --------- -The ``ProjectManager`` is the primary system for interacting with ORBIT to simulate -a wind project. Users can customize their project by specifying a a wide variety of -parameters as a dictionary (see :doc:`ProjectManager tutorial `). -For more details of the code implementation, please see :doc:`Project Manager API `. - -It instantiates a class aggregates project parameters, specifies a start date, and interprets a weather -profile, and it employs a collection of decorators, `methods`, and `classmethods` to run the simulation. -Among these methods are `design_phases` and `install_phases` that serve as components to the simulation. -Additionally, some methods search and catch key errors to avoid simulation issues, export progress logs, -and save the outputs. - -Run ---- -This method checks to see if a design or install phase is instatiated prior to running them. Depending on -which design phases are specified, each phase is run in no particular order and the results are added to -`.design_results` dictionary. Conversely, the install phases can be run sequentially or as overlapped -processes (see example: :doc:`Overlapping install `). It is worth noting, that ORBIT -has built in logic to determine any dependency between install phases. - -Properties ----------- -The `@property` decorators allow the ``ProjectManager`` to access and manipulate the attributes of certain classes. Of the -several properties some important ones are: - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - -- capex_categories: CapEx Categories -- npv: Net Present Value -- turbine_capex: CapEx of the Wind Turbine. -- bos_capex: BOS CapEx includes the System CapEx and Installation CapEx. -- system_capex: Total system procurement cost. -- installation_capex: Total installation cost. -- project_capex: Project Capex includes, site auction, site assessment, construction plan, and installation plan costs. -- soft_capex_breakdown: Soft CapEx Categories - -Finally, these attributes are collected in an `output` dictionary. - -Class Methods -------------- -The `@classmethod` decorator allows the ``ProjectManager`` to access and modify class-level attributes. - -- register_design_phase: Add a custom design phase to the ``ProjectManager`` class. -- register_install_phase: Add a custom install phase to the ``ProjectManager`` class. - -Soft CapEx Methodology ------------------------ -The methodology outlined in Beiter et al. (2016) applies multipliers -(or assumed factors) to the magnitude of capital expenditure (CapEx) -components in order to derive the Soft CapEx components. The factors used are -consistent with those used in Stehly et al. (2024), enabling the soft costs to -scale in proportion to the other costs calculated within ORBIT. Soft Capex is -calculated using the default multipliers and parameters from Stehly et al. (2024). -Users can specify any of the :py:attr:`soft_capex_factors` below if they prefer to -override the default values. Additionally, users can assign $/kW values for -any calculated Soft CapEx component, ending with :math:`\_capex`, for -simplicity. The soft CapEx component's definitions and their calculations -are provided below. - -Construction Insurance -~~~~~~~~~~~~~~~~~~~~~~ -All risk property, delays in start-up, third party liability, and broker's fees. - -:py:attr:`construction_insurance_factor` = 0.0115 - -:math:`construction\_insurance\_capex = construction\_insurance\_factor \quad \times` -:math:`\hspace{10em} (turbine\_capex + bos\_capex + project\_capex )` - -Commissioning -~~~~~~~~~~~~~ -Cost to integrate and commission the project. - -:py:attr:`commissioning_factor` = 0.0115 - -:math:`commissioning\_capex = commissioning\_factor \quad \times` -:math:`\hspace{10em} (turbine\_capex + bos\_capex + project\_capex )` - -Decommissioning -~~~~~~~~~~~~~~~ -Surety bond lease to ensure that the burden for removing offshore structures -at the end of their useful life does not fall on taxpayers. - -:py:attr:`decommissioning_factor` = 0.175 - -:math:`decommissioning\_capex = decommissioning\_factor \times installation\_capex` - -Procurement Contingency -~~~~~~~~~~~~~~~~~~~~~~~ -Provision for an unforeseen event or circumstance during the procurement process. - -:py:attr:`procurement_contingency_factor` = 0.0575 - -:math:`procurement\_contingency\_capex = procurement\_contingency\_factor \quad \times` -:math:`\hspace{10em} (turbine\_capex + bos\_capex + project\_capex - installation\_capex)` - - -Installation Contingency -~~~~~~~~~~~~~~~~~~~~~~~~ -Provision for an unforeseen event or circumstance during the installation process. - -:py:attr:`installation_contingency_factor` = 0.0345 - -:math:`installation\_contingency\_capex = installation\_contingency\_factor \times installation\_capex` - -Construction Financing -~~~~~~~~~~~~~~~~~~~~~~ -Additional expenses incurred from interest on loans used to fund a construction -project, calculated based on the borrowing period and the project's spending schedule. - -The spend schedule is based on industry data from a U.S. project. - -:py:attr:`spend_schedule` = - -+----------+-----------------+ -| Year | Amount | -+==========+=================+ -| 0 | 0.25 | -+----------+-----------------+ -| 1 | 0.25 | -+----------+-----------------+ -| 2 | 0.30 | -+----------+-----------------+ -| 3 | 0.10 | -+----------+-----------------+ -| 4 | 0.10 | -+----------+-----------------+ -| 5 | 0.00 | -+----------+-----------------+ - -.. note:: - The Amount in the spend schedule must sum to 1.0 (100%). - -:py:attr:`interest_during_construction` = 0.044 - -:py:attr:`tax_rate` = 0.26 - -:py:attr:`construction_financing_factor` = - -.. math:: - - \sum_{k=0}^{n-1} spend\_schedule_k \times (1 + (1 - tax\_rate) \times ((1+ interest\_during\_construction)^{k+0.5} - 1) - -where *k* is the current year and *n* is the total number of years in :py:attr:`spend_schedule`. - -:math:`construction\_financing\_capex = (construction\_financing\_factor - 1) \quad \times` -:math:`\hspace{10em} (construction\_insurance\_capex + commissioning\_capex \quad +` -:math:`\hspace{11em} decommissioning\_capex + procurement\_contingency\_capex \quad +` -:math:`\hspace{12em} installation\_contingency\_capex + turbine\_capex + bos\_capex)` - -References ----------- -- Stehly, T., Duffy, P., & Mulas Hernando, D. (2024). Cost of Wind Energy Review: 2024 Edition. https://doi.org/10.2172/2479271 -- Beiter, P., Musial, W., Kilcher, L., Sirnivas, S., Stehly, T., Gevorgian, V., Mooney, M., Scott, G., Smith, A., Damiani, R., & Maness, M. (2016). A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030. https://doi.org/10.2172/1324526 diff --git a/docs/source/examples.rst b/docs/source/examples.rst deleted file mode 100644 index e64f3996..00000000 --- a/docs/source/examples.rst +++ /dev/null @@ -1,10 +0,0 @@ -.. _examples: - -Examples -======== - -There are several example interactive notebooks located in the ORBIT -`repository `_ - -For information on running jupyter notebooks, please see this -`tutorial `_. diff --git a/docs/source/installation/index.rst b/docs/source/installation/index.rst deleted file mode 100644 index 4fdf184f..00000000 --- a/docs/source/installation/index.rst +++ /dev/null @@ -1,64 +0,0 @@ -.. _installation: - -Installing ORBIT -================ -As of version 0.5.2, ORBIT is now pip installable with ``pip install orbit-nrel``. - -Development Setup ------------------ - -The steps below are for more advanced users that would like to modify and -and contribute to ORBIT. - -Environment -~~~~~~~~~~~ - -A couple of notes before you get started: - - It is assumed that you will be using the terminal on MacOS/Linux or the - Anaconda Prompt on Windows. The instructions refer to both as the - "terminal", and unless otherwise noted the commands will be the same. - - To verify git is installed, run ``git --version`` in the terminal. If an error - occurs, install git using these `directions `_. - - The listed installation process is intended to be the easiest for any OS - to get started. An alternative setup that doesn't rely on Anaconda for - setting up an environment can be followed - `here `_. - -Instructions -~~~~~~~~~~~~ - -1. Download the latest version of `Miniconda `_ - for the appropriate OS. Follow the remaining `steps `_ - for the appropriate OS version. -2. From the terminal, install pip by running: ``conda install -c anaconda pip`` -3. Next, create a new environment for the project with the following. - - .. code-block:: console - - conda create -n python=3.10 --no-default-packages - - To activate/deactivate the environment, use the following commands. - - .. code-block:: console - - conda activate - conda deactivate - -4. Clone the repository: - ``git clone https://github.com/WISDEM/ORBIT.git`` -5. Navigate to the top level of the repository - (``/ORBIT/``) and install ORBIT as an editable package - with following command. - - .. code-block:: console - - # Note the "." at the end - pip install -e . - - # OR if you are you going to be contributing to the code or building documentation - pip install -e '.[dev]' -6. (Development only) Install the pre-commit hooks to autoformat and lint code. - - .. code-block:: console - - pre-commit install diff --git a/docs/source/intro/index.rst b/docs/source/intro/index.rst deleted file mode 100644 index 075ea499..00000000 --- a/docs/source/intro/index.rst +++ /dev/null @@ -1,14 +0,0 @@ -.. _intro: - -Introduction -============ - -Welcome to the ORBIT documentation! These next few pages will provide a brief -overview of balance-of-system cost modeling, a description of the model design -and a tutorial for getting started with ORBIT. - -.. toctree:: - :maxdepth: 1 - - overview - bos diff --git a/docs/source/methods.rst b/docs/source/methods.rst deleted file mode 100644 index d8fcae12..00000000 --- a/docs/source/methods.rst +++ /dev/null @@ -1,13 +0,0 @@ -.. _methods: - -Methodology -=========== - -.. toctree:: - :maxdepth: 2 - - doc_ProjectManager - doc_ParametricManager - doc_DesignPhase - doc_InstallPhase - doc_CommonCost diff --git a/docs/source/phases/design/api_ElectricalDesign.rst b/docs/source/phases/design/api_ElectricalDesign.rst deleted file mode 100644 index 4c65f8d1..00000000 --- a/docs/source/phases/design/api_ElectricalDesign.rst +++ /dev/null @@ -1,8 +0,0 @@ -Electrical System Design API -============================ - -For detailed methodology, please see -:doc:`Electrical System Design `. - -.. autoclass:: ORBIT.phases.design.ElectricalDesign - :members: diff --git a/docs/source/phases/design/api_ExportSystemDesign.rst b/docs/source/phases/design/api_ExportSystemDesign.rst deleted file mode 100644 index e2afab2f..00000000 --- a/docs/source/phases/design/api_ExportSystemDesign.rst +++ /dev/null @@ -1,8 +0,0 @@ -Export System Design API -======================== - -For detailed methodology, please see -:doc:`Export System Design `. - -.. autoclass:: ORBIT.phases.design.ExportSystemDesign - :members: diff --git a/docs/source/phases/design/api_MonopileDesign.rst b/docs/source/phases/design/api_MonopileDesign.rst deleted file mode 100644 index f8c63f86..00000000 --- a/docs/source/phases/design/api_MonopileDesign.rst +++ /dev/null @@ -1,7 +0,0 @@ -Monopile Design API -=================== - -For detailed methodology, please see :doc:`Monopile Design `. - -.. autoclass:: ORBIT.phases.design.MonopileDesign - :members: diff --git a/docs/source/phases/design/api_MooringSystemDesign.rst b/docs/source/phases/design/api_MooringSystemDesign.rst deleted file mode 100644 index bdc7b7a1..00000000 --- a/docs/source/phases/design/api_MooringSystemDesign.rst +++ /dev/null @@ -1,8 +0,0 @@ -Mooring System Design API -========================= - -For detailed methodology, please see -:doc:`Mooring System Design `. - -.. autoclass:: ORBIT.phases.design.MooringSystemDesign - :members: diff --git a/docs/source/phases/design/api_OffshoreSubstationDesign.rst b/docs/source/phases/design/api_OffshoreSubstationDesign.rst deleted file mode 100644 index 93171c0d..00000000 --- a/docs/source/phases/design/api_OffshoreSubstationDesign.rst +++ /dev/null @@ -1,8 +0,0 @@ -Offshore Substation Design API -============================== - -For detailed methodology, please see -:doc:`Offshore Substation Design `. - -.. autoclass:: ORBIT.phases.design.OffshoreSubstationDesign - :members: diff --git a/docs/source/phases/design/api_ScourProtectionDesign.rst b/docs/source/phases/design/api_ScourProtectionDesign.rst deleted file mode 100644 index 28e0bdcb..00000000 --- a/docs/source/phases/design/api_ScourProtectionDesign.rst +++ /dev/null @@ -1,8 +0,0 @@ -Scour Protection Design API -=========================== - -For detailed methodology, please see -:doc:`Scour Protection Design `. - -.. autoclass:: ORBIT.phases.design.ScourProtectionDesign - :members: diff --git a/docs/source/phases/design/api_SemiSubmersibleDesign.rst b/docs/source/phases/design/api_SemiSubmersibleDesign.rst deleted file mode 100644 index ed3ba2b6..00000000 --- a/docs/source/phases/design/api_SemiSubmersibleDesign.rst +++ /dev/null @@ -1,8 +0,0 @@ -Semi-Submersible Design API -=========================== - -For detailed methodology, please see -:doc:`Semi-Submersible Design `. - -.. autoclass:: ORBIT.phases.design.SemiSubmersibleDesign - :members: diff --git a/docs/source/phases/design/api_SparDesign.rst b/docs/source/phases/design/api_SparDesign.rst deleted file mode 100644 index 21e1593d..00000000 --- a/docs/source/phases/design/api_SparDesign.rst +++ /dev/null @@ -1,8 +0,0 @@ -Spar Design API -=============== - -For detailed methodology, please see -:doc:`Spar Design `. - -.. autoclass:: ORBIT.phases.design.SparDesign - :members: diff --git a/docs/source/phases/design/doc_ArraySystemDesign.rst b/docs/source/phases/design/doc_ArraySystemDesign.rst deleted file mode 100644 index 5509effe..00000000 --- a/docs/source/phases/design/doc_ArraySystemDesign.rst +++ /dev/null @@ -1,148 +0,0 @@ -Array Cabling System Design Methodology -======================================= - -For details of the code implementation, please see -:doc:`Array System Design API `. - -Overview --------- - -Below is an overview of the process used to design an array cable system in ORBIT. -For more details on the helper classes used to support this design please see -:doc:`Cabling Helper Classes `. - -As of the current version of the code there are three array cabling layouts -that can be configured in ORBIT: grid, ring and custom. Figure 1 is an example -of a grid layout featuring 7 "full-strings" and configured distances between -turbines on a string and each row. Figure 2 is an example of a ring layout -where the there is a predetermined distance between the first turbines on a -string and the substation. This figure is also an example of a -"partial string" that is needed to complete the layout. The next sections will -go into more detail of the key steps in building out the array cabling system. - -+------------------------------------------------------------+-----------------------------------------------------------+ -| .. image:: ../../images/examples/full_grid_example.png | .. image:: ../../images/examples/partial_ring_example.png | -+------------------------------------------------------------+-----------------------------------------------------------+ -| Fig 1. Grid layout with no partial strings | Fig 2. Ring layout with 1 partial string | -+------------------------------------------------------------+-----------------------------------------------------------+ - -Number of Strings ------------------ - -In order to create the minimum number of strings required to complete a -"standardized" array cable layout we must first determine how many turbines -can fit on a given set of cable types without overloading them. - -Maximum Turbines per Cable -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The maximum number of turbines that can fit on each cable is determined by -dividing each cable type's power rating by the rated capacity of the turbine -and rounding down to the nearest integer. - -:py:attr:`Cable.max_turbines` = :math:`\lfloor\frac{P}{turbine\_rating}\rfloor`, -where - -| :math:`P` = :py:attr:`Cable.power` -| :py:attr:`turbine_rating` = rated capacity of turbine - -Calculating a Complete String -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The number of turbines that can fit on a string is determined by the user -configured cable types of the system. Starting from the smallest capacity cable -available, turbines are added to the string until that cable's maximum power -capacity is reached. This process is repeated for each of the next largest -capacity cables until all cable types have reached their maximum capacity. The -number of cable sections added to the string in this process represents the -maximum number of cables that can be added to each string. - -.. code-block:: py - :name: string-computation - - # Assume that we are using the Fig 1. example so there can only be 6 - # turbines contained in a single string of cables - max_turbines_per_string = 6 - - # Keeping with the Fig 1. example, assume cable1 is a Cable object that - # represents the "XLPE_400mm_36kV" cable from and cable2 represents the - # "XLPE_630mm_36kV" cable. Note that this is sorted from smallest to largest. - cable_list = [cable1, cable2] - - # Start with an empty string - cable_layout = [] - n = len(cable_layout) - - # Loop through the cables as long as we haven't reached the string maximum - # and there are cables in cable_list - while n < max_turbines_per_string and cables: - cable = cable_list.pop(0) # remove the first cable in the list - - # Ensure that the most turbines in a string is is lower than the - # string maximum and the maximum the individual cable can support, - # then add another cable. - while max_turbines_per_string > n < cable.max_turbines: - cable_layout.append(cable.name) - n = len(cable_layout) - - -After the above calculation is performed, :py:func:`cable_layout` will contain -a list of cable sections starting from the offshore substation and ending at -the last turbine on a string and will look like the following: - -:py:attr:`full_string` = ``["XLPE_630mm_36kV", "XLPE_630mm_36kV", "XLPE_400mm_36kV",`` -``"XLPE_400mm_36kV", "XLPE_400mm_36kV", "XLPE_400mm_36kV"]`` - -In Figure 1, there are 7 of full strings. In the Figure 2 there are 7 full -strings and 1 partial string: - -:py:attr:`partial_string` = ``["XLPE_400mm_36kV", "XLPE_400mm_36kV", "XLPE_400mm_36kV"]`` - -Number of Strings -~~~~~~~~~~~~~~~~~ - -The number of full strings is calculated using the equation below, - -:py:attr:`num_full_strings` = :math:`\lfloor \frac{Plant.num\_turbines}{num\_turbines\_full\_string} \rfloor` - -and the number of partial strings (containing any remaining turbines) is -calculated with the following equation. - -:py:attr:`num_partial_strings` = :math:`Plant.num\_turbines \ \% \ num\_turbines\_full\_string` - -Layouts -------- - -Ring -~~~~ - -For a ring layout, the :py:attr:`substation_distance` is used as the radius of -the first row of turbines, spaced evenly around the ring. Subsequent turbines -on a string are spaced using the :py:attr:`turbine_distance` attribute. An -example of this layout can be seen above in Figure 2. - -Grid -~~~~ - -For the grid layout, an evenly spaced grid of (x, y) coordinates for each -turbine is calculated based off the :py:attr:`turbine_distance`, -:py:attr:`row_distance`, and :py:attr:`substation_distance` with the offshore -substation being located at (0, (:py:attr:`num_strings` - 1) * :py:attr:`row_distance` / :py:attr:`num_strings`) - -Custom -~~~~~~ - -Coming soon! - -Section Lengths ---------------- - -The distance between a turbine and it's subsequent connection determines the -cable length that is required for the array system. These lengths are summed up -and stored in the :py:attr:`design_result`, which can be utilized by the -:doc:`array cable installation module <../install/array/doc_ArrayCableInstall>`. - -Process Diagrams ----------------- - -.. image:: ../../images/process_diagrams/ArraySystemDesign.png diff --git a/docs/source/phases/design/doc_CableHelpers.rst b/docs/source/phases/design/doc_CableHelpers.rst deleted file mode 100644 index 60d1dd99..00000000 --- a/docs/source/phases/design/doc_CableHelpers.rst +++ /dev/null @@ -1,100 +0,0 @@ -Cabling Design Helpers -====================== - -For details of the code implementation, please see the -:doc:`Cabling Helpers API `. - -Overview --------- - -This overview provides the :class:`Cable` class, :class:`Plant` class, and -:class:`CableSystem` parent class. - -Cable ------ - -The cable class calculates a provided cable's power rating for determining the -maximum number of turbines that can be supported by a string of cable. - -Character Impedance (:math:`\Omega`) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. math:: Z_0 = \sqrt{\frac{R + 2 \pi f L}{G + j 2 \pi f C}} - -| :math:`R=` :py:attr:`ac_resistance` -| :math:`j=` the imaginary unit -| :math:`f=` :py:attr:`line_frequency` -| :math:`L=` :py:attr:`inductance` -| :math:`G = \frac{1}{R} =` :py:attr:`conductance` -| :math:`C=` :py:attr:`capacitance` - -Power Factor -~~~~~~~~~~~~ - -.. math:: - |P| &= \cos(\theta) \\ - &= \cos(\arctan(\frac{j Z_0}{Z_0})) - -| :math:`\theta=` the phase angle -| :math:`jZ_0=` the imaginary portion of :py:attr:`character_impedance` -| :math:`Z_0=` the real portion of :py:attr:`character_impedance` - -Cable Power (:math:`MW`) -~~~~~~~~~~~~~~~~~~~~~~~~ - -.. math:: - P = \sqrt{3} * V * I * |P| - -| :math:`V=` :py:attr:`rated_voltage` -| :math:`I=` :py:attr:`current_capacity` -| :math:`|P|=` :py:attr:`power_factor` - -Plant ------ - -Calculates the wind farm specifications to be used for -:doc:`array cable design phase `. The "data class" -accepts either set distances between turbines and rows or calculates them -based off of the number of rotor diameters specified, for example: - -.. code-block:: python - - # First see if there is a distance defined - self.turbine_distance = config["plant"].get("turbine_distance", None) - - # If not, then multiply the rotor diameter by the turbine spacing, - # an integer representation of the number of rotor diameters and covert - # to kilometers - if self.turbine_distance is None: - self.turbine_distance = ( - rotor_diameter * config["plant"]["turbine_spacing"] / 1000.0 - ) - - # Repeat the same process for row distance. - self.row_distance = config["plant"].get("row_distance", None) - if self.row_distance is None: - self.row_distance = ( - rotor_diameter * config["plant"]["row_spacing"] / 1000.0 - ) - -where :py:attr:`config` is the configuration dictionary passed to the -:doc:`array cable design phase ` - -The cable section length for the first turbine in each string is calculated as -the distance to the substation, ``substation_distance``. - -CableSystem ------------ - -:py:class:`CableSystem` acts as the parent class for both -:py:class:`ArrayDesignSystem` and :py:class:`ExportDesignSystem`. As such, it -is not intended to be invoked on its own, however it provides the shared -frameworks for both cabling system. - -.. note:: - - :py:class:`CableSystem` offers the cabling initialization and most of - the output properties such as :py:attr:`cable_lengths_by_type`, - :py:attr:`total_cable_lengths_by_type`, :py:attr:`cost_by_type`, - :py:attr:`total_phase_cost`, :py:attr:`total_phase_time`, - :py:attr:`detailed_output`, and most importantly :py:attr:`design_result`. diff --git a/docs/source/phases/design/doc_ExportSystemDesign.rst b/docs/source/phases/design/doc_ExportSystemDesign.rst deleted file mode 100644 index db47d957..00000000 --- a/docs/source/phases/design/doc_ExportSystemDesign.rst +++ /dev/null @@ -1,47 +0,0 @@ -Export System Design Methodology -================================ - -For details of the code implementation, please see -:doc:`Export System Design API `. - -Overview --------- - -Below is an overview of the process used to design an export cable system in -ORBIT. For more detail on the helper classes used to support this design please -see :doc:`Cabling Helper Classes `, specifically -:class:`Cable` and :class:`CableSystem`. - -Number of Required Cables -------------------------- - -The number of export cables required is calculated by dividing the windfarm's -capacity by the configured export cable's power rating and adding any user -defined redundnacy as seen below. - -:math:`num\_cables = \lceil\frac{plant\_capacity}{cable\_power}\rceil + num\_redundant` - -Export Cable length -------------------- - -The total length of the export cables is calculated as the sum of the site -depth, distance to landfall and distance to interconnection multiplied by the -user defined :py:attr`percent_added_length` to account for any exclusions or -geotechnical design considerations that make a straight line cable route -impractical. - -:math:`length = (d + distance_\text{landfall} + distance_\text{interconnection} * (1 + length_\text{percent_added})` - -Design Result -------------- - -The result of this design module (:py:attr:`design_result`) is a list of cable -sections and their lengths and masses that represent the export cable system. -This result can then be passed to the -:doc:`export cable installation module <../install/export/doc_ExportCableInstall>` -to simulate the installation of the system. - -Process Diagrams ----------------- - -.. image:: ../../images/process_diagrams/ExportSystemDesign.png diff --git a/docs/source/phases/design/doc_MonopileDesign.rst b/docs/source/phases/design/doc_MonopileDesign.rst deleted file mode 100644 index 9be757b6..00000000 --- a/docs/source/phases/design/doc_MonopileDesign.rst +++ /dev/null @@ -1,51 +0,0 @@ -Monopile Design Methodology -=========================== - -For details of the code implementation, please see -:doc:`Monopile Design API `. - -Overview --------- - -This module is based on initial pile dimension calculations from Arany (2017) -[#arany2017]_. Pile dimensions are chosen to withstand the bending moment from -the 50-year Extreme Operation Gust (EOG). This corresponds to wind scenario -U-3 in Section 2.2.1. This module is not intended to capture the complexities -of a full engineering design study for monopiles, but rather broadly capture -the scaling trends due to increased site depth, turbine size and material -parameters. - -The 50-year extreme wind speed can be calculated using the following cumulative -density function. - -:math:`U_{10,50-year}=K(-\ln(1-0.98^\frac{1}{52596}))^\frac{1}{S}` - -where :math:`K` and :math:`S` are the Weibull scale and shape factors -respectively. - -The mudline bending moment is calculated as: - -:math:`M_{wind,EOG} = \gamma_LF_{wind,EOG}(S + z_{hub})` - -where :math:`\gamma_L` is the load factor (defaults to 1.35), -:math:`F_{wind,EOG}` is the total wind load on the turbine, :math:`S` is the -water depth at site and :math:`z_{hub}` is the hub height of the turbine. The -derivation of :math:`F_{wind,EOG}` can be seen in detail in the -`ORBIT technical documentation `_. - -Initial pile dimensions are then calculated using Arany (2017) [#arany2017]_, -API (2005) [#api2005]_, and Poulos and Davis (1980) [#PoulosDavis1980]_. - -References ----------- - -.. [#arany2017] Laszlo Arany, S. Bhattacharya, John Macdonald, - S.J. Hogan, Design of monopiles for offshore wind turbines in 10 - steps, Soil Dynamics and Earthquake Engineering, - Volume 92, 2017, Pages 126-152, ISSN 0267-7261, - -.. [#api2005] API, 2005, Recommended Practice for Planning, Designing and - Constructing Fixed Offshore Platforms - Working Stress Design, Pages 68-71 - -.. [#PoulosDavis1980] Poulos and Davis, 1980, Pile foundation analysis and - design. Rainbow-Bridge Book Co. diff --git a/docs/source/phases/design/doc_MooringSystemDesign.rst b/docs/source/phases/design/doc_MooringSystemDesign.rst deleted file mode 100644 index eae92c9a..00000000 --- a/docs/source/phases/design/doc_MooringSystemDesign.rst +++ /dev/null @@ -1,18 +0,0 @@ -Mooring System Design Methodology -================================= - -For details of the code implementation, please see -:doc:`Mooring System Design API `. - -Overview --------- - -The mooring system design module in ORBIT is based on previous modeling -efforts undertaken by NREL, [#maness2017]_. The technical documentation for -this tool can be found `here _`. - -References ----------- - -.. [#maness2017] Michael Maness, Benjamin Maples, Aaron Smith, - NREL Offshore Balance-of-System Model, 2017 diff --git a/docs/source/phases/design/doc_OffshoreSubstationDesign.rst b/docs/source/phases/design/doc_OffshoreSubstationDesign.rst deleted file mode 100644 index ef309b47..00000000 --- a/docs/source/phases/design/doc_OffshoreSubstationDesign.rst +++ /dev/null @@ -1,19 +0,0 @@ -Offshore Substation Design Methodology -====================================== - -For details of the code implementation, please see -:doc:`Offshore Substation Design API `. - -Overview --------- - -The offshore substation design module in ORBIT is based on previous modeling -efforts undertaken by NREL, [#maness2017]_. A detailed description of the -model and its functionality can be found in the ORBIT technical -documentation (Coming soon!). - -References ----------- - -.. [#maness2017] Michael Maness, Benjamin Maples, Aaron Smith, - NREL Offshore Balance-of-System Model, 2017. https://www.nrel.gov/docs/fy17osti/66874.pdf diff --git a/docs/source/phases/design/doc_ScourProtectionDesign.rst b/docs/source/phases/design/doc_ScourProtectionDesign.rst deleted file mode 100644 index 449a81d9..00000000 --- a/docs/source/phases/design/doc_ScourProtectionDesign.rst +++ /dev/null @@ -1,76 +0,0 @@ -Scour Protection Design Methodology -=================================== - -For details of the code implementation, please see -:doc:`Scour Protection Design API `. - -Scour Protection Design ------------------------ - -This module calculates the required scour protection material for a fixed -substructure to avoid seabed erosion around the installation. It is based on -a DNV GL `standard `_ -and geometric calculations. - -The potential depth of the a free forming scour pit is calculated using a -simplified version of the relationship presented in the DNV GL report (equation -J.5): - -:math:`\frac{S}{D} = 1.3` - -where :math:`S` is the calculated depth of the scour pit and :math:`D` is the -overall diameter of the substructure. The default value (1.3) is a conservative -assumption and may be overridden by the user as follows: - -.. code-block:: python - - config = { - ... - - "scour_protection_design": { - "scour_depth_equilibrium": 1.2 - } - - ... - } - -The radius of the scour pit is then calculated using the soil friction -angle (:math:`\phi`) and a simple geometric relationship: - -:math:`r = \frac{D}{2} + \frac{S}{tan(\phi)}` - -The default value for :math:`\phi` is 33.5deg, representing the soil -friction angle for medium density sand. The total volume of scour -protection material is then calculated as follows, - -:math:`V = \pi * t * r^2` - -where :math:`t` represents the depth of the scour protection material. This -value defaults to 1m in the code, which represents an appropriate initial -assumption and not a complete design. For sites that exhibit greater seafloor -currents, the scour protection layer may be as thick as 2m, whereas calmer -sites may only need 0.3-0.5m of material. In the abscense of a geotechnical -study, this value is difficult to calculate and is instead presented to user as -a configurable input so the cost impacts of different thicknesses can be -investigated. - -Terms: - * :math:`S =` Scour depth - * :math:`D =` Monopile diameter - * :math:`r =` Radius of scour protection from the center of the monopile - * :math:`\phi =` Soil friction angle - -Default Assumptions: - * :math:`\frac{S}{D} = 1.3` - * :math:`\phi = 33.5` - * Angle for medium density sand - -References ----------- -.. [1] Det Norske Veritas AS. (2014, May). Design of Offshore Wind Turbine - Structures. Retrieved from - https://rules.dnvgl.com/docs/pdf/DNV/codes/docs/2014-05/Os-J101.pdf - -.. [2] Draft Construction and Operations Plan for Vineyard Wind Project Appendices. - Retrieved from - https://www.boem.gov/sites/default/files/renewable-energy-program/State-Activities/MA/Vineyard-Wind/Vineyard-Wind-COP-Volume-III-Appendix-III-K.pdf diff --git a/docs/source/phases/design/doc_SemiSubmersibleDesign.rst b/docs/source/phases/design/doc_SemiSubmersibleDesign.rst deleted file mode 100644 index cd9ed924..00000000 --- a/docs/source/phases/design/doc_SemiSubmersibleDesign.rst +++ /dev/null @@ -1,18 +0,0 @@ -Semi-Submersible Design Methodology -=================================== - -For details of the code implementation, please see -:doc:`Semi-Submersible Design API `. - -Overview --------- - -The semi-submersible design module in ORBIT is based on previous modeling -efforts undertaken by NREL, [#maness2017]_. The technical documentation for -this tool can be found `here _`. - -References ----------- - -.. [#maness2017] Michael Maness, Benjamin Maples, Aaron Smith, - NREL Offshore Balance-of-System Model, 2017 diff --git a/docs/source/phases/design/doc_SparDesign.rst b/docs/source/phases/design/doc_SparDesign.rst deleted file mode 100644 index 77f00834..00000000 --- a/docs/source/phases/design/doc_SparDesign.rst +++ /dev/null @@ -1,18 +0,0 @@ -Spar Design Methodology -======================= - -For details of the code implementation, please see -:doc:`Spar Design API `. - -Overview --------- - -The spar design module in ORBIT is based on previous modeling efforts -undertaken by NREL, [#maness2017]_. The technical documentation for -this tool can be found `here _`. - -References ----------- - -.. [#maness2017] Michael Maness, Benjamin Maples, Aaron Smith, - NREL Offshore Balance-of-System Model, 2017 diff --git a/docs/source/phases/install/array/api_ArrayCableInstall.rst b/docs/source/phases/install/array/api_ArrayCableInstall.rst deleted file mode 100644 index 7d03933c..00000000 --- a/docs/source/phases/install/array/api_ArrayCableInstall.rst +++ /dev/null @@ -1,8 +0,0 @@ -Array Cabling System Installation API -===================================== - -For detailed methodology, please see -:doc:`Array Cabling Installation Methodology `. - -.. autoclass:: ORBIT.phases.install.ArrayCableInstallation - :members: diff --git a/docs/source/phases/install/array/doc_ArrayCableInstall.rst b/docs/source/phases/install/array/doc_ArrayCableInstall.rst deleted file mode 100644 index 31e54574..00000000 --- a/docs/source/phases/install/array/doc_ArrayCableInstall.rst +++ /dev/null @@ -1,138 +0,0 @@ -Array Cabling System Installation Methodology -============================================= - -For details of the code implementation, please see -:doc:`Array Cable Installation API `. - -Overview --------- - -The ``ArrayCableInstallation`` module simulates the installation of array cable -sections between turbines and the offshore substation at site. This process is -one of the critical installation phases in the construction of a wind farm as -testing and final commissioning of the turbines can't occur until it is -complete. - -The installation of cables offshore is a complex process that depends on the -geotechnical parameters of the seabed along the cable route. Detailed -geotechnical data is typically not publicly available for all potential -offshore wind sites in the U.S., and as such, ORBIT was designed to allow a -user to investigate the impact of seabed conditions indirectly without -requiring detailed descriptions of the seabed. This is primarily done by -adjusting the cable burial speed, with harder or rockier soils requiring a -slower (and ultimately more expensive) burial speed. - -Input Structure ---------------- - -The design of the input data structure for this module allows the user to -define site specific array cable configurations. For each cable type, a list -of cable sections can be defined. The installation vessel will install each -section individually and the time to complete this operation is dynamic based -on the length and the linear density of the cable, site depth, etc. A user can -also define multiple cable types that comprise an individual string of -turbines. - -For example, - -.. code-block:: - - { - 'array_system': { - 'cables': {'XLPE_400mm_33kV': { - 'cable_sections': [ - (1.7958701547, 2), # There are two 1.79km sections, - (1.118, 16), # 16 1.118km sections - (1.2128290583, 2) # and two 1.213km sections - ], - 'linear_density': 35 - } - } - } - -The installation of each section above will be modeled seperately. In the above -example, only one cable was used, though there could be additional defined -cables (with their own `cable_sections` key). - -.. note:: - - The above data structure can be input directly by the user, or can be a - result of running the ``ArraySystemDesign`` module. - -.. _cable_strategies: - -Configuration -------------- - -ORBIT considers two installation strategies: a simultaneous lay/bury operation -using modern cable installation vessels and a seperated operation where one -vessel lays the cable and another follows behind to bury it. A detailed -description of the applicability of each strategy is covered in the ORBIT -technical `report `_. - -If the config contains the key ``'array_cable_bury_vessel'`` the separate -strategy will be used. If this key is not present, the -``'array_cable_install_vessel'`` will perform a simultaneous lay/bury of the -cable. - -The key ``'array_cable_trench_vessel'`` is an optional configuration that will -simulate a separate trenching operation along all cable routes completed by -the above vessel. If this key is not present, this operation will not be -modeled. - -.. code-block:: - - { - 'array_cable_install_vessel': 'example_vessel' - # 'array_cable_bury_vessel': 'example_vessel', - # 'array_cable_trench_vessel': 'example_vessel', - 'array_system': { - 'cables': {'XLPE_400mm_33kV': { - ... - } - } - } - -Processes ---------- - -The speed at which a vessel can perform the operations of each installation -strategy is determined by the vessel properties, passed kwargs or the -default speed for the process. This is the primary mechanism that a user can -adjust cable installation times for different seabed conditions. - -+------------------+--------------------------+------------+ -| Strategy | Key | Default | -+==================+==========================+============+ -| Lay/Bury Cable | ``cable_lay_bury_speed`` | 0.3 km/hr | -+------------------+--------------------------+------------+ -| Lay Cable | ``cable_lay_speed`` | 1 km/hr | -+------------------+--------------------------+------------+ -| Bury Cable | ``cable_bury_speed`` | 0.5 km/hr | -+------------------+--------------------------+------------+ - -Other operations in the installation process are determined by default values: - -+-----------------+----------------------------+---------+ -| Process | Key | Default | -+=================+============================+=========+ -| Position Onsite | ``site_position_time`` | 2h | -+-----------------+----------------------------+---------+ -| Prepare Cable | ``cable_prep_time`` | 1h | -+-----------------+----------------------------+---------+ -| Lower Cable | ``cable_lower_time`` | 1h | -+-----------------+----------------------------+---------+ -| Pull in Cable | ``cable_pull_in_time`` | 5.5h | -+-----------------+----------------------------+---------+ -| Test Cable | ``cable_termination_time`` | 5.5h | -+-----------------+----------------------------+---------+ - -Configuration Examples -~~~~~~~~~~~~~~~~~~~~~~ - -Coming soon! - -Process Diagrams ----------------- - -.. image:: ../../../images/process_diagrams/ArrayCableInstall.png diff --git a/docs/source/phases/install/export/api_ExportCableInstall.rst b/docs/source/phases/install/export/api_ExportCableInstall.rst deleted file mode 100644 index e2795466..00000000 --- a/docs/source/phases/install/export/api_ExportCableInstall.rst +++ /dev/null @@ -1,8 +0,0 @@ -Export Cabling System Installation API -====================================== - -For detailed methodology, please see -:doc:`Export Cable Installation Methodology `. - -.. autoclass:: ORBIT.phases.install.ExportCableInstallation - :members: diff --git a/docs/source/phases/install/export/doc_ExportCableInstall.rst b/docs/source/phases/install/export/doc_ExportCableInstall.rst deleted file mode 100644 index 18cc5601..00000000 --- a/docs/source/phases/install/export/doc_ExportCableInstall.rst +++ /dev/null @@ -1,105 +0,0 @@ -Export Cabling System Installation Methodology -============================================== - -For details of the code implementation, please see -:doc:`Export Cable Installation API `. - -Overview --------- - -The ``ExportCableInstallation`` module simulates the installation of the cables -between the offshore substation and the grid connection. Many processes in this -installation phase are similar to those in the array cable installation module, -however the cable distances and process times are often much greater. - -Input Structure ---------------- - -The design of the input data structure for this module is the same as the array -cable installation module. An example export system can be seen in the code -block below. - -.. code-block:: - - { - 'export_system': { - 'cables': {'XLPE_500mm_33kV': { - 'cable_sections': [ - (35, 2), # There are two 35km export cables to install - ], - 'linear_density': 35 - } - } - } - -.. note:: - - The above data structure can be input directly by the user, or can be a - result of running the ``ExportSystemDesign`` module. - -Configuration -------------- - -ORBIT considers the same possible installation strategies for array and export -cable systems. Please see this :ref:`section ` for the -different installation methods available. - -Processes ---------- - -Onshore Processes -~~~~~~~~~~~~~~~~~ - -At landfall, the cable must be pulled up onto shore, tested and terminated at -the grid connection point and buried. These processes utilize the following -inputs. - -+---------------+----------------------------+-----------+ -| Process | Key | Default | -+===============+============================+===========+ -| Dig Trench | ``trench_dig_speed`` | 0.1 km/hr | -+---------------+----------------------------+-----------+ -| Tow Plow | ``tow_plow_speed`` | 5 km/hr | -+---------------+----------------------------+-----------+ -| Pull in Winch | ``pull_winch_speed`` | 5 km/hr | -+---------------+----------------------------+-----------+ -| Pull in Cable | ``cable_pull_in_time`` | 5.5h | -+---------------+----------------------------+-----------+ -| Test Cable | ``cable_termination_time`` | 5.5h | -+---------------+----------------------------+-----------+ - -A detailed description of these processes is provided in the ORBIT technical -documentation. - -Offshore Processes -~~~~~~~~~~~~~~~~~~ - -The offshore installation process utilize the same configurable speeds as the -array system installation: - -+------------------+--------------------------+------------+ -| Strategy | Key | Default | -+==================+==========================+============+ -| Lay/Bury Cable | ``cable_lay_bury_speed`` | 0.3 km/hr | -+------------------+--------------------------+------------+ -| Lay Cable | ``cable_lay_speed`` | 1 km/hr | -+------------------+--------------------------+------------+ -| Bury Cable | ``cable_bury_speed`` | 0.5 km/hr | -+------------------+--------------------------+------------+ - -.. note:: - - The cable lengths for an export system are typically much longer than the - array system and thhere is the possibility that a cable splice will be - needed. The time for splicing a cable can be configured by the user using - the ``cable_splice_time`` key, which defaults to 48h. - -Configuration Examples -~~~~~~~~~~~~~~~~~~~~~~ - -Coming soon! - -Process Diagrams ----------------- - -.. image:: ../../../images/process_diagrams/ExportCableInstall.png diff --git a/docs/source/phases/install/jacket/api_JacketInstall.rst b/docs/source/phases/install/jacket/api_JacketInstall.rst deleted file mode 100644 index cf59fab7..00000000 --- a/docs/source/phases/install/jacket/api_JacketInstall.rst +++ /dev/null @@ -1,10 +0,0 @@ -Jacket Installation API -======================= - -For detailed methodology, please see -:doc:`Jacket Installation Methodology `. - -Coming soon! - -.. .. autoclass:: ORBIT.phases.install.SingleWtivJacketInstall -.. :members: diff --git a/docs/source/phases/install/jacket/doc_JacketInstall.rst b/docs/source/phases/install/jacket/doc_JacketInstall.rst deleted file mode 100644 index b30798b4..00000000 --- a/docs/source/phases/install/jacket/doc_JacketInstall.rst +++ /dev/null @@ -1,10 +0,0 @@ -Jacket Installation Methodology -=============================== - -For details of the code implementation, please see -:doc:`Jacket Installation API `. - -Overview --------- - -Coming soon! diff --git a/docs/source/phases/install/monopile/api_MonopileInstallation.rst b/docs/source/phases/install/monopile/api_MonopileInstallation.rst deleted file mode 100644 index ca207640..00000000 --- a/docs/source/phases/install/monopile/api_MonopileInstallation.rst +++ /dev/null @@ -1,18 +0,0 @@ -Monopile Installation API -========================= - -For detailed methodology, please see -:doc:`Monopile Installation Methodology `. - -.. automodule:: ORBIT.phases.install.monopile_install.standard - :members: - :exclude-members: extract_vessel_specs - -.. automodule:: ORBIT.phases.install.monopile_install._common - :members: - -.. automodule:: ORBIT.phases.install.monopile_install._single_wtiv - :members: - -.. automodule:: ORBIT.phases.install.monopile_install._wtiv_with_feeders - :members: diff --git a/docs/source/phases/install/monopile/doc_MonopileInstall.rst b/docs/source/phases/install/monopile/doc_MonopileInstall.rst deleted file mode 100644 index d2a02250..00000000 --- a/docs/source/phases/install/monopile/doc_MonopileInstall.rst +++ /dev/null @@ -1,189 +0,0 @@ -Monopile Installation Methodology -================================= - -For details of the code implementation, please see -:doc:`Monopile Installation API `. - -Overview --------- - -The ``MonopileInstallation`` module simulates the installation of monopile -substructures at site. Monopile substructures encompass the monopile itself, a -large steel cylindrical pile that is driven into the seabed, and a steel -transition piece that is placed on top of the pile to provide a level -attachment point for the turbine. The module can be configured such that a -single wind turbine installation vessel (WTIV) transports and installs all of -the substructure components or it can be configured to include feeder barges -that transport the components to site. Process diagrams detailing the vessel -logistics for these two installation strategies can be seen below. - -.. note:: - - For both installation strategies the WTIV performs all of the on site - operations with its onboard crane, either picking components from its own - deck or a neighboring feeder barge. - -Configuration -------------- - -To configure ``MonopileInstallation`` to utilize feeder barges for transit of -the monopile components from port, add the following configuration to the -project configuration. - -.. code-block:: python - - config = { - ... - - "feeder": "example_feeder", # name of vessel configuration file without extension - "num_feeders": 2, - - ... - } - -Processes ---------- - -Port Operations -~~~~~~~~~~~~~~~ - -Vessels load and fasten items on their deck at port using the port crane. -Ports are configured with one crane by default, which limits multiple vessels -from accessing port resources at a time. This can be overridden by configuring -a port with additional cranes in a project configuration: - -.. code-block:: python - - "port": { - "num_cranes": 2 # Two vessels can access port resource simultaneously. - } - -The default times for fastening each component to deck are listed below. - -+------------------+----------------------+------------+ -| Component | Inputs | Default | -+==================+======================+============+ -| Monopile | ``mono_fasten_time`` | 12h | -+------------------+----------------------+------------+ -| Transition Piece | ``tp_fasten_time`` | 8h | -+------------------+----------------------+------------+ - -Currently, all vessels are only able to load multiples of complete sets of -components (monopile and transition piece). - -Site Preperation -~~~~~~~~~~~~~~~~ - -Once the WTIV and a set of components are at site (either stored on the WTIV or -a feeder barge), the WTIV initiates site preperation. The WTIV positions itself -onsite and jacks up. If installing a monopile, the WTIV surveys the seabed with -an ROV. The following table outlines the inputs and default times for these -tasks. - -+-----------------+--------------------------+------------+ -| Action | Inputs | Default | -+=================+==========================+============+ -| Position Onsite | ``site_position_time`` | 2h | -+-----------------+--------------------------+------------+ -| Jack-up | | ``depth, extension`` | calculated | -| | | ``speed_above_depth`` | | -| | | ``speed_below_depth`` | | -+-----------------+--------------------------+------------+ -| ROV Survey | ``rov_survey_time`` | 1h | -+-----------------+--------------------------+------------+ - -Monopile Installation -~~~~~~~~~~~~~~~~~~~~~ - -After site preperation is complete, the WTIV then releases the monopile from -the fastenings (either on its own deck or neighboring feeder barge). When the -substructure is released, it is upended using the WTIV crane and lowered to the -seabed. The crane is then equiped with the driving equipment, and the monopile -is driven into the seabed. ORBIT currently only supports simple drive -logistics, but a drive-drill-drive installation strategy is planned for future -version. Inputs and process times are summarized in the following table. - -+------------------+--------------------------+------------+ -| Action | Inputs | Default | -+==================+==========================+============+ -| Release Monopile | ``mono_release_time`` | 3 | -+------------------+--------------------------+------------+ -| Upend Monopile | | ``monopile.length`` | calculated | -| | | ``crane_rate`` | | -| | | ``wave_height`` | | -+------------------+--------------------------+------------+ -| Lower Monopile | | ``site_depth`` | calculated | -| | | ``crane_rate`` | | -+------------------+--------------------------+------------+ -| Reequip Crane | ``crane_reequip_time`` | 1h | -+------------------+--------------------------+------------+ -| Drive Monopile | | ``mono_drive_rate`` | calculated | -| | | ``mono_embed_len`` | | -+------------------+--------------------------+------------+ - -Transition Piece Installation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -After the monopile is installed, the WTIV lowers and attaches the -transition piece onto the top of the monopile using the following processes: - -+------------------+--------------------------+------------+ -| Action | Inputs | Default | -+==================+==========================+============+ -| Reequip Crane | ``crane_reequip_time`` | 1h | -+------------------+--------------------------+------------+ -| Lower TP | | ``air_gap`` | calculated | -| | | ``crane_rate`` | | -| | | ``wave_height`` | | -+------------------+--------------------------+------------+ - -The transition piece can be attached with either a bolted or a grouted -connection. The bolted connection is selected by default. To configure the WTIV -to use a grouted connection, pass ``tp_connection_type="grouted"`` into the -installation module. - -For bolted connections, the WTIV performs these tasks: - -+------------------+--------------------------+------------+ -| Action | Inputs | Default | -+==================+==========================+============+ -| Bolt TP | ``tp_bolt_time`` | 4h | -+------------------+--------------------------+------------+ -| Jack-down | | ``depth, extension`` | calculated | -| | | ``speed_above_depth`` | | -| | | ``speed_below_depth`` | | -+------------------+--------------------------+------------+ - -For grouted connections, the WTIV performs these tasks: - -+------------------+--------------------------+------------+ -| Action | Inputs | Default | -+==================+==========================+============+ -| Pump Grout | ``grout_pump_time`` | 2h | -+------------------+--------------------------+------------+ -| Cure Grout | ``grout_cure_time`` | 24h | -+------------------+--------------------------+------------+ -| Jack-down | | ``depth, extension`` | calculated | -| | | ``speed_above_depth`` | | -| | | ``speed_below_depth`` | | -+------------------+--------------------------+------------+ - -Process Diagrams ----------------- - -Single WTIV Installation -~~~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: ../../../images/process_diagrams/monopile_single_wtiv.png - -.. _monopile_install_feeders: - -WTIV with Feeder Barges Installation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: ../../../images/process_diagrams/monopile_wtiv_with_feeders.png - -Component Installation -~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: ../../../images/process_diagrams/monopile_install.png diff --git a/docs/source/phases/install/mooring/api_MooringSystemInstallation.rst b/docs/source/phases/install/mooring/api_MooringSystemInstallation.rst deleted file mode 100644 index 2f2295a5..00000000 --- a/docs/source/phases/install/mooring/api_MooringSystemInstallation.rst +++ /dev/null @@ -1,8 +0,0 @@ -Mooring System Installation API -=============================== - -For detailed methodology, please see -:doc:`Mooring System Installation Methodology `. - -.. autoclass:: ORBIT.phases.install.MooringSystemInstallation - :members: diff --git a/docs/source/phases/install/mooring/doc_MooringSystemInstallation.rst b/docs/source/phases/install/mooring/doc_MooringSystemInstallation.rst deleted file mode 100644 index bd04b81d..00000000 --- a/docs/source/phases/install/mooring/doc_MooringSystemInstallation.rst +++ /dev/null @@ -1,59 +0,0 @@ -Mooring System Installation Methodology -======================================= - -For details of the code implementation, please see -:doc:`Mooring System Installation API `. - -Overview --------- - -The ``MooringSystemInstallation`` module simulates the installation of mooring -lines and anchors at site for a floating offshore wind project. The mooring -system installation is simulated using a multi-purpose support vessel that -transports the components to site and performs the onsite installation -procedures. - -Configuration -------------- - -The primary configuration parameters available for this module are the -installation vessel and the mooring system configuration. An example of these -parameters is presented below. - -.. code-block:: python - - config = { - - - "mooring_install_vessel": "example_support_vessel", - "mooring_system": { - "num_lines": 4, # per substructure - "line_mass": 500, # t - "anchor_mass": 500, # t - "anchor_type": "Drag Embedment", # or "Suction Pile" - } - ... - } - -Processes ---------- - -The default times associated with the installation procedure are listed in the -table below. - -+---------------------------+---------------------------------+--------------+ -| Process | Inputs | Default | -+===========================+=================================+==============+ -| Loadout | ``mooring_system_load_time`` | 5h | -+---------------------------+---------------------------------+--------------+ -| Transit | ``vessel.transit_speed`` | calculated | -+---------------------------+---------------------------------+--------------+ -| Survey | ``mooring_site_survey_time`` | 3h | -+---------------------------+---------------------------------+--------------+ -| Install Anchor (repeated) | | ``suction_pile_install_time`` | calculated | -| | | ``drag_embed_install_time`` | | -+---------------------------+---------------------------------+--------------+ -| Install Line (repeated) | NA | calculated | -+---------------------------+---------------------------------+--------------+ -| Transit | ``vessel.transit_speed`` | calculated | -+---------------------------+---------------------------------+--------------+ diff --git a/docs/source/phases/install/oss/api_OffshoreSubstationInstall.rst b/docs/source/phases/install/oss/api_OffshoreSubstationInstall.rst deleted file mode 100644 index 6ab5a93d..00000000 --- a/docs/source/phases/install/oss/api_OffshoreSubstationInstall.rst +++ /dev/null @@ -1,8 +0,0 @@ -Offshore Substation Installation API -==================================== - -For detailed methodology, please see -:doc:`Offshore Substation Installation Methodology `. - -.. autoclass:: ORBIT.phases.install.OffshoreSubstationInstallation - :members: diff --git a/docs/source/phases/install/oss/doc_OffshoreSubstationInstall.rst b/docs/source/phases/install/oss/doc_OffshoreSubstationInstall.rst deleted file mode 100644 index 492a1b07..00000000 --- a/docs/source/phases/install/oss/doc_OffshoreSubstationInstall.rst +++ /dev/null @@ -1,78 +0,0 @@ -Offshore Substation Installation Methodology -============================================ - -For details of the code implementation, please see -:doc:`Offshore Substation Installation API `. - -Overview --------- - -The ``OffshoreSubstationInstallation`` module simulates the installation of -offshore substations and their associated substructures. ORBIT currently only -considers monopile substructures, though future release will extend this module -to include an option for jacket substructures. The installation of the -substructure and substation topside is completed with an installation vessel -while components are delivered to site using a feeder barge. - -.. The :ref:`process-diagram` outlining the vessel logistics involved can be seen below. - -Processes ---------- - -Port Operations -~~~~~~~~~~~~~~~ - -The feeder barge loads and fastens components on deck at port using the port -crane. The default times for fastening each component to deck are listed below. - -+-----------+-------------------------+---------+ -| Component | Inputs | Default | -+===========+=========================+=========+ -| Monopile | ``mono_fasten_time`` | 12h | -+-----------+-------------------------+---------+ -| Topside | ``topside_fasten_time`` | 2h | -+-----------+-------------------------+---------+ - -Currently, all vessels are only able to load multiples of complete sets of -components (monopile and topside). - -Monopile Installation -~~~~~~~~~~~~~~~~~~~~~ - -Monopile substructures are installed using the installation processes outlined -in the ``MonopileInstallation`` module (process -:ref:`diagram ` with feeder barges). - -Topside Installation -~~~~~~~~~~~~~~~~~~~~ - -Once the monopile is installed on-site, the topside can be released from deck -storage and attached to the substructure. The following processes outline the -code the processes the installation vessel takes to complete this task: - -+-----------------+--------------------------+------------+ -| Action | Inputs | Default | -+=================+==========================+============+ -| Reequip Crane | ``crane_reequip_time`` | 1h | -+-----------------+--------------------------+------------+ -| Release Topside | ``topside_release_time`` | 2h | -+-----------------+--------------------------+------------+ -| Lift Topside | | ``wtiv.crane_rate`` | calculated | -| | | ``wave_height`` | | -+-----------------+--------------------------+------------+ -| Pump Grout | ``grout_pump_time`` | 2h | -+-----------------+--------------------------+------------+ -| Cure Grout | ``grout_cure_time`` | 24h | -+-----------------+--------------------------+------------+ -| Jack-down | | ``depth, extension`` | calculated | -| | | ``speed_above_depth`` | | -| | | ``speed_below_depth`` | | -+-----------------+--------------------------+------------+ - -.. Process Diagram -.. ---------------- - -.. Offshore Substation Installation -.. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. Coming soon! diff --git a/docs/source/phases/install/quayside_towout/api_GravityBasedInstallation.rst b/docs/source/phases/install/quayside_towout/api_GravityBasedInstallation.rst deleted file mode 100644 index 283eda91..00000000 --- a/docs/source/phases/install/quayside_towout/api_GravityBasedInstallation.rst +++ /dev/null @@ -1,8 +0,0 @@ -Gravity-Based Foundation Installation API -========================================= - -For detailed methodology, please see -:doc:`Gravity-Based Foundation Installation Methodology `. - -.. autoclass:: ORBIT.phases.install.GravityBasedInstallation - :members: diff --git a/docs/source/phases/install/quayside_towout/api_MooredSubInstallation.rst b/docs/source/phases/install/quayside_towout/api_MooredSubInstallation.rst deleted file mode 100644 index 7c7a5f0f..00000000 --- a/docs/source/phases/install/quayside_towout/api_MooredSubInstallation.rst +++ /dev/null @@ -1,8 +0,0 @@ -Moored Substructure Installation API -==================================== - -For detailed methodology, please see -:doc:`Moored Substructure Installation Methodology `. - -.. autoclass:: ORBIT.phases.install.MooredSubInstallation - :members: diff --git a/docs/source/phases/install/quayside_towout/doc_GravityBasedInstallation.rst b/docs/source/phases/install/quayside_towout/doc_GravityBasedInstallation.rst deleted file mode 100644 index 070e1bdb..00000000 --- a/docs/source/phases/install/quayside_towout/doc_GravityBasedInstallation.rst +++ /dev/null @@ -1,10 +0,0 @@ -Gravity-Based Foundation Installation Methodology -================================================= - -For details of the code implementation, please see -:doc:`Moored Substructure Installation API `. - -Overview --------- - -This module will be expanded in a future release. diff --git a/docs/source/phases/install/quayside_towout/doc_MooredSubInstallation.rst b/docs/source/phases/install/quayside_towout/doc_MooredSubInstallation.rst deleted file mode 100644 index 3838bca7..00000000 --- a/docs/source/phases/install/quayside_towout/doc_MooredSubInstallation.rst +++ /dev/null @@ -1,94 +0,0 @@ -Moored Substructure Installation Methodology -============================================ - -For details of the code implementation, please see -:doc:`Moored Substructure Installation API `. - -Overview --------- - -The ``MooredSubInstallation`` module simulates the manufacture and installation -of moored substuctures for a floating offshore wind project. The installation -procedures include the time required to manufacture a substructure at quayside, -assemble a turbine on the substructure, ballast the completed assembly, tow -the completed assembly to site and hook up the pre-installed moooring lines. - -Configuration -------------- - -The primary configuration parameters available for this module are related to -the quayside assembly process and the vessels used to tow the completed -assemblies to site and complete the installation. The code block highlights -the key parameters available. - -.. code-block:: python - - config = { - - ... - - "support_vessel": "example_support_vessel", # Will perform onsite installation procedures. - "ahts_vessel": "example_ahts_vessel", # Anchor handling tug supply vessel associated with each tow group. - "towing_vessel": "example_towing_vessel", # Towing groups will contain multiple of this vessel. - "towing_groups": { - "towing_vessel": 1, # Vessels used to tow the substructure to site. - "station_keeping_vessels": 3, # Vessels used for station keeping during mooring line hookups. - "num_groups": 1 # Number of independent groups. Optional, defualt: 1. - }, - - "port": { - "sub_assembly_lines": 2, # Independent substructure assembly lines. - "sub_storage": 8, # Available storage berths at port for completed substructures. - "turbine_assembly_cranes": 2, # Independent turbine assembly cranes. - "assembly_storage": 8, # Available storage berths at port for completed turbine/substructure assemblies. - }, - - "substructure": { - "takt_time": 168, # h, time to manufacture one substructure. - "towing_speed": 6, # km/h. - }, - - ... - } - - -Processes ---------- - -Quayside Assembly -~~~~~~~~~~~~~~~~~ - -+-------------------------------------------+---------+ -| Process | Default | -+===========================================+=========+ -| Substructure Assembly | 168h | -+-------------------------------------------+---------+ -| Prepare Substructure for Turbine Assembly | 12h | -+-------------------------------------------+---------+ -| Lift and Fasten Tower Section | 12h | -| (repeated if necessary) | | -+-------------------------------------------+---------+ -| Lift and Fasten Nacelle | 7h | -+-------------------------------------------+---------+ -| Lift and Fasten Blade (repeated) | 3.5h | -+-------------------------------------------+---------+ -| Mechanical Completion and Verification | 24h | -+-------------------------------------------+---------+ - - -Substructure Tow-out and Assembly -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -+-------------------------------------+------------+ -| Process | Default | -+=====================================+============+ -| Ballast to Towing Draft | 6h | -+-------------------------------------+------------+ -| Tow-out | calculated | -+-------------------------------------+------------+ -| Ballast to Operational Draft | 6h | -+-------------------------------------+------------+ -| Connect Mooring Lines | 22h | -+-------------------------------------+------------+ -| Check Mooring Lines and Connections | 12h | -+-------------------------------------+------------+ diff --git a/docs/source/phases/install/scour/api_ScourProtectionInstall.rst b/docs/source/phases/install/scour/api_ScourProtectionInstall.rst deleted file mode 100644 index 140b5f3c..00000000 --- a/docs/source/phases/install/scour/api_ScourProtectionInstall.rst +++ /dev/null @@ -1,8 +0,0 @@ -Scour Protection Installation API -================================= - -For detailed methodology, please see -:doc:`Scour Protection Installation Methodology `. - -.. autoclass:: ORBIT.phases.install.ScourProtectionInstallation - :members: diff --git a/docs/source/phases/install/scour/doc_ScourProtectionInstall.rst b/docs/source/phases/install/scour/doc_ScourProtectionInstall.rst deleted file mode 100644 index 74cd968d..00000000 --- a/docs/source/phases/install/scour/doc_ScourProtectionInstall.rst +++ /dev/null @@ -1,69 +0,0 @@ -Scour Protection Installation Methodology -========================================= - -For details of the code implementation, please see -:doc:`Scour Protection Installation API `. - -Overview --------- - -The ``ScourProtectionInstallation`` modules simulates the installation of scour -protection around the base of a offshore substructure. In many offshore site -conditions, scour protection is a necessary step to reduce the effects of -hydrodynamic scour development around the substructure. ORBIT models the -installation of a rock layer installed at a diameter surrounding the -substructure base. This process is not typically a significant cost driver for -the project, but the installation time and associated costs are significant -enough that they should not be ignored when computing BOS costs. - -Configuration -------------- - -This module is simple to configure, as the main parameter considered is the -``tons_per_substructure`` to install. Currently ORBIT models the simplest -installation method, involving "Side Stone Installation Vessels" that dump -loads of rocks next to the substructure without much ability to ensure that -their payload is distributed evenly. A future version of ORBIT may expand this -module to include more modern installation approaches using a "Fall Pipe -Vessel" that allow for an even distrubution of scour protection material. - -Example -~~~~~~~ - -.. code-block:: - - { - 'scour_protection_install_vessel': 'example_vessel' - 'site': {'distance': 20}, - - ... - - 'scour_protection': { - 'tons_per_substructure': 1200 - } - } - } - -Processes ---------- - -The scour protection installation vessel loads rock at port, transits to site -and installs the required amount at each substructure until empty. At this -point, it will return to port to load additional rock and repeat the above -steps until all substructures have had scour protection installed. The process -times for this operation are outlined below. - -+-----------------+--------------------------------------+------------+ -| Process | Inputs | Default | -+=================+======================================+============+ -| Load Rocks | ``load_rocks_time`` | 4h | -+-----------------+--------------------------------------+------------+ -| Transit to Site | ``site_distance``, ``transit_speed`` | calculated | -+-----------------+--------------------------------------+------------+ -| Drop Rocks | ``drop_rocks_time`` | 10h | -+-----------------+--------------------------------------+------------+ - -.. Process Diagrams -.. ---------------- - -.. Coming soon! diff --git a/docs/source/phases/install/turbine/api_TurbineInstallation.rst b/docs/source/phases/install/turbine/api_TurbineInstallation.rst deleted file mode 100644 index a7cc0843..00000000 --- a/docs/source/phases/install/turbine/api_TurbineInstallation.rst +++ /dev/null @@ -1,18 +0,0 @@ -Turbine Installation API -======================== - -For detailed methodology, please see -:doc:`Turbine Installation Methodology `. - -.. automodule:: ORBIT.phases.install.turbine_install.standard - :members: - :exclude-members: extract_vessel_specs - -.. automodule:: ORBIT.phases.install.turbine_install._common - :members: - -.. automodule:: ORBIT.phases.install.turbine_install._single_wtiv - :members: - -.. automodule:: ORBIT.phases.install.turbine_install._wtiv_with_feeders - :members: diff --git a/docs/source/phases/install/turbine/doc_TurbineInstall.rst b/docs/source/phases/install/turbine/doc_TurbineInstall.rst deleted file mode 100644 index 82431bb0..00000000 --- a/docs/source/phases/install/turbine/doc_TurbineInstall.rst +++ /dev/null @@ -1,151 +0,0 @@ -Turbine Installation Methodology -================================ - -For details of the code implementation, please see -:doc:`Turbine Installation API `. - -Overview --------- - -The ``TurbineInstallation`` module simulates the installation of turbines at -site. For the purpose of this module, the turbine is discretized into five -different components: a tower, nacelle and three turbine blades. The module can -be configured such that a single wind turbine installation vessel (WTIV) -transports and installs all of the turbine components or it can be configured -to include feeder barges that transport the components to site. -:ref:`process-diagrams` detailing the vessel logistics for these two installation -can be seen below. - -.. note:: - - For both installation strategies the WTIV performs all of the on site - operations with its onboard crane, either picking components from its own - deck or a neighboring feeder barge. - -Configuration -------------- - -To configure ``TurbineInstallation`` to utilize feeder barges to transit the -turbine components from port, add the following configuration to the project -configuration. - -.. code-block:: python - - config = { - ... - - "feeder": "example_feeder", # name of vessel configuration file without extension - "num_feeders": 2, - - ... - } - -Processes ---------- - -Port Operations -~~~~~~~~~~~~~~~ - -Vessels load items and fasten them on their deck at port using the port crane. -Ports are configured with one crane by default, which limits multiple vessels -from accessing port resources at a time. This can be overridden by configuring -a port with additional cranes in a project configuration: - -.. code-block:: python - - "port": { - "num_cranes": 2 # Two vessels can access port resource simultaneously. - } - -The default times for fastening each component to deck are listed below. - -+-----------+-------------------------+---------+ -| Component | Inputs | Default | -+===========+=========================+=========+ -| Tower | ``tower_fasten_time`` | 4h | -+-----------+-------------------------+---------+ -| Nacelle | ``nacelle_fasten_time`` | 4h | -+-----------+-------------------------+---------+ -| Blade | ``blade_fasten_time`` | 1.5h | -+-----------+-------------------------+---------+ - -Currently, all vessels are only able to load multiples of complete sets of -components (tower, nacelle and three blades). - -Site Preperation -~~~~~~~~~~~~~~~~ - -Once the WTIV and a set of components are at site (either on the WTIV or a -feeder barge), the WTIV positions itself onsite and jacks up. The following -table outlines the inputs and default times for these tasks. - -+-----------------+--------------------------+------------+ -| Action | Inputs | Default | -+=================+==========================+============+ -| Position Onsite | ``site_position_time`` | 2h | -+-----------------+--------------------------+------------+ -| Jack-up | | ``depth, extension`` | calculated | -| | | ``speed_above_depth`` | | -| | | ``speed_below_depth`` | | -+-----------------+--------------------------+------------+ - -Turbine Installation -~~~~~~~~~~~~~~~~~~~~ - -After site preperation is complete, the WTIV begins installation by releasing a -turbine from its fastening (either on its own deck or neighboring feeder -barge). The tower is then lifted into place using the WTIV crane and attached -to the substructure. The nacelle is then released from its fastenings, lifted -into place and attached to the tower. The same process is repeated for each of -the three turbine blades. Inputs and process times are summarized in the -following table. - -+------------------+--------------------------+------------+ -| Action | Inputs | Default | -+==================+==========================+============+ -| Reequip Crane | ``crane_reequip_time`` | 1h | -+------------------+--------------------------+------------+ -| Release Tower | ``tower_release_time`` | 3h | -+------------------+--------------------------+------------+ -| Lift Tower | | ``turbine.hub_height`` | calculated | -| | | ``wtiv.crane_rate`` | | -| | | ``wave_height`` | | -+------------------+--------------------------+------------+ -| Attach Tower | ``tower_attach_time`` | 6h | -+------------------+--------------------------+------------+ -| Release Nacelle | ``nacelle_release_time`` | 3h | -+------------------+--------------------------+------------+ -| Lift Nacelle | | ``turbine.hub_height`` | calculated | -| | | ``wtiv.crane_rate`` | | -| | | ``wave_height`` | | -+------------------+--------------------------+------------+ -| Attach Nacelle | ``nacelle_attach_time`` | 6h | -+------------------+--------------------------+------------+ -| Release Blade | ``blade_release_time`` | 1h | -+------------------+--------------------------+------------+ -| Lift Blade | | ``turbine.hub_height`` | calculated | -| | | ``wtiv.crane_rate`` | | -| | | ``wave_height`` | | -+------------------+--------------------------+------------+ -| Attach Blade | ``blade_attach_time`` | 3.5h | -+------------------+--------------------------+------------+ - -.. _process-diagrams: - -Process Diagrams ----------------- - -Single WTIV Installation -~~~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: ../../../images/process_diagrams/turbine_single_wtiv.png - -WTIV with Feeder Barges Installation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: ../../../images/process_diagrams/turbine_wtiv_with_feeders.png - -Component Installation -~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: ../../../images/process_diagrams/turbine_install.png diff --git a/docs/source/publications/index.rst b/docs/source/publications/index.rst deleted file mode 100644 index 2b644f5a..00000000 --- a/docs/source/publications/index.rst +++ /dev/null @@ -1,8 +0,0 @@ -.. _publications: - -Publications -============ - -- `"ORBIT: Offshore Renewables Balance-of-System and Installation Tool" `_ -- `"Process-Based Balance-of-System Cost Modeling for Offshore Wind Power Plants in the United States" `_ -- `"Impacts of Turbine and Plant Upsizing on the Levelized Cost of Energy for Offshore Wind" `_ diff --git a/docs/source/team.rst b/docs/source/team.rst deleted file mode 100644 index c0abb194..00000000 --- a/docs/source/team.rst +++ /dev/null @@ -1,28 +0,0 @@ -.. _team: - -ORBIT Team -========== - -Authors -------- -- Jake Nunemaker (NREL) -- Matt Shields (NREL) -- Rob Hammond (NREL) -- Patrick Duffy (NREL) -- Nick Riccobono (NREL) -- Daniel Mulas Hernando (NREL) - -Maintainer ------------ -- Nick Riccobono (NREL) - - -.. Contributors -.. ------------ - -.. Coming soon! - -.. Reviewers -.. --------- - -.. Coming soon! diff --git a/docs/source/tutorial/available_outputs.rst b/docs/source/tutorial/available_outputs.rst deleted file mode 100644 index 20b17231..00000000 --- a/docs/source/tutorial/available_outputs.rst +++ /dev/null @@ -1,119 +0,0 @@ -Available Outputs -================= - -``ProjectManager`` is used to run a collection of ORBIT modules representing a -complete offshore wind project installation. The outputs of each module are -then aggregated into several different project-level outputs available via -the ``ProjectManager`` API. - -System and Installation CapEx ------------------------------ - -The two main outputs of ORBIT are the System CapEx (the total cost of procuring -the configured subsystems) and the Installation CapEx (the total cost of -installing the subsystems). - -.. code-block:: python - - from ORBIT import ProjectManager - config = { - ... - } - - project = ProjectManager(config) - project.run() - - project.system_capex - project.installation_capex - -System CapEx is the sum of the unit costs (either user inputs or results from -design phases) multiplied by the configured number of turbines, cable section -lenghts or other appropriate unit. This output does not change based on the -installation simulation. - -Installation CapEx is a dynamic result based on the installation simulation and -is based on the times associated with each subsystem installation, day rates of -any vessels/ports and any accrued weather delays. - -BOS CapEx ---------- - -The balance-of-system CapEx (available as ``project.bos_capex``) is the sum of -the system and installation capex numbers and is one of the core outputs of the -ORBIT module. - -Soft CapEx ----------- - -Soft CapEx (``project.soft_capex``) represents additional project level costs -associated with commissioning, decommissioning and financing of the project. -The cost factors can be input in the ``project_parameters`` subdict of an ORBIT -configuration. The default cost factors for these categories are derived from the -`2018 Cost of Wind Energy Review `_. - -Project CapEx -------------- - -Project CapEx (``project.project_capex``) includes the costs associated with -the lease area, the development of the construction operations plan and any -environmental review and other upfront project costs. There are default values -for all of these subcategories, however the values can also be overridden in the -``project_parameters`` subdict. - -Total CapEx ------------ - -Total CapEx (``project.total_capex``) is the sum of the BOS, Soft and Project -CapEx numbers. This represents complete project costs including all upfront -costs, financing, procurement and installation of BOS subsystems and the -procurement costs of the turbines. - -.. note:: - - ORBIT doesn't explicity model the procurement of turbines, however the - Turbine CapEx is included within ``project.total_capex``. To configure the - cost of the turbines, ``turbine_capex`` can be passed into the - ``project_parameters`` subdict of an ORBIT config. The default is $1300/kW. - -Actions -------- - -A list of every step taken during the installation modules is available at -``project.actions``. The best way to view, sort and save these results is as -a pandas DataFrame. A few example use cases are presented below. - -.. code-block:: python - - import pandas as pd - df = pd.DataFrame(project.actions) - - # Sort by a specific phase - df.loc[df["phase"]=="MonopileInstallation"] - - # Group by vessel and action to see where each vessel spent the most time - df.groupby(["vessel", "action"]).sum()["duration"] - - # Save results to 'csv' - df.to_csv("filename.csv") - -Detailed Outputs ----------------- - -More detailed results from individual phases are available at -``project.detailed_outputs``. - -Cash Flow and Net Present Value -------------------------------- - -``ProjectManager`` also includes a basic cash flow and net present value model. -The project must have the array system, export system and the substation -installation modules configured for this model to be applicable. The model will -find the point in the project logs where the substation and export system -installations were completed and where each array system string was installed. -When all three of these conditions are met, the project can begin to generate -energy and produce revenue. The revenue generation is then superimposed on the -monthly spend of the installation modules for the ``project.cash_flow``. - -The net present value of the project can then be calculated and is available at -``project.npv``. The underlying financial assumptions for this model are also -contained within the ``project_parameters`` subdict of the ORBIT configuration. diff --git a/docs/source/tutorial/define_vessels.rst b/docs/source/tutorial/define_vessels.rst deleted file mode 100644 index 25e532e0..00000000 --- a/docs/source/tutorial/define_vessels.rst +++ /dev/null @@ -1,97 +0,0 @@ -Vessels and Cable Definitions -============================= - -Most installation modules require individual vessels, cables or turbines to be -defined to complete the required configuration. These definitions are composed -of several nested dictionaries (representing vessel subcomponents) that can be -cumbersome to define within the project level configuration. As such, there are -helper libraries included with ORBIT that allow vessels/cables to be defined -elsewhere and referenced with their name. By default, these libraries are -located at: - -.. code-block:: - - /path/to/orbit/library/cables/ - /path/to/orbit/library/vessels/ - /path/to/orbit/library/turbines/ - -External Libraries ------------------- - -It is possible to have ORBIT look in external folders for library items. To -configure an external library, use the ``initialize_library`` function. It is -recommended that any proprietary vessel or cable files be located outside -of the main repository. - -.. code-block:: python - - from ORBIT.core.library import initialize_library - initialize_library("path/to/external/library/") - -.. note:: - - If an external library is defined, ORBIT will search for a configured - library item there first and then search the library defined within ORBIT if - the item is not found. This is so that generic library items do not need to - be copied to the external library but can still be used within a project - definition. - -File Format ------------ - -Both vessels and cables are stored as ``.yaml`` files to preserve their -dictionary format. They can be referenced in project configurations using the -filename preceding ``.yaml``. For a filename of ``example_wtiv.yaml``, see the -example below: - -.. code-block:: python - - config = { - 'wtiv': 'example_wtiv', - - 'design_phases': ['MonopileDesign', 'ArraySystemDesign'], - 'install_phases': [ - 'MonopileInstallation', - 'TurbineInstallation' - ] - } - -For help on working with ``.yaml`` files, please see this -`tutorial `_. - -Vessel Configurations ---------------------- - -Throughout installation modules in ORBIT, there are several processes that -require the operating vessel to have a specific subcomponent. For example, all -offshore lifts require the vessel to have a crane onboard, otherwise the vessel -isn't able to perform the operation. These contraints translate into how -vessels are defined. Within a vessel definition (either a ``dict`` or a -``.yaml`` file), subcomponents are defined with another dictionary: - -.. code-block:: python - - vessel = { - 'crane_specs': { # <-- Vessel will be able to perform crane operations - 'max_lift': 500, - 'max_windspeed': 15, - ... - }, - - ... - - } - -In the example above, a vessel without ``'crane_specs'`` would not be able to -perform any crane operations. If a vessel is configured that can't complete an -operation in a phase, a ``MissingComponent`` error will be raised. The -following subcomponents and their use cases are available to be configured: - -- ``'vessel_specs'`` - General vessel parameters including day rate. -- ``'transport_specs'`` - Transit related parameters and constraints. -- ``'storage_specs'`` - Storage related parameters. Required to transport items - on deck. -- ``'jacksys_specs'`` - Jacking system related parameters. Currently required - for all fixed substructure and turbine installations. -- ``'crane_specs'`` - Crane related parameters and constraints. Required for - any offshore lifts. diff --git a/docs/source/tutorial/design_phases.rst b/docs/source/tutorial/design_phases.rst deleted file mode 100644 index c8a37bf4..00000000 --- a/docs/source/tutorial/design_phases.rst +++ /dev/null @@ -1,173 +0,0 @@ -.. _design_modules: - - -Design Modules -============== - -There are two types of modules within ORBIT, design and installation. -Installation modules require a number of inputs, setup a simulation and -model the installation of offshore wind components. Alternatively, design -modules model the design of an offshore wind components and can produce inputs. -Within the context of ``ProjectManager``, a design module can remove inputs from -the required configuration for a project. The following example will illustrate -this feature and how it is used within ``ProjectManager``. - -.. warning:: - - Design phase modules in ORBIT are intended to capture broad scaling trends - for offshore wind components and do not represent the required fidelity of a - full engineering design. - -Example -------- - -Consider a simple project with one monopile installation phase: - -.. code-block:: python - - from ORBIT import ProjectManager - - phases = [ - "MonopileInstallation", # Monopile installation with one vessel - ] - - required_config = ProjectManager.compile_input_dict(phases) - required_config - - >>> - - { - 'wtiv': 'dict | str', - - 'site': {'depth': 'float','distance': 'float'}, - 'plant': {'num_turbines': 'int'}, - 'turbine': {'hub_height': 'float'}, - - 'port': { - 'num_cranes': 'int', - 'monthly_rate': 'float', - 'name': 'str (optional)' - }, - - 'monopile': { - 'type': 'Monopile', - 'length': 'float', - 'diameter': 'float', - 'deck_space': 'float', - 'mass': 'float' - }, - - 'transition_piece': { - 'type': 'Transition Piece', - 'deck_space': 'float', - 'mass': 'float' - }, - - 'design_phases': [], - 'install_phases': ['MonopileInstallation'] - } - -In the required configuration for the above project, the user must fill in a -``'monopile'`` sub dictionary. Alternatively, a ``MonopileDesign`` phase could -be included in the phase list. This additional phase would effectively fill in -the ``'monopile'`` sub dictionary for the user: - -.. code-block:: python - - ... - - phases = [ - "MonopileDesign", # Basic monopile sizing based on turbine size and site - "MonopileInstallation", # Monopile installation with one vessel - ] - - required_config = ProjectManager.compile_input_dict(phases) - required_config - - >>> - - { - - ... - - 'turbine': { - 'hub_height': 'float', - 'rotor_diameter': 'float', # <-- Additional input from MonopileDesign - 'rated_windspeed': 'float' - }, - - ... - # <-- 'monopile' no longer required - - 'monopile_design': { - 'design_time': 'float (optional)', - ... - }, - - 'design_phases': ['MonopileDesign'], - 'install_phases': ['MonopileInstallation'] - } - -.. note:: - - There may be additional inputs required for a design phase. In this example, - additional site level information (eg. ``turbine.rotor_diameter``) is added - to the required configuration when the ``MonopileDesign`` phase is added to - the phase list. - -Overriding Values from a Design Phase -------------------------------------- - -In the example above, the ``MonopileDesign`` phase will produce the input -parameters ``'monopile'`` and ``'transition_piece'``. It is also possible to -supply some of the values for these designs if known and let ``MonopileDesign`` -fill in the rest. For example, if the user knows the dimensions of the monopile -but not the transition piece, the ``'monopile'`` dictionary can be added to the -project config above: - -.. code-block:: python - - config { - - 'turbine': { - 'hub_height': 130, - 'rotor_diameter': 154, # <-- Additional input from MonopileDesign - 'rated_windspeed': 11 - }, - - 'monopile': { # <-- 'monopile' isn't required but can be - 'type': 'Monopile', # added to include known project parameters. - 'mass': 800, # Other inputs produced by MonopileDesign will - 'length': 100 # be added to the config. - }, - - ... - - 'monopile_design': { - 'design_time': 'float (optional)', - ... - }, - - 'design_phases': ['MonopileDesign'], - 'install_phases': ['MonopileInstallation'] - } - - project = ProjectManager(config) - project.run() - - project.config - - >>> - - { - - ... - - 'monopile': { - 'type': 'Monopile', - 'mass': 800, - 'length': 100, - 'diameter': 8.512, # <-- Additional inputs added by MonopileDesign - 'deck_space': 36.245 # - }, - } diff --git a/docs/source/tutorial/index.rst b/docs/source/tutorial/index.rst deleted file mode 100644 index 969a1197..00000000 --- a/docs/source/tutorial/index.rst +++ /dev/null @@ -1,25 +0,0 @@ -.. _tutorial: - -Tutorial -======== - -Welcome to the tutorial for ORBIT! The following examples will cover basic -usage of ORBIT through ``ProjectManager`` and available configurations. For -backround on the project and Offshore Balance of System modeling, please see -the :ref:`introduction `. For more advanced examples and real world -validation cases, please see the -`examples `_ section on -GitHub. - -.. toctree:: - :maxdepth: 1 - :titlesonly: - - load_config - project_manager - design_phases - phase_specific - available_outputs - start_dates - define_vessels - parametric_manager diff --git a/docs/source/tutorial/load_config.rst b/docs/source/tutorial/load_config.rst deleted file mode 100644 index 9ed02fbc..00000000 --- a/docs/source/tutorial/load_config.rst +++ /dev/null @@ -1,97 +0,0 @@ -Working with ORBIT Modules and Projects -======================================= - -ORBIT is made up of many different modules representing the design and -installation of offshore wind components. Each module can be ran indepenently -or within a project using :ref:`ProjectManager `. Modules and -projects are configured with a set of nested dictionaries. - -Running Individual Modules --------------------------- - -To run a module indepenently: - -.. code-block:: python - - from ORBIT.phases.install import MonopileInstallation - config = { - 'site': { - 'depth': 20, - 'mean_windspeed': 9 - }, - - 'turbine': { - 'rotor_diameter': 130, - 'hub_height': 110, - }, - - # etc... - } - - phase = MonopileInstallation(config) - phase.run() - print(phase.total_cost) - -The inputs required for each module are stored in ``expected_config``. -For example: - -.. code-block:: python - - from ORBIT.phases.install import MonopileInstallation - MonopileInstallation.expected_config - - >>> - - { - 'site': { - 'depth': 'm', - 'mean_windspeed': 'm/s' - }, - - 'turbine': { - 'rotor_diameter': 'm', - 'hub_height': 'm', - }, - # etc... - } - -The returned nested dictionary can then be filled out by replacing the strings -('m', 'm/s', etc.) with the appropriate inputs for the analysis question. - -.. note:: - - `expected_config` will return the required unit of the input where applicable. - -Loading and Saving Configurations ---------------------------------- - -There are utility functions within ORBIT that allow configurations to be saved -and loaded from a '.yaml' format. Yaml is a data format similar to json in that -it can store nested data structures. Yaml has several advantages though, the -primary being that it supports comments. - -To load a configuration: - -.. code-block:: python - - from ORBIT import load_config - config = load_config("filepath/to/config.yaml") - -To save a configuration: - -.. code-block:: python - - from ORBIT import save_config - save_config(config, "filepath/to/config.yaml") - -.. note:: - - It isn't required to use these utility functions. The standard yaml load and - dump routines will work for converting python nested dictionaries to and from - the yaml format. However, the ``load_config`` method supports scientific - notation and the standard yaml routine does not. - -Running Multiple Phases ------------------------ - -To run multiple phases, see the :ref:`ProjectManager ` documentation. diff --git a/docs/source/tutorial/parametric_manager.rst b/docs/source/tutorial/parametric_manager.rst deleted file mode 100644 index eda0f7a0..00000000 --- a/docs/source/tutorial/parametric_manager.rst +++ /dev/null @@ -1,77 +0,0 @@ -.. _parametric: - -ParametricManager -================= - -``ParametricManager`` is used to run simple parametric studies in ORBIT -by defining a subset of the inputs as a list. A basic parametric study where -the site depth and distance to shore is varied is shown below. - -.. code-block:: python - - from ORBIT import ParametricManager - - # Any inputs that aren't parameterized are passed in using a - # typical ORBIT configuration - base = { - "turbine": "15MW_generic", - "wtiv": "example_wtiv", - - ... - } - - # Parameterized inputs are passed in as a list. The product of all - # scenarios will be ran. - params = { - "site.depth": [10, 30, 50], - "site.distance": [20, 40, 60], - } - - # Desired results are saved using lambda functions. These functions - # can be used to save any output normally available in ProjectManager. - # In this example, the installation and system CapEx results are saved. - results = { - "Installation": lambda project: project.installation_capex, - "System": lambda project: project.system_capex - } - - # A weather profile to use in all scenarios can also be passed in. - scenarios = ParametricManager(base, params, results, weather=weather) - scenarios.run() - -The results are saved as a pandas DataFrame at ``scenarios.results`` where each -row represents a different scenario run and includes the parameterized inputs -and any results the user configured. - -.. note:: - - The parameterized inputs were passed in using "dot-notation". In this - notation, each "." tells ParametricManager to go a level deeper in the ORBIT - configuration. For example, "site.depth" is the "site" subdict, and the - "depth" input. This can used at any depth within an ORBIT configuration. - -Plotting --------- - -The outputs can be easily visualized using -`matplotlib `_ or -`seaborn `_. - -.. code-block:: python - - import matplotlib.pyplot as plt - import seaborn as sns - - # Scatter Plot - plt.scatter(scenarios.results["site.depth"], scenarios.results["Installation"]) - - # Box Plot - sns.boxplot(data=scenarios.results, x='site.depth', y='System') - - # Box Plot with Hue - sns.boxplot(data=scenarios.results, x='site.depth', y='System', hue='site.distance') - -Parametric Weather ------------------- - -Coming soon! diff --git a/docs/source/tutorial/phase_specific.rst b/docs/source/tutorial/phase_specific.rst deleted file mode 100644 index 580fb16c..00000000 --- a/docs/source/tutorial/phase_specific.rst +++ /dev/null @@ -1,79 +0,0 @@ -Phase Specific Configurations -============================= - -By default, ``ProjectManager.compile_input_dict()`` returns the minimum -required configuration, combining the same parameter that is needed for -multiple phases into one input. This isn't always a desired outcome as there -are cases when inputs need to be different for each phase. For example, the -``distance_to_shore`` parameter may be different for each installation phase -if different ports are used to stage monopiles and turbines or the -installations may use different installation vessels. In these cases, it is -necessary to define phase specific input parameters. - -Example -------- - -Consider the following example with two installation phases: - -.. code-block:: python - - config = { - 'wtiv': 'example_wtiv', - 'site': { - 'depth': 20, - 'distance': 100 - }, - - ... - - 'design_phases': [], - 'install_phases': [ - 'MonopileInstallation', - 'TurbineInstallation' - ] - } - -In the above configuration, the same input parameters for the site and the WTIV -will be used for both installations. In order to modify this configuration to -include phase specific inputs, the phase namespace (eg. ``TurbineInstallation``) -must be introduced into the configuration: - -.. code-block:: python - - config = { - 'wtiv': 'example_wtiv', - 'site': { - 'depth': 20, - 'distance': 50 - }, - - ... - - 'TurbineInstallation': { # <-- Turbine installation namespace - 'wtiv': 'other_wtiv', # <-- Vessel defined specific to namespace - 'site': { - 'distance': 100 # <-- Distance to port defined specific - } # to namespace - - 'design_phases': [], - 'install_phases': [ - 'MonopileInstallation', - 'TurbineInstallation' - ] - } - - -In the above example, the turbine installation has inputs for the WTIV and site -distance defined specific to it's namespace. Notice that the structure of the -``TurbineInstallation`` follows the same structure as the overall -configuration. Any input parameter (no matter how many dictionaries down) can -be defined specific to each phase using this method. When the model is run with -``project.run()`` ORBIT will use the most specific parameter available -in the input configuration for each of the inputs. Phase specific parameters -always take precedence over the more general configuration. - -.. .. note:: - -.. Using the concepts above and overlapping start dates, complex phase -.. sequencing can be modeled with ORBIT. For an example of this, please see this -.. `validation case `_. diff --git a/docs/source/tutorial/project_manager.rst b/docs/source/tutorial/project_manager.rst deleted file mode 100644 index 05ba1285..00000000 --- a/docs/source/tutorial/project_manager.rst +++ /dev/null @@ -1,120 +0,0 @@ -.. _manager: - -ProjectManager -============== - -``ProjectManager`` is the primary system for interacting with ORBIT. It -provides the ability to configure and run one or multiple modules at a time, -allowing the user to customize ORBIT to fit the needs of a specific project. -It also provides a helper method to detail what inputs are required to run the -desired configuration. The example below shows how to import -``ProjectManager``, configure a simple project with two phases, and return the -required configuration parameters. - -.. code-block:: python - - from ORBIT import ProjectManager - - phases = [ - "MonopileDesign", # Returns monopile sizing given site information - "MonopileInstallation" # Simulates the installation of monopiles - ] - - expected_config = ProjectManager.compile_input_dict(phases) - expected_config - - >>> - - { - 'site': { - 'depth': 'm', - 'mean_windspeed': 'm/s' - }, - - 'turbine': { - 'rotor_diameter': 'm', - 'hub_height': 'm', - 'rated_windspeed': 'm/s' - }, - - 'monopile_design': {'air_density': 'kg/m3 (optional)', - 'load_factor': 'float (optional)', - 'material_factor': 'float (optional)', - 'monopile_density': 'kg/m3 (optional)', - 'monopile_modulus': 'Pa (optional)', - 'monopile_steel_cost': 'USD/t (optional)', - 'monopile_tp_connection_thickness': 'm (optional)', - 'soil_coefficient': 'N/m3 (optional)', - 'tp_steel_cost': 'USD/t (optional)', - 'transition_piece_density': 'kg/m3 (optional)', - 'transition_piece_length': 'm (optional)', - 'transition_piece_thickness': 'm (optional)', - 'turb_length_scale': 'm (optional)', - 'weibull_scale_factor': 'float (optional)', - 'weibull_shape_factor': 'float (optional)', - 'yield_stress': 'Pa (optional)'}, - ... - - 'design_phases': ['MonopileDesign'], - 'install_phases': ['MonopileInstallation'] - } - -``expected_config`` contains all parameters that are required to run the -``MonopileDesign`` and ``MonopileInstallation`` phases (as well as the optional -ones in the ``monopile_design`` sub dictionary). The returned dictionary can -now be filled out and the model can be ran: - -.. code-block:: python - - ... - - config = { - 'site': { - 'depth': 20, - 'mean_windspeed': 9.5, - }, - - 'turbine': { - 'rotor_diameter': 205, - 'hub_height': 125, - 'rated_windspeed': 11 - }, - - 'monopile_design': {}, - - 'design_phases': ['MonopileDesign'], - 'install_phases': ['MonopileInstallation'] - } - - project = ProjectManager(config) - project.run() - - # .design_results returns the results of all design phases that were ran - project.design_results - - >>> - - { - 'monopile': { - 'diameter': 7.11, # m - 'thickness': 0.078, # m - 'embedment_length': 55.84, # m - 'length': 85.84, # m - 'mass': 640.57, # t - 'deck_space': 5.58, # m2 - 'type': 'Monopile' - } - } - -.. note:: - - To include weather in the simulation, pass an hourly pandas DataFrame into - ``ProjectManager``. Eg. ``ProjectManager(config, weather=weather_df)``. All - installation phases will use this time series. - -Design Modules --------------- - -For a more detailed description of design modules and the interaction with -installation modules, please see the :ref:`Design Phases ` -documentation. diff --git a/docs/source/tutorial/start_dates.rst b/docs/source/tutorial/start_dates.rst deleted file mode 100644 index 2b343079..00000000 --- a/docs/source/tutorial/start_dates.rst +++ /dev/null @@ -1,104 +0,0 @@ -Phase Start Dates -================= - -Default Configuration ---------------------- - -By default, ``ProjectManager`` will run phases in the order that they are -defined in the lists ``'design_phases'``, then ``'install_phases'`` located in -the project configuration. This order also determines the sequence of phases in -the outputs, including the project level action log ``.actions``. If -the project is configured with a weather file, any installation phases will -start at the beginning of the weather profile. - -.. code-block:: python - - config = { - ... - - 'design_phases': ['MonopileDesign', 'ArraySystemDesign'], - 'install_phases': [ - 'MonopileInstallation', - 'TurbineInstallation' - ] - } - - >>> - - # ProjectManager will run the above phases in this order: - # - MonopileDesign - # - ArraySystemDesign - # - MonopileInstallation - # - TurbineInstallation - -Defining Start Dates --------------------- - -Installation phases can also be defined with optional start dates that will -determine which portion of the weather file to use and will affect the sequence -of outputs. This feature also allows phases to overlap if required. - -.. code-block:: - - { - ... - - 'design_phases': ['MonopileDesign', 'ArraySystemDesign'], - 'install_phases': { - 'MonopileInstallation': '03/01/2019', - 'TurbineInstallation': '05/01/2019' - } - } - -In the example above, the turbine installation will start two months after -the monopile installation and use the weather profile starting on May 1st, 2019. -If any defined start dates fall outside of the bounds of a configured weather -profile, ``WeatherProfileError`` will be raised. If a simulation reaches the -end of a weather profile before it completes, ``WeatherProfileExhuasted`` will -be raised. - -The starting point of the phases can also be indexed by the location in the -weather time series: - -.. code-block:: - - { - ... - - 'design_phases': ['MonopileDesign', 'ArraySystemDesign'], - 'install_phases': { - 'MonopileInstallation': 0, # <-- Will start at the first weather data point - 'TurbineInstallation': 2000 # <-- Starts 2000 data points later - } - } - -.. warning:: - - At this time, ORBIT does not have any constraints on overlapping phases, so - it is possible to configure an unrealistic project (eg. turbine installation - completes before substructure installation). - -.. note:: - - It should also be noted that overlapping phases do not affect one another. - Port constraints (eg. number of cranes available for loading) are applied per - installation phase. - -Phase Dependencies ------------------- - -Phases can also be defined to start at a percentage completed for a different -phase. For example, the following config could be used to have the installation -of the turbines start when the monopiles are 50% installed: - -.. code-block:: - - { - ... - - 'design_phases': ['MonopileDesign', 'ArraySystemDesign'], - 'install_phases': { - 'MonopileInstallation': 0, - 'TurbineInstallation': ('TurbineInstallation', 0.5) - } - } diff --git a/docs/team.md b/docs/team.md new file mode 100644 index 00000000..1d59c60e --- /dev/null +++ b/docs/team.md @@ -0,0 +1,15 @@ +(team)= +# ORBIT Team + +## Authors + +- Jake Nunemaker (NLR) +- Matt Shields (NLR) +- Rob Hammond (NLR) +- Patrick Duffy (NLR) +- Nick Riccobono (NLR) +- Daniel Mulas Hernando (NLR) + +## Maintainer + +- Rob Hammond (NLR) diff --git a/docs/topical_guides/cable_installation.md b/docs/topical_guides/cable_installation.md new file mode 100644 index 00000000..e064667b --- /dev/null +++ b/docs/topical_guides/cable_installation.md @@ -0,0 +1,130 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Cable Laying and Burying + +This guide will demonstrate the use of a combined cable laying and burying vessel compared to using +separate cable laying and burying vessels. Here we will focus on the array cabling, but the same +logic applies to the export cables. + +```{code-cell} ipython3 +from copy import deepcopy + +import pandas as pd + +from ORBIT import ProjectManager +``` + +Below, we set up a base configuration using an imagined cable and sections (25 each of 1km and 2km cable sections) designed for simplicity. + +```{code-cell} ipython3 +base_config = { + "site": {"distance": 20, "depth": 35}, + "array_system": { + "system_cost": 50e6, + "cables": { + "ExampleCable": { + "linear_density": 40, + "cable_sections": [(2, 25), (1, 25)] + } + } + }, + "install_phases": ["ArrayCableInstallation"] +} +``` + +## Single Cable Laying and Burying Process + +Now we can add a cable laying vessel that will simultaneously lay and bury cables by defining the +`array_cable_install_vessel`. For export cables, this is the `export_cable_install_vessel`. We will +create and run the project for later results comparison. + +```{code-cell} ipython3 +config_combined = deepcopy(base_config) +config_combined["array_cable_install_vessel"] = "example_cable_lay_vessel" + +project_combined = ProjectManager(config_combined) +project_combined.run() +``` + +## Separate Cable Laying and Burying Processes + +Using the same base configuration, we can now signal to the simulation to use a separate cable +laying and burying process by defining both the `array_cable_install_vessel` and +`array_cable_bury_vessel`. Note that the laying and combined vessel configuration keys are the same, +so that a separate input is only required when the cable burying vessel is utilized. Similar to the +above example, the export cable burying vessel is `export_cable_bury_vessel`. + +Even though the vessel is the same, by defining both vessel keys, we indicate that the processes +should be separated. + +```{code-cell} ipython3 +config_separate = deepcopy(base_config) +config_separate["array_cable_install_vessel"] = "example_cable_lay_vessel" +config_separate["array_cable_bury_vessel"] = "example_cable_lay_vessel" + +project_separate = ProjectManager(config_separate) +project_separate.run() +``` + +## Including a Trenching Vessel + +A third option is to also define a cable trenching vessel that digs out the trench for the cable +to lie in prior to the cable laying. This is often required for rocky soil types. Similar to the +separate process, we simply define the trenching vessel to activate the separated process using +the `array_cable_trench_vessel` key or `export_cable_trench_vessel` for export cables. + +```{code-cell} ipython3 +config_separate_with_trench = deepcopy(base_config) +config_separate_with_trench["array_cable_install_vessel"] = "example_cable_lay_vessel" +config_separate_with_trench["array_cable_bury_vessel"] = "example_cable_lay_vessel" +config_separate_with_trench["array_cable_trench_vessel"] = "example_cable_lay_vessel" + +# Run +project_separate_with_trench = ProjectManager(config_separate_with_trench) +project_separate_with_trench.run() +``` + +## Viewing the results + +Below we show the combined process for laying and burying the first cable. Note the "action" +column contains the "Lay/Bury" action to indicate the combined process. + +```{code-cell} ipython3 +df_combined = pd.DataFrame(project_combined.actions) +df_combined.iloc[3:12] +``` + +Now, we demonstrate the separate process by combining the separate laying and burying steps taken +for the first cable. Note that we have to concatenate two separate sections of the actions log +to highlight this process. For each process the vessel has to "Position Onsite", then go on +with the separate logic. For the burying process, this is much simpler than the intial laying +and cable connection. + +```{code-cell} ipython3 +df_separate = pd.DataFrame(project_separate.actions) +pd.concat((df_separate.iloc[4:13], df_separate.iloc[455:457])) + +``` + +Similar to the above, when we add trenching as a separate step, we have three discrete stages to +combine to demonstrate the trenching, laying, and burying for the first cable. + +```{code-cell} ipython3 +df_separate_with_trench = pd.DataFrame(project_separate_with_trench.actions) +pd.concat(( + df_separate_with_trench.iloc[4:6], + df_separate_with_trench.iloc[107:116], + df_separate_with_trench.iloc[558:560], +)) +``` diff --git a/docs/topical_guides/custom_array.md b/docs/topical_guides/custom_array.md new file mode 100644 index 00000000..49c3c9a3 --- /dev/null +++ b/docs/topical_guides/custom_array.md @@ -0,0 +1,416 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +(custom-array-layou)= +# Custom Array Cabling Guide + +## Dudgeon Windfarm + +This guide will walk through four of the main use cases for using the custom array cable layout +functionality of `ORBIT` for when custom turbine locations, cable lengths or burial speeds are needed. + +This example uses the Dudgeon Wind Farm turbine locations derived from their publicly available +[Call to Mariners](http://dudgeonoffshorewind.co.uk/news/notices/Dudgeon%20-%20Notice%20to%20Mariners%20wk25.pdf) documents. + +## Setup + +First, we'll import the necessary libraries and functionality, and setup our library reference. + +```{code-cell} ipython3 +from copy import deepcopy +from pprint import pprint +from pathlib import Path + +import numpy as np +import pandas as pd + +import ORBIT +from ORBIT import ProjectManager +from ORBIT.core import library +from ORBIT.phases.design import CustomArraySystemDesign +from ORBIT.phases.install import ArrayCableInstallation + + +# Set the library path for later use and initialize the ORBIT library +here = Path(".").resolve() +library_path = here.parents[1] / "library" if here.stem == "topical_guides" else here +library.initialize_library(library_path) +``` + +## Contents + +- [Overview](#overview): How to use the inputs +- [Case 1](#case_1): Needing to know what to collect +- [Case 2](#case_2): Coordinates with a straight-line distance for cable length +- [Case 3](#case_3): Using distance from a reference point +- [Case 4](#case_4): Adjusting for exclusions in the cable paths +- [Case 5](#case_5): Fully customizing the cabling parameters +- [Applying the cases to `ArrayCableInstallation`](#running) +- [Using `ProjectManager` to model the entire process](#project_manager) + +## Overview + +### Working with the ORBIT Library + +In the highest level of this repository there is a folder called `library` where all of the example +data for this notebook is going to be stored. While any folder could be used, the folder structure +must be strictly adhered to. More details on this structure can be found in the +[library section of the ORBIT introduction tutorial](#library-tutorial). + +For this example of how to setup a configuration, we will be using the file +[`library/project/config/example_custom_array_simple.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/project/config/example_custom_array_simple.yaml). + +Now, we will load the configuration file and display it below. + +```{code-cell} ipython3 +config = library.extract_library_specs("config", "example_custom_array_simple") +pprint(config) +``` + +### Key Differences In A Custom Layout Configuration + +There are 2 important differences in the custom array design that are work calling out: + +1) The `array_system_design` dictionary contains the `location_data` key, which contains the base + file name for the layout file, which is assumed to be CSV file located at + [`library/cables/dudgeon_array.csv`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/dudgeon_array.csv) +2) The `plant` dictionary uses the "custom" for `layout` to indicate that the custom array design + workflow will be used. + +Now, let's see what is contained within the additional files from the configuration dictionary. It +should be noted that running the design class extracts the data from the files automatically to +produce the below output. + +```{code-cell} ipython3 +array = CustomArraySystemDesign(config) +array.run() +print(array.config.dump()) +``` + +### Custom Array Layout CSV Explanation + +When the `dudgeon_array.csv` file is loaded, it is not passed back into the configuration +dictionary, so let's dissect this file: + +1. The file must have all of the columns shown below (not case-sensitive). + - All columns must be completely filled out for turbines (note on substation(s) following). + - `cable_length` and `bury_speed` are optional and if these are not known, simply fill with a 0. +2. A latitude and longitude must be provided for all turbines and substation(s). This can either be + a WGS-84 decimal coordinate or a distance-based "coordinate" where latitude and longitude are the + distances from some reference point, in kilometers; see [Case 3](#case_3) for more details. +3. Define the offshore substation(s) + - For each substation, the values in columns `id` and `substation_id` _must_ be the same. + - There is no need to fill in any data for the columns `String`, `Order`, `cable_length` and + `bury_speed`. +4. Define the turbines + - Each turbine should have a reference to its substation in the `substation_id` column. + - In this example, there is one substaion, so all of the values are "DOW_OSS". + - `string` and `order` should be 0-indexed for their ordering and not skip any numbers. + - In this example, the strings are ordered in clock-wise order starting from the string with + turbines labeled with an "A" in the + [Call to Mariners](http://dudgeonoffshorewind.co.uk/news/notices/Dudgeon%20-%20Notice%20to%20Mariners%20wk25.pdf) + - The ordering on a string should travel from substation to the farthest end of the cable + +Below is the how the Dudgeon layout has been configured. + +```{code-cell} ipython3 +df = pd.read_csv(library_path / "cables/dudgeon_array.csv").fillna("") +df.sort_values(by=["String", "Order"]) +``` + +(case_1)= +## Case 1: Needing to know what to collect + +In this first case, we assume little knowledge of what data are required for the CSV, and walk +through generating a sample CSV. We will use the +[`library/cables/example_custom_array_no_data.csv`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_no_data.csv) +configuration for this example. + +First, we need to load in the configuration dictionary. Then, we will create a starter file in +the `/project/config/plant` folder that can be filled in for a new project, which will be saved in the initialized library folder. + +```{code-cell} ipython3 +config = library.extract_library_specs("config", "example_custom_array_no_data") +pprint(config) + +array = CustomArraySystemDesign(config) +save_name = array.config["array_system_design"]["location_data"] +array.create_project_csv(save_name, folder="plant") +``` + +There are a few items worth noting in the layout: + +1. The offshore substation (row 0) is indicated via the `id` and `substation_id` columns being equal +2. For substaions only the `id`, `substation_id`, `name`, `latitude`, and `longitude` are required +3. `cable_length` and `bury_speed` are optional columns for turbines +4. `string` and `order` are filled out to maximize the length of a string given the cable(s) + provided, which translates to a maximum of 5 turbines in a string. +5. The string and cable numbering are 0-indexed, so the numbering system starts with 0. + +```{code-cell} ipython3 +dudgeon_array_no_data = pd.read_csv(library_path / f"project/plant/{save_name}.csv") +dudgeon_array_no_data + +# NOTE: remove this line if you would like to keep this data +Path(library_path / "project/plant/dudgeon_array_no_data.csv").unlink() +``` + +(case_2)= +## Case 2: Straight-Line Distance for Cable Lengths + +We have the turbine and offshore substation locations that were extracted from the Call to Mariners +referenced in the [Dudgeon Wind Farm Overview](#dudgeon-windfarm). However there is not any +information regarding the actual cable lengths or the cable burial speeds for each section. As such, +we will demonstrate using the standard straight-line distance and default cable burying rates. + +This case will rely on the +[`library/cables/example_custom_array_simple.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_simple.yaml) configuration. + +```{code-cell} ipython3 +config = library.extract_library_specs("config", "example_custom_array_simple") +pprint(config) +``` + +The below figure demonstrates the meaning of the straight-line distance between two points. + +```{code-cell} ipython3 +array = CustomArraySystemDesign(config) +array.run() +array.plot_array_system(show=True) +``` + +Here the cable length and bury speed are still set to 0 to indicate that they are unknown, which +will tell the installation phase to use either ORBIT's defaults or the vessel's settings. Notice +that the latitude and longitude here are WGS-84 decimal coordinates. + +```{code-cell} ipython3 +array.location_data +``` + +For later comparison, we'll show the cabling costs for the straight-line cabling assumption. + +```{code-cell} ipython3 +print(f"{'Cable Type':<16}| {'Cost in USD':>15}") +for cable, cost in array.cost_by_type.items(): + print(f"{cable:<16}| ${cost:>15,.2f}") + +print(f"{'Total':<16}| ${array.total_cable_cost:>15,.2f}") +``` + +(case_3)= +## Case 3: Distance-based coordinate system + +In this case, we will consider each turbine and substation on a distance-based coordinate system +where the longitude and latitude are the longitudinal (x direction) and latitudinal (y direction) +**distances**, in kilometers, from a common reference point. We are still using the Dudgeon data, +but the distances were computed outside of this example and the details are not be included. + +:::{important} +For distance-based coordinate systems, all points should be be positive, meaning the reference point +should either be both west and south of the farm itself, or at the west-most and south-most point. +::: + +Below, we can see that the input file +[`library/cables/dudgeon_distance_based.csv`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/dudgeon_distance_based.csv) +is still encoded in the exact same manner as [Case 2](#case_2), but latitude and longitude are +relative distances and not proper coordinates. + +```{code-cell} ipython3 +df = pd.read_csv(library_path / "cables/dudgeon_distance_based.csv", index_col=False).fillna("") +df +``` + +Using the distance-based location data requires us to set `distance` to True in the +`array_system_design` section of the configuration. This change is shown below in the +[`library/cables/example_custom_array_simple_distance_based.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_simple_distance_based.yaml) configuration. + +```{code-cell} ipython3 +config = library.extract_library_specs("config", "example_custom_array_simple_distance_based") +pprint(config) +``` + +Alternatively, we can set the `distance=True` when calling the `CustomArraySystemDesign`, however +the configuration dictionary's setting will override this input to allow for project-level +configurations to run as expected. Below, we can see some of the cable lengths differ slightly due +to the methodology of converting the WGS-84coordinates to relative points, however the spacing is +maintained, and we can see that this is still the Dudgeon windfarm. + +```{code-cell} ipython3 +array_distance = CustomArraySystemDesign(config, distance=True) +array_distance.run() +array_distance.plot_array_system(show=True) +``` + +Overall, the cabling cost is highly similar, with the difference being attributed to the method +to convert the WGS-84 coordiantes to relative coordinates. + +```{code-cell} ipython3 +print(f"{'Cable Type':<16} | {'Cost in USD (lat,lon)':>20} | {'Cost in USD (dist_lat,dist_lon)':>15}") +for (cable1, cost1), (cable2, cost2) in zip(array.cost_by_type.items(), array_distance.cost_by_type.items()): + print(f"{cable1:<16} | ${cost1:>20,.2f} | ${cost2:>15,.2f}") + +print(f"{'Total':<16} | ${array.total_cable_cost:>20,.2f} | ${array_distance.total_cable_cost:>15,.2f}") +``` + +(case_4)= +## Case 4: Site-Wide Cable Length Modifications + +To account for exclusion zones from rocky soil or other seabed conditions, we use the +`average_exclusion_percent` input in the `array_system_design` configuration section. This exclusion +will be applied to all cable sections, so it's important to account for this when modeling +additional cable lengths. + +In the +[`library/cables/example_custom_array_exclusions.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_exclusions.yaml) +configuration, a 4.8% exclusion is applied to the entire farm. When plotting farms with exclusion +zones, they will not be shown since we are not mapping the true cable path, simply the connections +between turbines. In this case, we can also a modest increase in cabling costs resulting from the +additional cable required to account for the exclusion zones. + +```{code-cell} ipython3 +config = library.extract_library_specs("config", "example_custom_array_exclusions") +pprint(config) + +array_exclusion = CustomArraySystemDesign(config) +array_exclusion.run() +``` + +```{code-cell} ipython3 +print(f"{'Cable Type':<16}| {'Cost in USD':>15}") +for cable, cost in array_exclusion.cost_by_type.items(): + print(f"{cable:<16}| ${cost:>15,.2f}") + +print(f"{'Total':<16}| ${array_exclusion.total_cable_cost:>15,.2f}") +``` + +(case_5)= +## Case 5: Custom Cable Lengths + +If we look at the map in the +[Call to Mariners](http://dudgeonoffshorewind.co.uk/news/notices/Dudgeon%20-%20Notice%20to%20Mariners%20wk25.pdf) +there are different sized exclusions in the cables, so for this example we'll change the distances +from [Case 4](#case_4) to have more variation by using the `cable_length` column of the +`location_data` CSV. In addition, we will utilize the `bury_speed` column to demonstrate how these +columns will be used. Please note this work was performed outside the example, and we will only +show the resulting configurations. + +For this example, half of the windfarm will have different soil condition, so we will use our proxy: +`bury_speed` by modifying the burial speed to be fast (0.5 km/h) and slow (0.05 km/hr), +respectively, to account for sandy soil and rocky soil. The purpose of this is for passing through +customized parameters in the design phase to be utilized in the installation phase as will be seen +in the final two examples. + +```{code-cell} ipython3 +config = library.extract_library_specs("config", "example_custom_array_custom") +pprint(config) + +array_custom = CustomArraySystemDesign(config) +array_custom.run() +``` + +Note that there are now cable lengths defined as well as burial speeds for the installation phase. + +```{code-cell} ipython3 +array_custom.location_data +``` + +Once again, the cabling costs have increased. + +```{code-cell} ipython3 +print(f"{'Cable Type':<16}| {'Cost in USD':>15}") +for cable, cost in array_custom.cost_by_type.items(): + print(f"{cable:<16}| ${cost:>15,.2f}") + +print(f"{'Total':<16}| ${array_custom.total_cable_cost:>15,.2f}") +``` + +(running)= +## Incorporating Custom Array Designs Into `ProjectManager` + +Using cases 2, 3, 4, and 5 we will demonstrate the project-wide effects from differing cabling layouts. + +### Setting Up The Cases + +Using the +[`library/cables/example_array_cable_install.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_array_cable_install.yaml) +configuration as a base configuration, we'll create a new configuration for each of the cases +using each case's `design_result` as the `array_system` values. + +```{code-cell} ipython3 +base_config = library.extract_library_specs("config", "example_array_cable_install") + +array_case2 = deepcopy(base_config) +array_case2["array_system"] = array.design_result["array_system"] + +array_case3 = deepcopy(base_config) +array_case3["array_system"] = array_distance.design_result["array_system"] + +array_case4 = deepcopy(base_config) +array_case4["array_system"] = array_exclusion.design_result["array_system"] + +array_case5 = deepcopy(base_config) +array_case5["array_system"] = array_custom.design_result["array_system"] + +sim2 = ArrayCableInstallation(array_case2) +sim3 = ArrayCableInstallation(array_case3) +sim4 = ArrayCableInstallation(array_case4) +sim5 = ArrayCableInstallation(array_case5) +``` + +### Run And Inspect The Simulation Results + +We can see that both the installation cost and the time required to complete the installations have +all increased here, corresponding to the increased cable lengths and changes to the burial speeds +defined above. + +```{code-cell} ipython3 +names = ("straight-line distance", "distance-based coordinates", "with exclusions", "custom") +simulations = (sim2, sim3, sim4, sim5) + +print(f"{'Simulation':<26} | {'Cost (in USD)':>14} | {'Time (in hours)':>16}") +for name, simulation in zip(names, simulations): + simulation.run() + cost = simulation.installation_capex + time = simulation.total_phase_time + print(f"{name:<26} | ${cost:>13,.2f} | {time:>16,.0f}") +``` + +(project_manager)= +### Incorporating Case 5 Into `ProjectManager` + +We will now incorporate the desgin settings from [Case 5](#case_5) to demonstrate incorporation +of the custom array design tooling into `ProjectManager`. This example will use the +[`library/cables/example_custom_array_project_manager.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_project_manager.yaml) +configuration. + +```{code-cell} ipython3 +config = library.extract_library_specs("config", "example_custom_array_project_manager") +config["array_system_design"]["location_data"] = library.extract_library_specs( + "cables", config["array_system_design"]["location_data"], file_type="csv" +) +config +``` + +Below, we can see that the results coming from the `ProjectManager` are the same as the additive +results of running each phase separately. + +```{code-cell} ipython3 +project = ProjectManager(config) +project.run() + +total = array_custom.total_cable_cost + sim5.installation_capex +print(f"Custom Design | ${array_custom.total_cable_cost:>13,.2f}") +print(f"Custom Installation | ${sim5.installation_capex:>13,.2f}") +print(f"Total Custom Cost | ${total:>13,.2f}") +print(f"Project Manager Cost | ${project.bos_capex:>13,.2f}") +``` diff --git a/docs/topical_guides/export_cable_system.md b/docs/topical_guides/export_cable_system.md new file mode 100644 index 00000000..886bae68 --- /dev/null +++ b/docs/topical_guides/export_cable_system.md @@ -0,0 +1,168 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# HVAC vs HVDC Systems + +Technology decisions have an impact on a wind project's CapEx. This example will provide a basic +setup to compare how different project sizes, export cable types (HVDC or HVDC), distances to shore, +and etc. effect project costs. Instead of other guides' approach of using the `ExportSystemDesign`, +this will highlight the use of the `ElectricalDesign` model that co-designs the substation +and export cabling system. + +```{code-cell} ipython3 +from copy import deepcopy + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from ORBIT.phases.design import ElectricalDesign +from ORBIT import ProjectManager, ParametricManager + +# Apply thousands separators and no decimals to floats +pd.options.display.float_format = '{:,.0f}'.format +``` + +## Setup The Models + +Here we will setup a base configuration for use with `ProjectManager` plus additional configurations +for running in the `ParametericManager` to look at the cost tradeoffs in export cable types +depending on project size. +Config must include all required variables except those you plan to vary. In this example, we will be manually vary the cable type and then use the `ParametricManager` to vary cable type and plant capacity. + +```{code-cell} ipython3 +base_config = { + "export_cable_install_vessel": "example_cable_lay_vessel", + "site": { + "distance": 100, + "depth": 20, + "distance_to_landfall": 50, + }, + "plant": { + "capacity": 1000, + }, + "turbine": "12MW_generic", + "oss_install_vessel": "example_heavy_lift_vessel", + "feeder": "future_feeder", + "design_phases": [ + "ElectricalDesign", + ], + "install_phases": [ + "ExportCableInstallation", + "OffshoreSubstationInstallation", + ], +} +``` + +Now we can create an HVAC and HVDC variation of the `base_config` + +```{code-cell} ipython3 +hvac_config = deepcopy(base_config) +hvac_config["export_system_design"] = {"cables": "XLPE_1000mm_220kV"} + +hvdc_config = deepcopy(base_config) +hvdc_config["export_system_design"] = {"cables": "HVDC_2000mm_320kV"} + + +hvac_project = ProjectManager(hvac_config) +hvac_project.run() + +hvdc_project = ProjectManager(hvdc_config) +hvdc_project.run() +``` + +## Compare the Results + +```{code-cell} ipython3 +print(f"HVAC CapEx per kW: ${hvac_project.total_capex_per_kw:,.2f} ") +print(f"HVDC CapEx per kW: ${hvdc_project.total_capex_per_kw:,.2f} ") +``` + +```{code-cell} ipython3 +capex_df = ( + pd.DataFrame( + [*hvac_project.capex_breakdown.items()], + columns=["Category", "HVAC CapEx"] + ).set_index("Category") + .join( + pd.DataFrame( + [*hvdc_project.capex_breakdown.items()], + columns=["Category", "HVDC CapEx"] + ).set_index("Category") + ) +) +capex_df +``` + +## Setup The Parametric Runs + +From the two base cases above, we see that HVDC cables are more cost effective than HVAC by +roughly 30%. However, the offshore substation (OSS) is over three times the cost of an HVAC OSS. +To compare this sensitivity and see if there is an point that these technologies cross we'll use +the `ParametricManager` and sweep each cable for a range of plant capacities. + +```{code-cell} ipython3 +parameters = { + "export_system_design.cables": ["XLPE_1000mm_220kV", "HVDC_2000mm_320kV"], + "plant.capacity": np.arange(100, 2100, 100), +} + +results = { + "cable_cost": lambda run: run.total_cable_cost, + "oss_cost": lambda run: run.substation_cost, + "num_cables": lambda run: run.num_cables, + "num_substations": lambda run: run.num_substations, +} +``` + +```{code-cell} ipython3 +parametric = ParametricManager( + base_config, parameters, results, module = ElectricalDesign, product=True +) +parametric.run() +``` + +## Compare the Cost vs Capacity Trade Off + +The inflection point of the below graph shows that for a project that has less than 700 MW of +capacity, HVAC is more cost effective, and for projects greater than 700 MW should, HVDC is more +cost effective. + +```{code-cell} ipython3 +df = pd.DataFrame(parametric.results) + +fig = plt.figure(figsize=(6,4), dpi=200) +ax = fig.subplots(1) + +hvac_df = df[df["export_system_design.cables.XLPE_1000mm_220kV.name"] == "XLPE_1000mm_220kV"] +hvdc_df = df[df["export_system_design.cables.HVDC_2000mm_320kV.name"] == "HVDC_2000mm_320kV"] + +ax.plot( + hvac_df["plant.capacity"], + (hvac_df["cable_cost"] + hvac_df["oss_cost"]) / 1e6, + label="HVAC" +) + +ax.plot( + hvdc_df["plant.capacity"], + (hvdc_df["cable_cost"] + hvdc_df["oss_cost"]) / 1e6, + label="HVDC", +) + +ax.set_ylabel("CapEx [$M]") +ax.set_xlabel("Capacity [MW]") +ax.legend() +ax.grid() + +fig.tight_layout() +``` diff --git a/docs/topical_guides/fixed_bottom_installations.md b/docs/topical_guides/fixed_bottom_installations.md new file mode 100644 index 00000000..89cb80b1 --- /dev/null +++ b/docs/topical_guides/fixed_bottom_installations.md @@ -0,0 +1,266 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Fixed-Bottom Substructure Installation Models in ORBIT + +This guide walks through the use of three separate substructure and turbine installation methods +listed below. All configuration files for this guide can be found in the +[`examples/configs/`](https://github.com/NLRWindSystems/ORBIT/tree/main/examples/configs) +folder. + +1. Separate monopile and turbine installation using a heavy lift vessel (HLV) for the monopiles + and wind turbine installation vessel (WTIV) for the turbines. +2. Onshore assembly of the gravity-based foundation (GBF) and turbine, tow out and joint + installation at site. +3. Tow-out of the GBF and turbine installation using the WTIV. + +First, we'll import the required libraries and functionality, and initialize any common variables. + +```{code-cell} ipython3 +import copy +from pprint import pprint +from pathlib import Path + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import matplotlib.ticker as ticker + +from ORBIT import ProjectManager, load_config + +# Apply thousands separators and no decimals to floats +pd.options.display.float_format = '{:,.0f}'.format + +# Set the example path for use in the docs and standalone examples usage +here = Path(".").resolve() +example_path = here.parents[1] / "examples" if here.stem == "topical_guides" else here + +weather = pd.read_csv( + example_path / "data/example_weather.csv", parse_dates=["datetime"] +).set_index("datetime") +``` + +## Load The Configurations + +Each of cases 1 and 2 have their own configuration file, however the third case is highly similar +to Case 2, so we will make a distinct copy of it, and add the turbine installation phase to +indicate the separate substructure and turbine installations. + +```{code-cell} ipython3 +case1_config = load_config(example_path / "configs/example_separate_monopile_turbine_vessel.yaml") +case2_config = load_config(example_path / "configs/example_gravity_based_project.yaml") + +case3_config = copy.deepcopy(case2_config) +case3_config["wtiv"] = "example_wtiv" +case3_config["install_phases"]["TurbineInstallation"] = 0 +``` + +The primary differences between these projects deal with the installation strategies, and +required design stages to support them. Below, we note these differences, but highlighting +the installation phases that will be modeled. + +Note that the `TurbineInstallation` model is only required for projects involving a WTIV (Case 1 +and 2). The `GravityBasedInstallation` model offers flexibility so that if a WTIV is not specified +in the configuration file, then it models the tow-out of a fully assembled substructure and turbine; +and, if a WTIV is present, it models the tow-out of the substructure alone with discrete GBF +and turbine installation phases. + +```{code-cell} ipython3 +print(f"Monopile and Turbine Installation (Heavy Lift Vessel for Monopile Installation, WTIV for Turbine Installation)") +print(f"Install phases: {list(case1_config['install_phases'].keys())}\n") +print(f"Gravity-Based Foundation Intallation (Substructure-Turbine Assembly Tow-out, no WTIV)") +print(f"Install phases: {list(case2_config['install_phases'].keys())}\n") +print(f"Gravity-Based Foundation and Turbine Intallation (Substructure Tow-out, WTIV for Turbine Installation)") +print(f"Install phases: {list(case3_config['install_phases'].keys())}\n") +``` + +## Run The Three Cases + +This project is always being modeled with the example weather project supplied that is representative of US East Coast wind farm locations. + +```{code-cell} ipython3 +case1_project = ProjectManager(case1_config, weather=weather) +case1_project.run() + +case2_project = ProjectManager(case2_config, weather=weather) +case2_project.run() + +case3_project = ProjectManager(case3_config, weather=weather) +case3_project.run() +``` + +## Results Comparison + +### CapEx Breakdown + +```{code-cell} ipython3 +# The breakdown of project costs by module is available at 'capex_breakdown' + +df = pd.DataFrame({ + 'Monopiles + WTIV': pd.Series(case1_project.capex_breakdown), + 'GBF-Turbine Assembly Tow-out': case2_project.capex_breakdown, + 'GBF Tow-out + WTIV': pd.Series(case3_project.capex_breakdown) +}).fillna(0) +df.loc['Total'] = df.sum() +df.index.name = "CapEx Component" +df +``` + +```{code-cell} ipython3 +def plot_capex_comparison(df, num_turbines, project_capacity_mw, top_limit=4000): + # Reformat the data for easier plotting + ix_order = ["Monopiles + WTIV", "GBF-Turbine Assembly Tow-out", "GBF Tow-out + WTIV"] + df = df.copy() + df.columns = df.columns.str.strip() + df /= 1e6 + df = df.drop("Total").T.loc[ix_order] + + capacity_kw = project_capacity_mw * 1000 + + # Colors for components + colors = plt.get_cmap("tab20").colors + component_order = df.columns.tolist() + color_map = {component: colors[i % len(colors)] for i, component in enumerate(component_order)} + + fig = plt.figure(figsize=(14, 14)) + ax = fig.add_subplot(111) + + bar_width = 0.7 # Slightly thinner bars + bottoms = np.zeros(len(df)) + for component in component_order: + vals = df[component].values + bars = ax.bar( + df.index, + vals, + bottom=bottoms, + width=bar_width, + color=color_map[component], + edgecolor="black", + linewidth=0.8, + label=component + ) + bottoms += vals + + # Add text inside each stacked segment if $/kW >= 40 + for i, val_musd in enumerate(vals): + if val_musd == 0: + continue + val_usd = val_musd * 1e6 + val_per_kw = val_usd / capacity_kw + val_per_wtg_musd = val_musd / num_turbines + + if val_per_kw >= 40: + y_pos = bars[i].get_y() + bars[i].get_height() / 2 + text = ( + f"${val_musd:,.1f}M | " + f"${val_per_kw:,.0f}/kW | " + f"${val_per_wtg_musd:,.2f}M/WTG" + ) + ax.text( + i, + y_pos, + text, + ha="center", + va="center", + fontsize=8, + color="black" + ) + + # Add total values text on top of bars + total_musd = df.sum(axis=1) + for i, total in enumerate(total_musd): + total_usd = total * 1e6 + total_per_kw = total_usd / capacity_kw + total_per_wtg_musd = total / num_turbines + text = ( + f"Total:\n" + f"${total:,.1f}M | " + f"${total_per_kw:,.0f}/kW | " + f"${total_per_wtg_musd:,.2f}M/WTG" + ) + ax.text( + i, + total * 1.005, + text, + ha="center", + va="bottom", + fontsize=9, + color="black", + fontweight="bold" + ) + + # Format y-axis ticks with commas + ax.yaxis.set_major_formatter(ticker.StrMethodFormatter("{x:,.0f}")) + + ax.set_ylabel("CapEx ($ Millions)", fontweight="bold") + ax.set_xlabel("Installation Strategy", fontweight="bold") + ax.set_title("CapEx Breakdown by Component") + ax.set_ylim(0, top_limit) + ax.grid(axis="y", linestyle="--", alpha=0.7) + + ax.legend(title="CapEx Component", ncols=5, loc="upper center") + fig.tight_layout() +``` + +```{code-cell} ipython3 +plot_capex_comparison(df, 50, 750, top_limit=4500) +``` + +### Substructure and Turbine Installation CapEx Breakdown + +```{code-cell} ipython3 +sub_turb_install_df = df.loc[["Substructure Installation", "Turbine Installation"]] +sub_turb_install_df.loc["Total"] = sub_turb_install_df.sum(axis=0) +sub_turb_install_df +``` + +### Comparing Installation Timing + +Now we can compare the actual installation phase timing to demonstrate the effects of using +differing numbers of vessels and installation strategies. Notice that for both Case 1 and 3 +the installations all start at the same date, which is likely unrealistic in practice as each +stage cannot happen at a single turbine at the same time, nor can the installation of scouring +protection be installed prior to the monopiles being installed. + +```{code-cell} ipython3 +fig = plt.figure(figsize=(10, 4)) +ax = fig.add_subplot(111) + +cases = ["Monopiles + WTIV", "GBF-Turbine Assembly Tow-out", "GBF Tow-out + WTIV"] +case1_df = pd.DataFrame.from_dict(case1_project.phase_dates).T +case1_df = case1_df.loc[["MonopileInstallation", "ScourProtectionInstallation", "TurbineInstallation"]] +case1_df.start = pd.to_datetime(case1_df.start) +case1_df.end = pd.to_datetime(case1_df.end) +case1_df = case1_df.rename(index={ix: f"{cases[0]}\n{ix}" for ix in case1_df.index}) + +case2_df = pd.DataFrame.from_dict(case2_project.phase_dates).T +case2_df = case2_df.loc[["GravityBasedInstallation"]] +case2_df.start = pd.to_datetime(case2_df.start) +case2_df.end = pd.to_datetime(case2_df.end) +case2_df = case2_df.rename(index={ix: f"{cases[1]}\n{ix}" for ix in case2_df.index}) + +case3_df = pd.DataFrame.from_dict(case3_project.phase_dates).T +case3_df = case3_df.loc[["GravityBasedInstallation", "TurbineInstallation"]] +case3_df.start = pd.to_datetime(case3_df.start) +case3_df.end = pd.to_datetime(case3_df.end) +case3_df = case3_df.rename(index={ix: f"{cases[2]}\n{ix}" for ix in case3_df.index}) + +ax.barh(y=case3_df.index, width=case3_df.end - case3_df.start, left=case3_df.start); +ax.barh(y=case2_df.index, width=case2_df.end - case2_df.start, left=case2_df.start); +ax.barh(y=case1_df.index, width=case1_df.end - case1_df.start, left=case1_df.start); + +ax.grid(axis="x") +ax.set_axisbelow(True) +ax.set_xlim(pd.to_datetime("2009-10-21"), pd.to_datetime("2010-07")) +fig.tight_layout() +``` diff --git a/docs/topical_guides/index.md b/docs/topical_guides/index.md new file mode 100644 index 00000000..2f93320f --- /dev/null +++ b/docs/topical_guides/index.md @@ -0,0 +1,13 @@ +(topical-guides)= +# Topical Guides + +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/NLRWindSystems/ORBIT/main?urlpath=%2Fdoc%2Ftree%2Fexamples) + +All of the topical guides can be found as Jupyter Notebook versions in the +[ORBIT repository examples folder](https://github.com/NLRWindSystems/ORBIT/tree/main/examples) and +can be run on [Binder](https://mybinder.org/v2/gh/NLRWindSystems/ORBIT/main?urlpath=%2Fdoc%2Ftree%2Fexamples). + +## Tutorials and Topical Guides Execution Status + +```{nb-exec-table} +``` diff --git a/docs/topical_guides/supply_chains.md b/docs/topical_guides/supply_chains.md new file mode 100644 index 00000000..aab53ea3 --- /dev/null +++ b/docs/topical_guides/supply_chains.md @@ -0,0 +1,204 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Modeling Supply Chains + +In this example we will model the effects of the supply chain on substructure fabrication on +jacket-pile installations. For this example we will consider the following three cases: + +1. Jacket installations with unlimited port storage. +2. Insufficient jacket fabrication to match installation rates. +3. Increased jacket fabrication to keep pace with installation rates. + +```{code-cell} ipython3 +from copy import deepcopy +from pathlib import Path + +import pandas as pd +import matplotlib.pyplot as plt + +from ORBIT import ProjectManager, load_config +from ORBIT.phases.install import MonopileInstallation, JacketInstallation + +# Set the example path for use in the docs and standalone examples usage +here = Path(".").resolve() +example_path = here.parents[1] / "examples" if here.stem == "topical_guides" else here + +weather = pd.read_csv( + example_path / "data/example_weather.csv", parse_dates=["datetime"] +).set_index("datetime") +``` + +## Preparing The Cases + +Here, we will load the +[`examples/configs/example_fixed_project.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/examples/configs/example_fixed_project.yaml) +configuration and modify it to run a jacket installation process. Please note that there is no +jacket design model in ORBIT, so we must provide the basic jacket parameterizations for the +design result. + +```{code-cell} ipython3 +base_jacket_config = load_config("configs/example_fixed_project.yaml") +base_jacket_config["jacket"] = { + "diameter": 10, + "height": 100, + "length": 100, + "mass": 100, + "deck_space": 100, + "unit_cost": 1e6 +} + +slow_prod_config = deepcopy(base_jacket_config) +slow_prod_config["jacket_supply_chain"] = { + "enabled": True, + "substructure_delivery_time": 500, + "num_substructures_delivered": 2, +} + +increased_prod_config = deepcopy(base_jacket_config) +increased_prod_config["jacket_supply_chain"] = { + "enabled": True, + "substructure_delivery_time": 250, + "num_substructures_delivered": 2, +} + +case1_project = JacketInstallation(base_jacket_config, weather=weather) +case2_project = JacketInstallation(slow_prod_config, weather=weather) +case3_project = JacketInstallation(increased_prod_config, weather=weather) + +case1_project.run() +case2_project.run() +case3_project.run() +``` + +## Comparing Results + +### Installation Time + +```{code-cell} ipython3 +print(f"Case 1 Installation Time: {case1_project.total_phase_time / 24:.1f} days") +print(f"Case 2 Installation Time: {case2_project.total_phase_time / 24:.1f} days") +print(f"Case 3 Installation Time: {case3_project.total_phase_time / 24:.1f} days") +``` + +### Installation Timing + +Below, we plot the timing of jacket deliveries and their subsequent installations without +considering vessel logistic delays. Note the inclement weather around the 400th day of +Case 2's installation simulation. + +```{code-cell} ipython3 +case2_df = pd.DataFrame(case2_project.env.actions) +case2_deliveries = case2_df.loc[case2_df["action"].str.contains("Delivered"), ["action", "time"]] +case2_deliveries["number"] = case2_deliveries["action"].str.split(" ").str[1].astype(int) + +case2_installs = case2_df.loc[case2_df["action"].str.contains("Grout Jacket"), ["action", "time"]] +case2_installs["number"] = 1 + +case2_deliveries.time /= 24.0 +case2_installs.time /= 24.0 + + +case3_df = pd.DataFrame(case3_project.env.actions) +case3_deliveries = case3_df.loc[case3_df["action"].str.contains("Delivered"), ["action", "time"]] +case3_deliveries["number"] = case3_deliveries["action"].str.split(" ").str[1].astype(int) + +case3_installs = case3_df.loc[case3_df["action"].str.contains("Grout Jacket"), ["action", "time"]] +case3_installs["number"] = 1 + +case3_deliveries.time /= 24.0 +case3_installs.time /= 24.0 +``` + +```{code-cell} ipython3 +fig = plt.figure(figsize=(8,4), dpi=200) +ax = fig.add_subplot(111) + +ax.scatter( + case2_deliveries["time"], + case2_deliveries["number"].cumsum(), + marker="o", + c="tab:blue", + label="Substructure Delivered - Slow Fabrication" +) +ax.scatter( + case2_installs["time"], + case2_installs["number"].cumsum(), + marker="x", + c="tab:blue", + label="Completed Installation - Slow Fabrication" +) + +ax.scatter( + case3_deliveries["time"], + case3_deliveries["number"].cumsum(), + marker="o", + c="tab:orange", + label="Substructure Delivered - Increased Fabrication" +) +ax.scatter( + case3_installs["time"], + case3_installs["number"].cumsum(), + marker="x", + c="tab:orange", + label="Completed Installation - Increased Fabrication" +) + +ax.set_xlim(0, ax.get_xlim()[1]) +ax.set_ylim(0, 60) + +ax.set_xlabel("Simulation Time (Days)") +ax.set_ylabel("Substructures") + +ax.legend() +ax.grid() +fig.tight_layout() +``` + +### Port Storage + +```{code-cell} ipython3 +fig = plt.figure(figsize=(8,4), dpi=200) +ax = fig.add_subplot(111) + +case2_installs_neg = case2_installs.copy() +case2_installs_neg["number"] *= -1 +case2_total = pd.concat([case2_deliveries, case2_installs_neg]).sort_values('time') +case2_total['storage'] = case2_total['number'].cumsum() + +case3_installs_neg = case3_installs.copy() +case3_installs_neg["number"] *= -1 +case3_total = pd.concat([case3_deliveries, case3_installs_neg]).sort_values('time') +case3_total['storage'] = case3_total['number'].cumsum() + +ax.plot( + case2_total['time'], + case2_total['storage'], + label="Storage Required - Slow Fabrication" +) +ax.plot( + case3_total['time'], + case3_total['storage'], + label="Storage Required - Increased Fabrication" +) + +ax.set_xlim(0, ax.get_xlim()[1]) +# ax.set_ylim(0, 5) + +ax.axhline(4, ls="--", lw=0.5, c='k', label="Theoretical Port Storage Limit) + +ax.set_xlabel("Simulation Time (h)") +ax.set_ylabel("Substructures") + +ax.legend() +``` diff --git a/docs/tutorials/available_outputs.md b/docs/tutorials/available_outputs.md new file mode 100644 index 00000000..02ae515f --- /dev/null +++ b/docs/tutorials/available_outputs.md @@ -0,0 +1,429 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +(outputs-tutorial)= +# Available Outputs + +Using `ProjectManager` to run a collection of ORBIT design and installation models representing a +partial or complete offshore wind project installation enables a variety of project-level metrics +to be calculated that are not available in individual models. The outputs of each model are also +made directly available by access to the model itself or in aggregate form for all project-level +outputs available via the `ProjectManager` API. + +## Model Setup + +Before diving in, we will import all the packages and functionality we'll need, and run a project +that can highlight the project's metrics. + +```{code-cell} ipython3 +from pathlib import Path +from pprint import pprint + +import pandas as pd +import matplotlib.pyplot as plt + +from ORBIT import ProjectManager, load_config + +# Ensure the correct examples directory is used when running this in docs or in examples +here = Path(".").resolve() +example_dir = here.parents[1] / "examples" if here.stem == "tutorials" else here + +config = load_config(example_dir / "configs/example_fixed_project.yaml") +project = ProjectManager(config) +project.run() +``` + +## Project Details + +### Model Design Results + +The `design_results` object is dictionary mapping between phase names and the dictionary of outputs +used by `ProjectManger` to pass into other phases or calculate further metrics. + +```{code-cell} ipython3 +pprint(project.design_results) +``` + +### Project Parameterizaions + +Below is brief example showing the basic project parameterizations that are availabe. + +- `num_turbines`: the number of turbines. +- `turbine_rating`: the rating of an individual turbine, in MW. +- `capacity`: The total project capacity, in MW. +- `project_time`: The total project installation time, including all delays, in hours. + +```{code-cell} ipython3 +print(f"Number of turbines: {project.num_turbines}") +print(f"Turbine Rating: {project.turbine_rating:.2f}") +print(f"Project Capacity (MW): {project.capacity:,.2f}") +print(f"Project Installation Time (days): {project.project_time / 24:,.1f}") +``` + +### Event Timing + +The `installation_time` provides the sum total installation time of all phases, in hours, without +accounting for timing overlaps, whereas the `project_days` provides the total number of days between +the start and completion of the project. + +```{code-cell} ipython3 +print(f"Total Installation Time: {project.installation_time / 24:.0f} days") +print(f"Total Elapsed Time: {project.project_days} days") +``` + +## All Outputs At Once + +The `outputs` method provides a dictionary mapping all the major project costs and timing details +in a single view. There are two parameters that can be passed to provide further details that will +not be demonstrated. For further details on any of these metrics, please refer to that metric's +section. + +- `include_logs`: include the full project installation action logs if `True`. +- `npv_details`: include the `cash_flow`, `monthly_revenue`, and `monthly_expenses`, if `True`. + +```{code-cell} ipython3 +pprint(project.outputs()) +``` + +## CapEx + +This section will start from the total CapEx, and work backwards demonstrating how to access +the various CapEx breakouts and breakdowns. + +### Total CapEx + +The `total_capex` is the sum of the BOS, soft, and project CapEx numbers (details in following +sections). This represents the complete project costs including all upfront costs, financing, +procurement and installation of BOS subsystems and the procurement costs of the turbines. + +:::{note} +ORBIT doesn't explicity model the procurement of turbines, however the Turbine CapEx is included +within `project.total_capex`. To configure the cost of the turbines, `turbine_capex` can be passed +into the `project_parameters` section of an ORBIT configuration. +::: + +```{code-cell} ipython3 +print(f"Total CapEx (millions, USD): {project.total_capex / 1e6:,.2f}") +print(f"Total CapEx (USD) per kW: {project.total_capex_per_kw:,.2f}") +``` + +### Categorical CapEx Breakdowns + +The `capex_breakdown` property provides a dictionary of all the procurement, installation, soft, +and project costs associated with a project. Below we will print out the dictionary keys and +the values in millions USD. + +```{code-cell} ipython3 +for name, capex in project.capex_breakdown.items(): + print(f"{name:>35}: ${capex / 1e6:6,.2f} (millions, USD)") +``` + +Like in the previous examples, the `capex_breakdown_per_kw` will provide each category's associated +costs as a capacity normalized value. + +```{code-cell} ipython3 +for name, capex in project.capex_breakdown_per_kw.items(): + print(f"{name:>35}: ${capex:8,.2f} (USD/kW)") +``` + +### BOS CapEx + +The balance-of-system (BOS) CapEx (`bos_capex`) is the sum of the system and installation CapEx, +and is one of the core outputs of the ORBIT module. + +```{code-cell} ipython3 +print(f"BOS CapEx (millions, USD): {project.bos_capex / 1e6:,.2f}") +print(f"BOS CapEx (USD) per kW: {project.bos_capex_per_kw:,.2f}") +``` + +### System CapEx + +The `system_capex` property provides the total procurement costs for all modeled systems, whether +the costs were user inputs, or the results of design models. This value will not change unless +the design or plant's properties (e.g., distance to shore, depth, or number of turbines) change. + +In addition, `system_capex_per_kw` provies the capacity-normalized CapEx for the project. + +```{code-cell} ipython3 +print(f"System (procurement) CapEx (millions, USD): {project.system_capex / 1e6:,.2f}") +print(f"System (procurement) CapEx (USD) per kW: {project.system_capex_per_kw:,.2f}") +``` + +To view the individual component system costs, users can inspect the `system_costs` dictionary where +costs are summarized by each modeled or input system. + +```{code-cell} ipython3 +for name, capex in project.system_costs.items(): + print(f"{name:>35}: ${capex / 1e6:6,.2f} (millions, USD)") +``` + +### Installation Capex + +Installation CapEx is a dynamic result based on the installation simulation and the timing +associated with each subsystem installation, day rates of any vessels/ports and any accrued weather +delays. + +In addition, `installation_capex_per_kw` provies the capacity-normalized CapEx for the project. +Below we will print out the dictionary keys and the values in millions USD. + +```{code-cell} ipython3 +print(f"Installation CapEx (millions, USD): {project.installation_capex / 1e6:,.2f}") +print(f"Installation CapEx (USD) per kW: {project.installation_capex_per_kw:,.2f}") +``` + +To view the individual component installation costs, users can inspect the `installation_costs` +dictionary where costs are summarized by each modeled system. Below we will print out the dictionary +keys and the values in millions USD. + +```{code-cell} ipython3 +for name, capex in project.installation_costs.items(): + print(f"{name:>35}: ${capex / 1e6:6,.2f} (millions, USD)") +``` + +### Turbine CapEx + +The `turbine_capex` is directly derived from the user inputs, and if none are provided, it is +assumed to be $1,300 USD/kW. + +```{code-cell} ipython3 +print(f"Turbine CapEx (millions, USD): {project.turbine_capex / 1e6:,.2f}") +print(f"Turbine CapEx (USD) per kW: {project.turbine_capex_per_kw:,.2f}") +``` + +### Project CapEx + +Project CapEx (`project.project_capex`) includes the costs associated with +the lease area, the development of the construction operations plan and any +environmental review and other upfront project costs. There are default values +for all of these subcategories, however the values can also be overridden in the +`project_parameters` subdict. + +```{code-cell} ipython3 +print(f"Turbine CapEx (millions, USD): {project.project_capex / 1e6:,.2f}") +print(f"Turbine CapEx (USD) per kW: {project.project_capex_per_kw:,.2f}") +``` + +### Soft CapEx + +Soft CapEx (`project.soft_capex`) represents additional project level costs +associated with commissioning, decommissioning and financing of the project. +The cost factors can be input in the `project_parameters` subdict of an ORBIT +configuration. The default cost factors for these categories are derived from the +[2018 Cost of Wind Energy Review](https://www.nlr.gov/docs/fy20osti/74598.pdf). + +```{code-cell} ipython3 +print(f"Soft CapEx (millions, USD): {project.soft_capex / 1e6:,.2f}") +print(f"Soft CapEx (USD) per kW: {project.soft_capex_per_kw:,.2f}") +``` + +The soft CapEx can also be broken down using both the `soft_capex_breakdown` and the `capex_detailed_soft_capex_breakdown`, which also provide a capacity-noramlized variation by adding +`_per_kw` to the end of either (not shown in this demonstration). The primary difference (as shown +below) is that the `capex_detailed_soft_capex_breakdown` metric provides the capex breakdown with +the additional soft capex breakdown. + +```{code-cell} ipython3 +for name, capex in project.soft_capex_breakdown.items(): + print(f"{name:>35}: ${capex / 1e6:8,.2f} (millions,USD)") +``` + +```{code-cell} ipython3 +for name, capex in project.capex_detailed_soft_capex_breakdown.items(): + print(f"{name:>35}: ${capex / 1e6:8,.2f} (millions,USD)") +``` + +The soft CapEx values are also available as independent values: + +- `construction_insurance_capex` +- `commissioning_capex` +- `decommissioning_capex` +- `procurement_contingency_capex` +- `installation_contingency_capex` +- `construction_financing_capex` + +```{code-cell} ipython3 +print(f"Construction Insurance CapEx (millions, USD): {project.construction_insurance_capex() / 1e6:,.2f}") +print(f"Commissioning CapEx (millions, USD): {project.commissioning_capex() / 1e6:,.2f}") +print(f"Decommissioning CapEx (millions, USD): {project.decommissioning_capex() / 1e6:,.2f}") +print(f"Procurement Contingency CapEx (millions, USD): {project.procurement_contingency_capex() / 1e6:,.2f}") +print(f"Installation Contingency CapEx (millions, USD): {project.installation_contingency_capex() / 1e6:,.2f}") +print(f"Construction Financing CapEx (millions, USD): {project.construction_financing_capex() / 1e6:,.2f}") +``` +### All Other CapEx Categories + +#### Supply Chain CapEx + +The supply chain CapEx (`supply_chain_capex`) directly captures the user-provided +`supply_chain_capex` from the `project_parameters` section of the project configuration. This +value should encompass any project-level investements in supply chain development, port upgrade, +community benefit agreements, fisheries mitigation funds, community or research initiatives, and +US-built vessels. + +```{code-cell} ipython3 +print(f"Supply Chain CapEx (millions, USD): {project.supply_chain_capex / 1e6:,.2f}") +print(f"Supply Chain CapEx (USD) per kW: {project.supply_chain_capex_per_kw:,.2f}") +``` + +#### Onshore Substation CapEx + +The CapEx associated with onshore substation as prescribed by the `ElectricalDesign` + +```{code-cell} ipython3 +print(f"Turbine CapEx (millions, USD): {project.turbine_capex / 1e6:,.2f}") +print(f"Turbine CapEx (USD) per kW: {project.turbine_capex_per_kw:,.2f}") +``` + +#### Overnight CapEx + +The `overnight_capex` provides the overnight capital cost (system and turbine CapEx) of the project. + +```{code-cell} ipython3 +print(f"Overnight CapEx (millions, USD): {project.overnight_capex / 1e6:,.2f}") +``` + +## Logging + +The installation logs can produced in varying details from high-level phase start and end dates, and +all the way down to the detailed installation logics. This section will go through the methods +provided to access these data and demonstrate some simple ways of displaying it conveniently. + +### Installation Progress + +The `progress_summary` provides an aggregated view of the `progress_logs` to show the number +of completed component installations for each month in the simulation. + +```{code-cell} ipython3 +pprint(project.progress_summary) +``` + +The `project_logs` provides a list of the when a component installation was completed using the +total number of hours since the start of the simulation. + +As an example, this looks like the following: + +```python +[ + ('Offshore Substation', 88.0925357142857), + ('Turbine', 97.7933333333333), + ('Substructure', 130.14586018219498), + ('Substructure', 147.89172036438998), + ('Turbine', 150.18666666666658), + ... +] +``` + +### Phase timing + +The `phase_dates` provides access to the starting and ending time of each installation phase as +a dictionary. In the following example, we will convert this data into a Pandas DataFrame with +datetime formatting, and produce a Gantt chart to highlight where the phases occur relative to +each other. + +```{code-cell} ipython3 +df = pd.DataFrame.from_dict(project.phase_dates).T +df.start = pd.to_datetime(df.start) +df.end = pd.to_datetime(df.end) +df = df.sort_values("start", ascending=False) +df +``` + +Below, we can see the installation timing is not quite realistic given the WTIV is used for the +monopile, turbine, and OSS installations and the cabling vessel is used for both the array and +export cabling installations. For both vessels, there should not be overlapping installations +unless multiple vessels are made available for these actions. + +```{code-cell} ipython3 +fig = plt.figure(figsize=(10, 4)) +ax = fig.add_subplot(111) + +ax.barh(y=df.index, width=df.end - df.start, left=df.start); + +annotation = ( + f"Total Installation Time: {project.installation_time / 24:.0f} days\n" + f"Total Elapsed Time: {project.project_days} days" +) +ax.text( + pd.to_datetime("2010-05-06"), 3, annotation, + bbox={"boxstyle": "square", "fc": (0.9, 0.9, 0.9, 0.9), "linewidth": 0.5}, + ha="left", va="center", size=12, +) + +ax.grid(axis="x") +ax.set_axisbelow(True) +ax.set_xlim(pd.to_datetime("2009-12"), pd.to_datetime("2010-10")) +fig.tight_layout() +``` + +### Detailed Event Timing + +The `actions` property provides access to a JSON-style list of every step taken during the +installation simulation. It is highly recommended to convert this to a Pandas DataFrame or similar +for inspection. Below, we will walk through some basic filtering of these data. + +```{code-cell} ipython3 +df = pd.DataFrame(project.actions) +df.head() +``` + +Using the data frame we can filter produce vessel timing summaries for a single phase or a single +vessel, or any combination of vessels and phases. Below is a demonstration of filtering the time +spent in various activities during the monopile installation. From an operational standpoint, this +provides insight into what actions take the longest or cost the most, and can provide a means to +identify room for innovation or process efficiencies. Please see the +[project manager phase timing tutorial](#phase-dependent-timing) for more information about +customizing timing dependencies. + +```{code-cell} ipython3 +mp_install = df.loc[df.phase.eq("MonopileInstallation")] +mp_vessel_summary = ( + mp_install[["agent", "action", "duration", "cost"]] + .groupby(["agent", "action"]) + .sum() + .style + .format("{:,.2f}") +) +mp_vessel_summary +``` + +## Cash Flow and Net Present Value + +The `ProjectManager` includes a basic cash flow and net present value (NPV) model. The project must +have the array, export, and substation installation models configured for this model to be +applicable. The model will find the point in the project logs where the substation and export +cable installations were completed and where each completed string of array cables was installed. +When all three of these conditions are met, the project can begin to generate energy and produce +revenue. The revenue generation is then superimposed on the monthly spend of the installation +models for the `project.cash_flow`. Please note this assumes a fixed operational expenditure (OpEx). + +The NPV of the project can then be calculated and is available through `npv`. The underlying +financial assumptions for this model are also contained within the `project_parameters` section of +the ORBIT configuration. + +```{code-cell} ipython3 +print(f"NPV: ${project.npv / 1e6:,.2f} (millions, USD)") +``` + +Below, we highlight the first 12 months of the project cash flow. In the 10th month we can see that +there are no more installation costs, and the project produces the same values for each field +until the end of the project. + +```{code-cell} ipython3 +pd.concat( + [ + pd.DataFrame(project.monthly_opex.values(), columns=["monthly_opex"]), + pd.DataFrame(project.monthly_expenses.values(), columns=["monthly_expenses"]), + pd.DataFrame(project.monthly_revenue.values(), columns=["monthly_revenue"]), + pd.DataFrame(project.cash_flow.values(), columns=["cash_flow"]), + ], + axis=1 +).head(12).style.format("{:,.2f}") +``` diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md new file mode 100644 index 00000000..cdf34927 --- /dev/null +++ b/docs/tutorials/index.md @@ -0,0 +1,19 @@ +(tutorials)= + +# Tutorials + +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/NLRWindSystems/ORBIT/main?urlpath=%2Fdoc%2Ftree%2Fexamples) + +Welcome to the ORBIT tutorials! The following examples will cover the basic usage of ORBIT. For +backround on the project and Offshore Balance of System modeling, please see the +[introduction](#bos-intro). For more advanced examples and real world +validation cases, please see the [topical guides](#topical-guides) section of the documentation. + +All tutorials can be found as Jupyter Notebook versions in the +[ORBIT repository examples folder](https://github.com/NLRWindSystems/ORBIT/tree/main/examples) and +can be run on [Binder](https://mybinder.org/v2/gh/NLRWindSystems/ORBIT/main?urlpath=%2Fdoc%2Ftree%2Fexamples). + +## Tutorials and Topical Guides Execution Status + +```{nb-exec-table} +``` diff --git a/docs/tutorials/introduction.md b/docs/tutorials/introduction.md new file mode 100644 index 00000000..68f986e6 --- /dev/null +++ b/docs/tutorials/introduction.md @@ -0,0 +1,374 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +(intro-tutorial)= +# ORBIT Introduction + +ORBIT's CapEx modeling is comprised of the both design and installation models for a variety of +offshore wind turbine subsystems. As such, the model's core functionality are split into the +`design` and `install` model classes. The design classes are intended to model the sizing and cost +of offshore wind components and the installation modules simulate the installation of these +subcomponents in a discrete event simulation framework. This tutorial will walk through the basics +of modeling the design and then installation of the monopile, leading to the introduction of the +`ProjectManger` to orchestrate the design and installation of multiple turbine subsystems. + +To get started, we first import the required imports that will be used in this demonstration. + +```{code-cell} ipython3 +from pathlib import Path +from copy import deepcopy +from pprint import pprint + +from ORBIT import ProjectManager, load_config, save_config +from ORBIT.phases.design import MonopileDesign, design_phases +from ORBIT.phases.install import MonopileInstallation, install_phases +``` + +While this introduction will focus on the monopile design and installation to highlight working with +ORBIT, it should be noted that there are both fixed and floating substructure models. Below is an +easy way to check what models are available in ORBIT. + +```{code-cell} ipython3 +pprint(design_phases) +``` + +```{code-cell} ipython3 +pprint(install_phases) +``` + +## Configuration Basics + +Each model has a property `expected_config` that provides basic information about the required and +optional inputs for the model. Notice that for each input there is a provided data type, an +indication if the parameter is optional, and any nested dictionary configurations are fully mapped +in the same way as individal parameters. Below, we can see the expected configurations for both +the monopile design and installation classes. It should be noted that when combining complimentary +design and installation phases for a component, that the design model will provide most of the +installation inputs as a `design_result` (more details in the `ProjectManager` introduction). + +```{code-cell} ipython3 +pprint(MonopileDesign.expected_config) +``` + +```{code-cell} ipython3 +pprint(MonopileInstallation.expected_config) +``` + +### Design Models + +Design phase modules in ORBIT are intended to capture broad scaling trends for offshore wind +components and do not represent the required fidelity of a full engineering design. Please see NLR's +[WISDEM](https://github.com/NLRWindSystems/WISDEM/) if a higher fidelity turbine design model +is required. + +For the sake of illustration we will provide only the required inputs, as shown below. + +```{code-cell} ipython3 +# Filling out the config for a simple fixed bottom project: +design_config = { + "site": { + "depth": 25, + "mean_windspeed": 9.5, + }, + "plant": { + "num_turbines": 50, + }, + "turbine": { + "rotor_diameter": 220, + "hub_height": 120, + "rated_windspeed": 13, + } +} +``` + +Similar to `expected_config`, every design and installation model contains a `run` method that runs +the design or installation simulation logic. + +```{code-cell} ipython3 +monopile_design = MonopileDesign(design_config) +monopile_design.run() +print(f"Total Substructure Cost: ${monopile_design.total_cost / 1e6:,.1f} M") +pprint(monopile_design.design_result) +``` + +### Incomplete or Incorrect Configurations + +If a required input is missing, an error message will be raised with the input and it's location +within the configuration. This error message used dot-notation to show the structure of the +dictionary. Each "." represents a lower level in the dictionary such that `site.depth` means the "site" subdictionary is missing the "depth" key, value pair. + +In the example below, the `site` inputs have been removed. The following inputs will be missing: +`["site.depth", "site.mean_windspeed"]` + +```{code-cell} ipython3 +:tags: [raises-exception] + +config_error = deepcopy(design_config) +_ = config_error.pop("site") + +failed_monopile_design = MonopileDesign(config_error) +``` + +### Optional Inputs + +Optional inputs can be provided as they are available or desired in place of ORBIT's +defaults. In general ORBIT's default values are updated on annual basis to align with +the last complete year of inflationary data and commodity price indices. These values +also align with the annual [NLR Cost of Wind Energy Review](https://github.com/NatLabRockies/AnnualReportingWind/). + +```{code-cell} ipython3 +design_config = { + "site": { + "depth": 25, + "mean_windspeed": 9.5, + }, + "plant": { + "num_turbines": 50, + }, + "turbine": { + "rotor_diameter": 220, + "hub_height": 120, + "rated_windspeed": 13, + }, + + # Overriding of the design cost defaults, both in $USD/tonne + "monopile_design": { + "monopile_steel_cost": 3500, + "tp_steel_cost": 4500, + } +} + +monopile_design = MonopileDesign(design_config) +monopile_design.run() +print(f"Total Substructure Cost: ${monopile_design.total_cost / 1e6:,.2f} M") +pprint(monopile_design.design_result) +``` + +### Overriding Values from the Design Phase + +In the example above, the `MonopileDesign` phase will produce the input parameters "monopile and +"transition_piece". It is also possible to supply some of the values for these designs if they are +known, and let `MonopileDesign` fill in the rest. For example, if the user knows the dimensions of +the monopile but not the transition piece, the "monopile" dictionary can be added to the project config above: + +```{code-cell} ipython3 +design_config_custom = { + "site": { + "depth": 25, + "mean_windspeed": 9.5, + }, + "plant": { + "num_turbines": 50, + }, + "turbine": { + "rotor_diameter": 220, + "hub_height": 120, + "rated_windspeed": 13, + }, + "monopile": { + "type": "Monopile", + "mass": 800, + "length": 100, + }, +} + +monopile_design = MonopileDesign(design_config_custom) +monopile_design.run() +monopile_design_result = monopile_design.design_result +pprint(monopile_design_result) +``` + +### Installation Phases + +ORBIT's installation phases tend to require more inputs and provide implicit pathways to model +installation strategies. For instance, in the monopile installation, we can provide a "wtiv" vessel +for a single WTIV installation strategy or provide a "feeder" configuration with "num_feeders" +to model barges ferrying components to and from the site while a WTIV installs the turbines. +Additionally, supply chains and ports can be configured to model component availability and port +logistics. + +Using the output from the above example, we can add further configurations. Note that ORBIT provides +a series of default vessls in `library/vessels/` to support all possible installation strategies. +For more details on vessel configurations, please see the [vessels section](#vessels). + +```{code-cell} ipython3 +install_config = deepcopy(monopile_design_result) +install_config["wtiv"] = "example_wtiv" +install_config["feeder"] = "example_feeder" +install_config["num_feeders"] = 2 +install_config["site"] = design_config["site"] | {"distance": 70} +install_config["plant"] = design_config["plant"] +install_config["turbine"] = design_config["turbine"] + +monopile_install = MonopileInstallation(install_config) +monopile_install.run() +print(f"Total Installation Cost: ${monopile_install.installation_capex / 1e6:,.2f} M") +print(monopile_install.config.dump()) +``` + +### Loading and Saving Configurations + +In addition to writing dictionaries in a script or Notebook file, ORBIT also provides the +`load_config` and `save_config` functions to load and save configurations for easier scenario +management. In the following example, we demonstrate a hypothetical workflow loading, updating, and +saving a new monopile design configuration. + +```python +design_config = load_config("path/to/monopile_design.yaml") + +... # calculate additional properties of the monopile and update the configuration + +save_config(design_config, "path/to/new_monopile_design.yaml") +``` + +Other use cases could be for creating input templates for project configurations, such +as those used by `ProjectManager` in the next section. + +(library-tutorial)= +## Using A Data Library + +ORBIT makes use of its own +[internal library](https://github.com/NLRWindSystems/ORBIT/tree/main/library) when a user-provided +library path is not provided (i.e. a value isn't provide so the default `None` is used in +`ProjectManager(config, library_path=None)`). When a value is provided, user library files will be +searched for first, and the default library will be checked for any unfound files. + +This is made visible in the [installation phases section](#installation-phases) where the value +"example_wtiv" is provided to the "wtiv" key. When the configuration is loaded, `ProjectManager` +will attempt to find the `example_wtiv.yaml` file in the ORBIT default library under the `vessels/` +folder. Below is the expected folder structure of the library. I + +```console +# /path/to/library/ +├── defaults <- Top-level default data +├── project +│ ├── config <- Configuration dictionary repository +│ ├── port <- Port specific data setttings +│ ├── plant <- Wind farm specific data setttings +│ ├── site <- Project site data settings +│ ├── development <- Project development cost settings +├── cables <- Cable data files: array cables, export cables +├── substructures <- Substructure data files: monopiles, jackets, etc. +├── turbines <- Turbine data files +├── vessels <- Vessel data files +│ ├── defaults <- Default data related to vessel tasks +├── weather <- Weather profiles +├── results +``` + +## Vessels + +All installation models rely on at least one vessel to perform the installation routines. Similar +to turbine and cable configuration files, these should be stored in the YAML format in the +`vessels` library folder. Below are the + +- `vessel_specs` - General vessel parameters including day rate. + - `day_rate`: Daily cost to operate the vessel, $USD/day. + - `min_draft`: Minimum distance between the waterline and the bottom of the hull, m. + - `overall_length`: Length of the vessel, m. + - `mobilization_days`: Number days required to mobilize the vessel to site. + - `mobilization_mult`: Mobilization multiplier applied to `day_rate`. + - Any other custom input that will override logistics defaults. +- `transport_specs` - Transit related parameters and constraints. + - `transit_speed`: Average transiting speed, km/h. + - `max_waveheight`: Maximum operational wave height, m/s. + - `max_windspeed`: Maximum operational wind speed, m/s. +- `storage_specs` - Storage related parameters. Required to transport items + on deck. + - `max_cargo`: Maximum cargo capacity, metric tonnes. + - `max_deck_load`: Maximum capacity to be loaded on deck, metric tonnes per square meter, $t/m^2$. + - `max_deck_space`: Maximum amount of space on deck for loading components, $m^2$. +- `cable_storage`: Array and export cable carousel storage parameters. + - `max_mass`: Maximum mass of the cable carousel, in metric tonnes. +- `spi_specs`: Scouring protection installation vessel storage parameters. + - `max_cargo_mass`: Maximum mass allowed to be loaded for a single trip, in metric tonnes. +- `jacksys_specs`: Jacking system related parameters. Currently required + for all fixed substructure and turbine installations. + - `leg_length`: Length of the jackup vessel's legs, m. + - `air_gap`: Distance between sea level and the vessel bottom when fully jacked up, m. + - `leg_pen`: How far the leg penetrates the sea floor for stability, m. + - `max_depth`: Maxium water depth, m. + - `max_extension`: Maximum leg extension, m. + - `speed_below_depth`: Jackup speed when leg extension has not reached the sea floor, m/min + - `speed_above_depth`: Jackup speed after the leg has reached the sea floor and the vessel is + being raised above sea level, m/min. +- `dynamic_positioning_specs`: Dynamic positioning related parameters + - `class`: integer of the dynamic positioning class. +- `crane_specs` - Crane related parameters and constraints. Required for + any offshore lifts. + - `max_lift`: Maximum mass that can be lifted, metric tonnes. + - `max_hook_height`: Maximum height the hook can be raised, m. + - `max_windspeed`: Maximum operational windspeed, m/s. + - `crane_rate`: Crane lift rate, m/h. + +## Syncing Design and Installation with `ProjectManager` + +`ProjectManager` is the primary system for interacting with ORBIT. It provides the ability to +configure and run one or multiple design and installation at a time, allowing the user to customize +ORBIT to fit the needs of a specific project. It also provides a helper method to detail what inputs +are required to run the desired configuration. + +Continuing to work with just the monopile, we can provide a barebones configuration to set the +desired phases, and output the required inputs when running the design and installation phase in +unison. Notice that the "monopile" definition is no longer required for the installation as the +`design_result` will be automatically passed from the design phase to the installation phase. +There are now additional project parameters to supply development and other non-modeled fixed costs +the project will incur. Similar to the design and installation models, anything that is marked as +optional will have a default value within the model. + +For more details on the `ProjectManager`, please see the [tutorial](#project-manager-tutorial). + +```{code-cell} ipython3 +phases = ["MonopileDesign", "MonopileInstallation"] +config_template = ProjectManager.compile_input_dict(phases) +pprint(config_template) +``` + +Now, we can combine the monopile design and installation configurations that were +used in the previous examples, and run the model to get a single CapEx alongsie the +high level category breakdown. + +```{code-cell} ipython3 +project_config = deepcopy(design_config) +project_config["wtiv"] = "example_wtiv" +project_config["feeder"] = "example_feeder" +project_config["num_feeders"] = 2 +project_config["site"] = install_config["site"] +project_config["turbine"]["turbine_rating"] = 12 +project_config["design_phases"] = ["MonopileDesign"] +project_config["install_phases"] = ["MonopileInstallation"] + +project = ProjectManager(project_config) +project.run() +print(f"{'Project Capex':>30}: {project.bos_capex / 1e6:6,.2f} M") +for category, cost in project.capex_breakdown.items(): + print(f"{category:>30}: {cost / 1e6:6,.2f} M") +``` + +To continue with the previous subsection's demonstration, we can also save the final configuration +in one combined file, so the project could be reloaded and rerun in the future. + +```{code-cell} ipython3 +config_fn = Path("monopile_demo.yaml").resolve() +save_config(project.config, config_fn) + +config = load_config(config_fn) +project = ProjectManager(config) +project.run() + +print(f"{'Project Capex':>30}: {project.bos_capex / 1e6:6,.2f} M") +for category, cost in project.capex_breakdown.items(): + print(f"{category:>30}: {cost / 1e6:6,.2f} M") + +config_fn.unlink() # delete the demo file +``` diff --git a/docs/tutorials/parametric_manager.md b/docs/tutorials/parametric_manager.md new file mode 100644 index 00000000..bfb82e53 --- /dev/null +++ b/docs/tutorials/parametric_manager.md @@ -0,0 +1,143 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +(parametric-manager-tutorial)= +# ParametricManager + +Similar to the `ProjectManager`, ORIBT provides the `ParametricManager` to run simple parametric +studies by defining a subset of the inputs as a list. This allows for tradeoff studies to compare +the effects of siting (e.g., water depth and distance) on cost and installation timing. For complete +details on using the `ParmetricManager` please see the [API documentation](#parametric-manager-api). + +First, we'll import the necessary libraries, and load the example fixed-bottom project to use as +our project base with the 15 MW turbine. + +```{code-cell} ipython3 +from pathlib import Path + +import pandas as pd +import matplotlib.pyplot as plt +from matplotlib.ticker import StrMethodFormatter + +from ORBIT import ParametricManager, load_config + +here = Path(".").resolve() +example_dir = here.parents[1] / "examples" if here.stem == "tutorials" else here + +config = load_config(example_dir / "configs/example_fixed_project.yaml") +config["turbine"] = "15MW_generic" + +weather = pd.read_csv(example_dir / "data/example_weather.csv").set_index("datetime") +``` + +For all the non-parameterized inputs, they can be left as-is. However, all parameterized variables +should be provided in a separate dictionary as a list. Because ORBIT uses the `benedict` library +for more streamlined dictionary access, nested keys can be represented using dot-notation as is +shown below where we parameterize the key siting details. + +```{code-cell} ipython3 +params = { + "site.depth": list(range(10, 71, 10)), + "site.distance": list(range(20, 201, 20)), + "site.distance_to_landfall": [60, 80, 100], +} +``` + +Similar to the parameterized inputs, we must also define the desired outputs. However, outputs must +be provided as a dictionary of `lambda` functions for what metrics should be captured. In the +below example, we extract just the installation and system CapEx. + +```{code-cell} ipython3 +results = { + "Installation": lambda project: project.installation_capex, + "System": lambda project: project.system_capex +} +``` + +If many parameters are configured, it will take a longer time to run, especially if a weather +profile is provided and `product=True`. To get an idea of the total run time, use the `preview` +method, as seen below. + +Setting `product` to `True` means that all of the parameters will be run as a combination of all +possible permutations rather than a zipped list. When using `False` extra care must be taken to +ensure the correct outcomes will be achieved by using equally-lengthed parameterizations. For +instacnce, in our current example, the shortest parameterization has only 3 values, so the first +3 values of `depth` and `distance` will be selected for the parameterized run. + +```{code-cell} ipython3 +project = ParametricManager(config, params, results, product=True, weather=weather) +project.preview() +``` + +```{code-cell} ipython3 +project.run() +``` + +The results are saved as a pandas DataFrame in the `results` attribute where each row represents a +different scenario run and the columns are labeled with with the various parameters and results +values that were configured. + +## Plotting + +It is more convenient to plot the results of the `ParametricManager` than it is to view them as a +table, especially with a large number of parameters. First, we will create a matrix of results +for each of the installation and system CapEx. Please note the `installation_arr` index is sorted +in reverse order for convenience in creating the heatmap. + +```{code-cell} ipython3 +results = project.results.set_index(["site.depth", "site.distance"]) / 1e6 +installation_arr = results.unstack()["Installation"].sort_index(ascending=False) +system_arr = results.unstack()["System"].sort_index() +``` + +As mentioned in the [`ProjectManager` tutorial](#project-manager-tutorial), the system CapEx will +not change given certain parameter changes. In this case, the installation CapEx increases both +as the site's distance and depth increases, as can be seen in the following heatmap. + +```{code-cell} ipython3 +fig = plt.figure() +ax = fig.add_subplot(111) + +im = ax.imshow(installation_arr.values, vmin=290, vmax=380) + +cbar = fig.colorbar(im, ax=ax, shrink=0.8) +cbar.ax.set_ylabel("Installation CapEx (millions, USD)", rotation=-90, va="bottom") + +ax.set_xticks(range(len(installation_arr.columns)), labels=installation_arr.columns, rotation=45, ha="right", rotation_mode="anchor") +ax.set_yticks(range(len(installation_arr.index)), labels=installation_arr.index) + +ax.set_xlabel("Site Distance (km)") +ax.set_ylabel("Site Depth (m)") + +fig.tight_layout() +``` + +However, the system CapEx only increases as the site's depth increases because only the site's +distance to port changes, and not the distance to landfall, meaning the export cable length will +not change across scenarios. This can be seen in the below bar graph. + +```{code-cell} ipython3 +fig = plt.figure() +ax = fig.add_subplot(111) + +x = range(len(system_arr.index)) +ax.bar(x, system_arr.values[:, 0]) + +ax.set_xticks(x) +ax.set_xticklabels(system_arr.index.values) +ax.yaxis.set_major_formatter(StrMethodFormatter("{x:,.0f}")) +ax.set_xlabel("Site Depth (m)") +ax.set_ylabel("System CapEx (millions, USD)") + +fig.tight_layout() +``` diff --git a/docs/tutorials/project_manager.md b/docs/tutorials/project_manager.md new file mode 100644 index 00000000..aeac455d --- /dev/null +++ b/docs/tutorials/project_manager.md @@ -0,0 +1,322 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +(project-manager-tutorial)= +# `ProjectManager` Deep Dive + +`ProjectManager` is the primary system for interacting with ORBIT. It provides the ability to +configure and run one or multiple models at a time, allowing the user to customize ORBIT to fit the +needs of a specific project. + +```{code-cell} ipython3 +from pathlib import Path +from pprint import pprint + +import pandas as pd +import matplotlib.pyplot as plt + +from ORBIT import ProjectManager + +# Ensure the correct examples directory is used when running this in docs or in examples +here = Path(".").resolve() +example_dir = here.parents[1] / "examples" if here.stem == "tutorials" else here +``` + +## Compiling Input Requirements Dynamically + +To better understand the input requirements for designing and installing multiple turbine subsystems, +`ProjectManager` provides the `compile_input_dict()` method that will generate the expected +configuration of each provided phase in a single configuration dictionary. The example below shows +how to configure a simple project with a design and multiple installation phases, and return the required configuration parameters. + +```{code-cell} ipython3 +phases = [ + "MonopileDesign", + "MonopileInstallation", + "TurbineInstallation", +] + +expected_config = ProjectManager.compile_input_dict(phases) +pprint(expected_config) +``` + +Using the results of the `expected_config`, the following configuration is now created to minimally +define a project running only the monopile phases for design and installation, and the turbine +installation phase. Note that the turbine is a copy of the +[12MW generic turbine from ORBIT library](https://github.com/NLRWindSystems/ORBIT/tree/main/library/turbines/12MW_generic.yaml). + +```{code-cell} ipython3 +config = { + "site": { + "depth": 20, + "distance": 50, + "mean_windspeed": 9.5, + }, + "plant": { + "num_turbines": 50, + }, + "turbine": { + "name": "12MW Generic Turbine", + "rotor_diameter": 205, + "hub_height": 125, + "rated_windspeed": 11, + "blade": { + "deck_space": 385, + "length": 107, + "type": "Blade", + "mass": 54, + }, + "nacelle": { + "deck_space": 203, + "type": "Nacelle", + "mass": 604, + }, + "tower": { + "deck_space": 50.24, + "sections": 2, + "type": "Tower", + "length": 132, + "mass": 399, + }, + }, + "wtiv": "example_wtiv", + "design_phases": ["MonopileDesign"], + "install_phases": ["MonopileInstallation", "TurbineInstallation"], +} + +project = ProjectManager(config) +project.run() +``` + +## Weather Profiles + +To include wind and wave conditions in the simulation for vessel and port constraints, pass an +hourly pandas DataFrame to `ProjectManager` using the `weather` keyword argument. All installation +phases will now use this time series to account for weather delays. + +```{code-cell} ipython3 +weather = pd.read_csv(example_dir / "data/example_weather.csv").set_index("datetime") + +project = ProjectManager(config, weather=weather) +project.run() +``` + +## Accessing Individual Models + +The `ProjectManager` provides a dictionary-based attribute `phases` that allows users to access the +design or installation class for custom results gathering or model inspection. Using the previously +run project, we now directly access the monopile design costs. + +```{code-cell} ipython3 +monopile_design_cost = project.phases["MonopileDesign"].total_cost +print(f"Total Monopile Cost: ${monopile_design_cost / 1e6:,.2f} M") +``` + +## Phase-Specific Configurations + +As was seen in [inputs compilation demonstration](#compiling-input-requirements-dynamically), +`ProjectManager` compiles the minimum required configuration, combining the same parameter that is +needed for multiple phases into one input. This isn't always a desired outcome as there are cases +when inputs need to be different for each phase. For example, the `distance_to_shore` parameter may +be different for each installation phase if different ports are used to stage monopiles and turbines +or the installations may use different installation vessels. + +In these cases, it is necessary to define phase specific input parameters using the phase's name as +the dictionary key. Below, we can see how we model a differing staging port where a separate WTIV +will be used with its much further port distance. + +Please note that phase-specific configurations will always override their general counterparts. + +```python +config = { + "site": { + "depth": 20, + "distance": 50, + "mean_windspeed": 9.5, + }, + "plant": { + "num_turbines": 50, + }, + "turbine": { + "name": "12MW Generic Turbine", + "rotor_diameter": 205, + "hub_height": 125, + "rated_windspeed": 11, + "blade": { + "deck_space": 385, + "length": 107, + "type": "Blade", + "mass": 54, + }, + "nacelle": { + "deck_space": 203, + "type": "Nacelle", + "mass": 604, + }, + "tower": { + "deck_space": 50.24, + "sections": 2, + "type": "Tower", + "length": 132, + "mass": 399, + }, + }, + "TurbineInstallation": { + "wtiv": "other_wtiv", + "site": { + "distance": 100, + }, + }, + "wtiv": "example_wtiv", + "design_phases": ["MonopileDesign"], + "install_phases": ["MonopileInstallation", "TurbineInstallation"], +} +``` + +## Phase Timing + +By default, all phases will run in the order they are defined in both the `design_phases` and +`install_phases`. When a weather profile is provided, all phases will start at the beginning of the +weather profile. To more realistically simulate the timing of installations, phase start dates +can be customized to start at a specific date, or be reliant on the completion status of a dependent +phase. The next two subsections will detail how both of these work, and can be used together. + +:::{warning} +ORBIT does not have any safety mechanisms to avoid inappropriate installation overlaps, i.e., +installing turbines before the monopiles have been fully installed, so it is important to check +the installation timing to ensure unrealistic conditions have not been modeled. +::: + +### Defining Start Dates + +Instead of defining the `install_phases` as a list of strings for each phase, a dictionary of the +phase's class name and the string starting date should be provided. In the following example +configuration (derived from +[`examples/configs/example_fixed_project.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/examples/configs/example_fixed_project.yaml)) for a complete wind power plant, we can see how each +of the phases are staggered based on an imagined idealized starting date. + +```{code-cell} ipython3 +config = { + "design_phases": [ + "MonopileDesign", + "ScourProtectionDesign", + "ArraySystemDesign", + "ExportSystemDesign", + "OffshoreSubstationDesign", + ], + "install_phases": { + "MonopileInstallation": "04/01/2020", + "TurbineInstallation": "05/01/2020", + "ArrayCableInstallation": "08/01/2020", + "OffshoreSubstationInstallation": "08/01/2020", + "ScourProtectionInstallation": "03/01/2021", + "ExportCableInstallation": "08/15/2020", + }, + "turbine": "12MW_generic", + "project_parameters": {"turbine_capex": 1500}, + "site": { + "depth": 22.5, + "distance": 124, + "distance_to_landfall": 35, + "mean_windspeed": 9, + }, + "plant": { + "layout": "grid", + "num_turbines": 50, + "row_spacing": 7, + "substation_distance": 1, + "turbine_spacing": 7, + }, + "array_system_design": {"cables": ["XLPE_630mm_33kV", "XLPE_400mm_33kV"]}, + "export_system_design": { + "cables": "XLPE_500mm_132kV", + "percent_added_length": 0.0, + "landfall": {"interconnection_distance": 3, "trench_length": 2}, + }, + "scour_protection_design": {"cost_per_tonne": 40, "scour_protection_depth": 1}, + "OffshoreSubstationInstallation": { + "feeder": "example_heavy_feeder", + "num_feeders": 1, + }, + "wtiv": "example_wtiv", + "feeder": "example_heavy_feeder", + "num_feeders": 2, + "spi_vessel": "example_scour_protection_vessel", + "oss_install_vessel": "example_heavy_lift_vessel", + "array_cable_install_vessel": "example_cable_lay_vessel", + "export_cable_bury_vessel": "example_cable_lay_vessel", + "export_cable_install_vessel": "example_cable_lay_vessel", +} + +project = ProjectManager(config) +project.run() +``` + +Now, we can make a quick visualization to see how the start timing plays out. Notice how the +monopile and turbine installations overlap yet there is only a single WTIV assigned to the site. In +practice this should not be possible, but is helpful to highlight why care is needed when +configuring phase timing. + +```{code-cell} ipython3 +df = pd.DataFrame.from_dict(project.phase_dates).T +df.start = pd.to_datetime(df.start) +df.end = pd.to_datetime(df.end) +df.sort_values("start") + +fig = plt.figure(figsize=(10, 4)) +ax = fig.add_subplot(111) + +ax.barh(y=df.index, width=df.end - df.start, left=df.start); + +ax.grid(axis="x") +ax.set_axisbelow(True) +ax.set_xlim(pd.to_datetime("2020-03"), pd.to_datetime("2021-05")) +fig.tight_layout() +``` + +(phase-dependent-timing)= +### Phase Dependent Timing + +The other method to configure installation phase timing is to define dependent phase completion +rates. For instance, instead of providing `"TurbineInstallation": "05/01/2020"`, we could simply +wait until 30% of the monopiles are installed by providing ``"TurbineInstallation": ("MonopileInstallation", 0.3)`. Below, we rely on dependencies instead of dates for nearly all +phases and show the results. Note, that mixed date and dependency inputs are allowed. + +```{code-cell} ipython3 +dependent_starts = { + "MonopileInstallation": "04/01/2020", + "TurbineInstallation": ("MonopileInstallation", 0.3), + "ArrayCableInstallation": ("TurbineInstallation", 0.25), + "OffshoreSubstationInstallation": "08/01/2020", + "ScourProtectionInstallation": ("ArrayCableInstallation", 0.8), + "ExportCableInstallation": ("OffshoreSubstationInstallation", 1), +} +config["install_phases"] = dependent_starts +project = ProjectManager(config) +project.run() + +df = pd.DataFrame.from_dict(project.phase_dates).T +df.start = pd.to_datetime(df.start) +df.end = pd.to_datetime(df.end) +df.sort_values("start") + +fig = plt.figure(figsize=(10, 4)) +ax = fig.add_subplot(111) + +ax.barh(y=df.index, width=df.end - df.start, left=df.start); + +ax.grid(axis="x") +ax.set_axisbelow(True) +ax.set_xlim(pd.to_datetime("2020-03"), pd.to_datetime("2021-05")) +fig.tight_layout() +``` diff --git a/examples/1. Introduction.ipynb b/examples/1. Introduction.ipynb deleted file mode 100644 index a7ff9c4c..00000000 --- a/examples/1. Introduction.ipynb +++ /dev/null @@ -1,280 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ORBIT Introduction\n", - "\n", - "ORBIT is organized into two different types of modules: design and installation. Design modules are intended to model the sizing and cost of offshore wind subcomponents and installation modules simulate the installation of these subcomponents in a discrete event simulation framework. The easiest way to start working with ORBIT is to look at one module. This tutorial will look at the monopile design module and the next tutorial will look at the monopile installation module." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# To import a design module:\n", - "from ORBIT.phases.design import MonopileDesign" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'site': {'depth': 'm', 'mean_windspeed': 'm/s'},\n", - " 'plant': {'num_turbines': 'int'},\n", - " 'turbine': {'rotor_diameter': 'm',\n", - " 'hub_height': 'm',\n", - " 'rated_windspeed': 'm/s'},\n", - " 'monopile_design': {'yield_stress': 'Pa (optional)',\n", - " 'load_factor': 'float (optional)',\n", - " 'material_factor': 'float (optional)',\n", - " 'monopile_density': 'kg/m3 (optional)',\n", - " 'monopile_modulus': 'Pa (optional)',\n", - " 'monopile_tp_connection_thickness': 'm (optional)',\n", - " 'transition_piece_density': 'kg/m3 (optional)',\n", - " 'transition_piece_thickness': 'm (optional)',\n", - " 'transition_piece_length': 'm (optional)',\n", - " 'soil_coefficient': 'N/m3 (optional)',\n", - " 'air_density': 'kg/m3 (optional)',\n", - " 'weibull_scale_factor': 'float (optional)',\n", - " 'weibull_shape_factor': 'float (optional)',\n", - " 'turb_length_scale': 'm (optional)',\n", - " 'monopile_steel_cost': 'USD/t (optional)',\n", - " 'tp_steel_cost': 'USD/t (optional)'}}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Each module has a property `.expected_config` that gives hints as to how to configure the module properly.\n", - "# This property returns a nested dictionary with all of the inputs (including optional ones) that can be used\n", - "# to configure this module.\n", - "\n", - "# For example:\n", - "MonopileDesign.expected_config" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# For now, lets ignore the optional inputs in the 'monopile_design' subdict and just look at the required inputs:\n", - "config_unfilled = {\n", - " 'site': { # Inputs are grouped into subdicts, eg. site, plant, etc.\n", - " 'depth': 'm', # The value represents the unit where applicable\n", - " 'mean_windspeed': 'm/s'\n", - " },\n", - " \n", - " 'plant': {\n", - " 'num_turbines': 'int'\n", - " },\n", - " \n", - " 'turbine': {\n", - " 'rotor_diameter': 'm',\n", - " 'hub_height': 'm',\n", - " 'rated_windspeed': 'm/s'\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ORBIT library intialized at '/Users/jnunemak/Fun/repos/ORBIT/library'\n", - "Total Substructure Cost: 276.77 M\n" - ] - } - ], - "source": [ - "# Filling out the config for a simple fixed bottom project:\n", - "config = {\n", - " 'site': {\n", - " 'depth': 25,\n", - " 'mean_windspeed': 9.5\n", - " },\n", - " \n", - " 'plant': {\n", - " 'num_turbines': 50\n", - " },\n", - " \n", - " 'turbine': {\n", - " 'rotor_diameter': 220,\n", - " 'hub_height': 120,\n", - " 'rated_windspeed': 13\n", - " }\n", - "}\n", - "\n", - "# To run the module, create an instance by passing the config into the module and then use module.run()\n", - "\n", - "module = MonopileDesign(config)\n", - "module.run()\n", - "print(f\"Total Substructure Cost: {module.total_cost/1e6:.2f} M\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "ename": "MissingInputs", - "evalue": "Input(s) '['site.depth', 'site.mean_windspeed']' missing in config.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mMissingInputs\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"site\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mmodule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMonopileDesign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Fun/repos/ORBIT/ORBIT/phases/design/monopile_design.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, config, **kwargs)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialize_library\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 78\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Fun/repos/ORBIT/ORBIT/phases/base.py\u001b[0m in \u001b[0;36mvalidate_config\u001b[0;34m(self, config)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMissingInputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mMissingInputs\u001b[0m: Input(s) '['site.depth', 'site.mean_windspeed']' missing in config." - ] - } - ], - "source": [ - "# If a required input is missing, an error message will be raised with the input and it's location within the configuration.\n", - "# This error message used 'dot-notation' to show the structure of the dictionary. Each \".\" represents a lower level in the dictionary.\n", - "# \"site.depth\" indicates that it is the 'depth' input in the 'site' subdict.\n", - "\n", - "# In the example below, the 'site' inputs have been removed.\n", - "# The following inputs will be missing: '['site.depth', 'site.mean_windspeed']'\n", - "\n", - "tmp = deepcopy(config)\n", - "_ = tmp.pop(\"site\")\n", - "\n", - "module = MonopileDesign(tmp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Optional Inputs" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Substructure Cost: 361.02 M\n" - ] - } - ], - "source": [ - "# Now lets add more optional inputs:\n", - "config = {\n", - " 'site': {\n", - " 'depth': 25,\n", - " 'mean_windspeed': 9.5\n", - " },\n", - " \n", - " 'plant': {\n", - " 'num_turbines': 50\n", - " },\n", - " \n", - " 'turbine': {\n", - " 'rotor_diameter': 220,\n", - " 'hub_height': 120,\n", - " 'rated_windspeed': 13\n", - " },\n", - " \n", - " # --- New Inputs ---\n", - " 'monopile_design': {\n", - " 'monopile_steel_cost': 3500, # USD/t\n", - " 'tp_steel_cost': 4500 # USD/t\n", - " }\n", - "}\n", - "\n", - "module = MonopileDesign(config)\n", - "module.run()\n", - "print(f\"Total Substructure Cost: {module.total_cost/1e6:.2f} M\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'monopile': {'diameter': 10.217490535969192,\n", - " 'thickness': 0.10852490535969192,\n", - " 'moment': 44.02602353978204,\n", - " 'embedment_length': 37.11640362329476,\n", - " 'length': 72.11640362329476,\n", - " 'mass': 1082.5344126589946,\n", - " 'deck_space': 104.39711285262001,\n", - " 'unit_cost': 3788870.444306481},\n", - " 'transition_piece': {'thickness': 0.10852490535969192,\n", - " 'diameter': 10.434540346688577,\n", - " 'mass': 762.5683087222009,\n", - " 'length': 25,\n", - " 'deck_space': 108.87963224667176,\n", - " 'unit_cost': 3431557.389249904}}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# To look at more detailed results:\n", - "module.design_result" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/2. Installation Modules.ipynb b/examples/2. Installation Modules.ipynb deleted file mode 100644 index 2723ee0a..00000000 --- a/examples/2. Installation Modules.ipynb +++ /dev/null @@ -1,1742 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation Modules\n", - "\n", - "Installation modules have the same external structure (ie. 'expected_config') as design modules, however they have additional internal pieces to them that power the discrete event simulation framework." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# To import an installation module:\n", - "from ORBIT.phases.install import MonopileInstallation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'wtiv': 'dict | str',\n", - " 'feeder': 'dict | str (optional)',\n", - " 'num_feeders': 'int (optional)',\n", - " 'site': {'depth': 'm', 'distance': 'km'},\n", - " 'plant': {'num_turbines': 'int'},\n", - " 'turbine': {'hub_height': 'm'},\n", - " 'port': {'num_cranes': 'int (optional, default: 1)',\n", - " 'monthly_rate': 'USD/mo (optional)',\n", - " 'name': 'str (optional)'},\n", - " 'monopile': {'length': 'm',\n", - " 'diameter': 'm',\n", - " 'deck_space': 'm2',\n", - " 'mass': 't',\n", - " 'unit_cost': 'USD'},\n", - " 'transition_piece': {'deck_space': 'm2', 'mass': 't', 'unit_cost': 'USD'}}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Expected config:\n", - "MonopileInstallation.expected_config" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "config_unfilled = {\n", - " 'site': { # Similar to the design module, inputs are grouped by category\n", - " 'depth': 'm', # Many of the inputs required are the same as the design module\n", - " 'distance': 'km'\n", - " },\n", - " \n", - " 'plant': {\n", - " 'num_turbines': 'int'\n", - " },\n", - " \n", - " 'turbine': {\n", - " 'hub_height': 'm'\n", - " },\n", - " \n", - " 'wtiv': 'dict | str', # The WTIV that will be installing the monopiles.\n", - " # Vessel are defined in the library in .yaml files.\n", - " \n", - " 'monopile': { # Notice that the result of the last module (monopile + transition piece sizing)\n", - " 'length': 'm', # is an input into the installation module.\n", - " 'diameter': 'm',\n", - " 'deck_space': 'm2',\n", - " 'mass': 't',\n", - " 'unit_cost': 'USD'\n", - " },\n", - " \n", - " 'transition_piece': {\n", - " 'deck_space': 'm2',\n", - " 'mass': 't',\n", - " 'unit_cost': 'USD'\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installation CapEx: 21.94 M\n" - ] - } - ], - "source": [ - "config = {\n", - " 'site': {\n", - " 'depth': 25,\n", - " 'distance': 50\n", - " },\n", - " \n", - " 'plant': {\n", - " 'num_turbines': 50\n", - " },\n", - " \n", - " 'turbine': {\n", - " 'hub_height': 120\n", - " },\n", - " \n", - " 'wtiv': 'example_wtiv', # See 'example_wtiv.yaml'\n", - " \n", - " 'monopile': {\n", - " 'length': 72.1,\n", - " 'diameter': 10.2,\n", - " 'deck_space': 104.4,\n", - " 'mass': 1082.5,\n", - " 'unit_cost': 3788870\n", - " },\n", - " \n", - " 'transition_piece': {\n", - " 'deck_space': 108.9,\n", - " 'mass': 762.6,\n", - " 'unit_cost': 3431557\n", - " }\n", - "}\n", - "\n", - "module = MonopileInstallation(config)\n", - "module.run()\n", - "\n", - "print(f\"Installation CapEx: {module.installation_capex/1e6:.2f} M\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Simulation Logs" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_multiplieragentactiondurationcostleveltimephasesite_depthhub_height
01.0WTIVMobilize168.0000001260000.00ACTION0.000000Monopile InstallationNaNNaN
1NaNWTIVFasten Monopile12.00000090000.00ACTION12.000000Monopile Installation25.0120.0
2NaNWTIVFasten Transition Piece8.00000060000.00ACTION20.000000Monopile Installation25.0120.0
3NaNWTIVFasten Monopile12.00000090000.00ACTION32.000000Monopile Installation25.0120.0
4NaNWTIVFasten Transition Piece8.00000060000.00ACTION40.000000Monopile Installation25.0120.0
5NaNWTIVFasten Monopile12.00000090000.00ACTION52.000000Monopile Installation25.0120.0
6NaNWTIVFasten Transition Piece8.00000060000.00ACTION60.000000Monopile Installation25.0120.0
7NaNWTIVFasten Monopile12.00000090000.00ACTION72.000000Monopile Installation25.0120.0
8NaNWTIVFasten Transition Piece8.00000060000.00ACTION80.000000Monopile Installation25.0120.0
9NaNWTIVTransit5.00000037500.00ACTION85.000000Monopile InstallationNaNNaN
10NaNWTIVPosition Onsite2.00000015000.00ACTION87.000000Monopile InstallationNaNNaN
11NaNWTIVJackup0.3333332500.00ACTION87.333333Monopile Installation25.0120.0
12NaNWTIVRovSurvey1.0000007500.00ACTION88.333333Monopile Installation25.0120.0
13NaNWTIVRelease Monopile3.00000022500.00ACTION91.333333Monopile InstallationNaNNaN
14NaNWTIVUpend Monopile0.7210005407.50ACTION92.054333Monopile Installation25.0120.0
15NaNWTIVLower Monopile0.00350026.25ACTION92.057833Monopile Installation25.0120.0
16NaNWTIVCrane Reequip1.0000007500.00ACTION93.057833Monopile Installation25.0120.0
17NaNWTIVDrive Monopile1.50000011250.00ACTION94.557833Monopile Installation25.0120.0
18NaNWTIVRelease Transition Piece2.00000015000.00ACTION96.557833Monopile InstallationNaNNaN
19NaNWTIVCrane Reequip1.0000007500.00ACTION97.557833Monopile Installation25.0120.0
20NaNWTIVLower TP1.0000007500.00ACTION98.557833Monopile Installation25.0120.0
21NaNWTIVBolt TP4.00000030000.00ACTION102.557833Monopile Installation25.0120.0
22NaNWTIVJackdown0.3333332500.00ACTION102.891167Monopile Installation25.0120.0
23NaNWTIVPosition Onsite2.00000015000.00ACTION104.891167Monopile InstallationNaNNaN
24NaNWTIVJackup0.3333332500.00ACTION105.224500Monopile Installation25.0120.0
\n", - "
" - ], - "text/plain": [ - " cost_multiplier agent action duration cost \\\n", - "0 1.0 WTIV Mobilize 168.000000 1260000.00 \n", - "1 NaN WTIV Fasten Monopile 12.000000 90000.00 \n", - "2 NaN WTIV Fasten Transition Piece 8.000000 60000.00 \n", - "3 NaN WTIV Fasten Monopile 12.000000 90000.00 \n", - "4 NaN WTIV Fasten Transition Piece 8.000000 60000.00 \n", - "5 NaN WTIV Fasten Monopile 12.000000 90000.00 \n", - "6 NaN WTIV Fasten Transition Piece 8.000000 60000.00 \n", - "7 NaN WTIV Fasten Monopile 12.000000 90000.00 \n", - "8 NaN WTIV Fasten Transition Piece 8.000000 60000.00 \n", - "9 NaN WTIV Transit 5.000000 37500.00 \n", - "10 NaN WTIV Position Onsite 2.000000 15000.00 \n", - "11 NaN WTIV Jackup 0.333333 2500.00 \n", - "12 NaN WTIV RovSurvey 1.000000 7500.00 \n", - "13 NaN WTIV Release Monopile 3.000000 22500.00 \n", - "14 NaN WTIV Upend Monopile 0.721000 5407.50 \n", - "15 NaN WTIV Lower Monopile 0.003500 26.25 \n", - "16 NaN WTIV Crane Reequip 1.000000 7500.00 \n", - "17 NaN WTIV Drive Monopile 1.500000 11250.00 \n", - "18 NaN WTIV Release Transition Piece 2.000000 15000.00 \n", - "19 NaN WTIV Crane Reequip 1.000000 7500.00 \n", - "20 NaN WTIV Lower TP 1.000000 7500.00 \n", - "21 NaN WTIV Bolt TP 4.000000 30000.00 \n", - "22 NaN WTIV Jackdown 0.333333 2500.00 \n", - "23 NaN WTIV Position Onsite 2.000000 15000.00 \n", - "24 NaN WTIV Jackup 0.333333 2500.00 \n", - "\n", - " level time phase site_depth hub_height \n", - "0 ACTION 0.000000 Monopile Installation NaN NaN \n", - "1 ACTION 12.000000 Monopile Installation 25.0 120.0 \n", - "2 ACTION 20.000000 Monopile Installation 25.0 120.0 \n", - "3 ACTION 32.000000 Monopile Installation 25.0 120.0 \n", - "4 ACTION 40.000000 Monopile Installation 25.0 120.0 \n", - "5 ACTION 52.000000 Monopile Installation 25.0 120.0 \n", - "6 ACTION 60.000000 Monopile Installation 25.0 120.0 \n", - "7 ACTION 72.000000 Monopile Installation 25.0 120.0 \n", - "8 ACTION 80.000000 Monopile Installation 25.0 120.0 \n", - "9 ACTION 85.000000 Monopile Installation NaN NaN \n", - "10 ACTION 87.000000 Monopile Installation NaN NaN \n", - "11 ACTION 87.333333 Monopile Installation 25.0 120.0 \n", - "12 ACTION 88.333333 Monopile Installation 25.0 120.0 \n", - "13 ACTION 91.333333 Monopile Installation NaN NaN \n", - "14 ACTION 92.054333 Monopile Installation 25.0 120.0 \n", - "15 ACTION 92.057833 Monopile Installation 25.0 120.0 \n", - "16 ACTION 93.057833 Monopile Installation 25.0 120.0 \n", - "17 ACTION 94.557833 Monopile Installation 25.0 120.0 \n", - "18 ACTION 96.557833 Monopile Installation NaN NaN \n", - "19 ACTION 97.557833 Monopile Installation 25.0 120.0 \n", - "20 ACTION 98.557833 Monopile Installation 25.0 120.0 \n", - "21 ACTION 102.557833 Monopile Installation 25.0 120.0 \n", - "22 ACTION 102.891167 Monopile Installation 25.0 120.0 \n", - "23 ACTION 104.891167 Monopile Installation NaN NaN \n", - "24 ACTION 105.224500 Monopile Installation 25.0 120.0 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The logs of all simulation steps taken by the vessel(s) are stored and available for analysis.\n", - "\n", - "# The following code returns a list of all actions with the associated agent (vessel), duration, cost, and time completed.\n", - "# Once we configure a weather file, this will also include any accrued weather delays.\n", - "\n", - "import pandas as pd\n", - "\n", - "df = pd.DataFrame(module.env.actions)\n", - "df.head(25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Inlcude Weather" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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windspeedwaveheight
datetime
2009-10-21 23:00:005.0752260.59
2009-10-22 00:00:005.4384000.65
2009-10-22 01:00:004.9470520.55
2009-10-22 02:00:004.3671950.57
2009-10-22 03:00:004.1352620.49
.........
2013-12-31 19:00:009.6041720.97
2013-12-31 20:00:009.8941040.97
2013-12-31 21:00:009.9093630.98
2013-12-31 22:00:0011.8564381.13
2013-12-31 23:00:0012.3691481.53
\n", - "

36769 rows \u00d7 2 columns

\n", - "
" - ], - "text/plain": [ - " windspeed waveheight\n", - "datetime \n", - "2009-10-21 23:00:00 5.075226 0.59\n", - "2009-10-22 00:00:00 5.438400 0.65\n", - "2009-10-22 01:00:00 4.947052 0.55\n", - "2009-10-22 02:00:00 4.367195 0.57\n", - "2009-10-22 03:00:00 4.135262 0.49\n", - "... ... ...\n", - "2013-12-31 19:00:00 9.604172 0.97\n", - "2013-12-31 20:00:00 9.894104 0.97\n", - "2013-12-31 21:00:00 9.909363 0.98\n", - "2013-12-31 22:00:00 11.856438 1.13\n", - "2013-12-31 23:00:00 12.369148 1.53\n", - "\n", - "[36769 rows x 2 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Weather data can be loaded and included in the simulation. Each action can have associated weather\n", - "# constraints (eg. windspeed < 15 m/s, sig. waveheight < 2.5). As the simulation progresses, each action\n", - "# checks that it can proceed given the weather forecast. If the constraints are not met, the agent will\n", - "# accrue weather delays until they are.\n", - "\n", - "# To load a weather file:\n", - "weather = pd.read_csv(\"data/example_weather.csv\", parse_dates=['datetime']).set_index(\"datetime\")\n", - "weather" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installation CapEx: 27.95 M\n" - ] - } - ], - "source": [ - "# To include weather in the simulation, pass it into the 'weather' keyword:\n", - "\n", - "module = MonopileInstallation(config, weather=weather)\n", - "module.run()\n", - "\n", - "print(f\"Installation CapEx: {module.installation_capex/1e6:.2f} M\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_multiplieragentactiondurationcostleveltimephasesite_depthhub_height
01.0WTIVMobilize168.0000001260000.00ACTION0.000000Monopile InstallationNaNNaN
1NaNWTIVFasten Monopile12.00000090000.00ACTION12.000000Monopile Installation25.0120.0
2NaNWTIVFasten Transition Piece8.00000060000.00ACTION20.000000Monopile Installation25.0120.0
3NaNWTIVFasten Monopile12.00000090000.00ACTION32.000000Monopile Installation25.0120.0
4NaNWTIVFasten Transition Piece8.00000060000.00ACTION40.000000Monopile Installation25.0120.0
5NaNWTIVFasten Monopile12.00000090000.00ACTION52.000000Monopile Installation25.0120.0
6NaNWTIVFasten Transition Piece8.00000060000.00ACTION60.000000Monopile Installation25.0120.0
7NaNWTIVDelay29.000000217500.00ACTION89.000000Monopile Installation25.0120.0
8NaNWTIVFasten Monopile12.00000090000.00ACTION101.000000Monopile Installation25.0120.0
9NaNWTIVFasten Transition Piece8.00000060000.00ACTION109.000000Monopile Installation25.0120.0
10NaNWTIVTransit5.00000037500.00ACTION114.000000Monopile InstallationNaNNaN
11NaNWTIVPosition Onsite2.00000015000.00ACTION116.000000Monopile InstallationNaNNaN
12NaNWTIVJackup0.3333332500.00ACTION116.333333Monopile Installation25.0120.0
13NaNWTIVRovSurvey1.0000007500.00ACTION117.333333Monopile Installation25.0120.0
14NaNWTIVRelease Monopile3.00000022500.00ACTION120.333333Monopile InstallationNaNNaN
15NaNWTIVUpend Monopile0.7210005407.50ACTION121.054333Monopile Installation25.0120.0
16NaNWTIVLower Monopile0.00350026.25ACTION121.057833Monopile Installation25.0120.0
17NaNWTIVCrane Reequip1.0000007500.00ACTION122.057833Monopile Installation25.0120.0
18NaNWTIVDrive Monopile1.50000011250.00ACTION123.557833Monopile Installation25.0120.0
19NaNWTIVRelease Transition Piece2.00000015000.00ACTION125.557833Monopile InstallationNaNNaN
20NaNWTIVCrane Reequip1.0000007500.00ACTION126.557833Monopile Installation25.0120.0
21NaNWTIVLower TP1.0000007500.00ACTION127.557833Monopile Installation25.0120.0
22NaNWTIVBolt TP4.00000030000.00ACTION131.557833Monopile Installation25.0120.0
23NaNWTIVJackdown0.3333332500.00ACTION131.891167Monopile Installation25.0120.0
24NaNWTIVPosition Onsite2.00000015000.00ACTION133.891167Monopile InstallationNaNNaN
\n", - "
" - ], - "text/plain": [ - " cost_multiplier agent action duration cost \\\n", - "0 1.0 WTIV Mobilize 168.000000 1260000.00 \n", - "1 NaN WTIV Fasten Monopile 12.000000 90000.00 \n", - "2 NaN WTIV Fasten Transition Piece 8.000000 60000.00 \n", - "3 NaN WTIV Fasten Monopile 12.000000 90000.00 \n", - "4 NaN WTIV Fasten Transition Piece 8.000000 60000.00 \n", - "5 NaN WTIV Fasten Monopile 12.000000 90000.00 \n", - "6 NaN WTIV Fasten Transition Piece 8.000000 60000.00 \n", - "7 NaN WTIV Delay 29.000000 217500.00 \n", - "8 NaN WTIV Fasten Monopile 12.000000 90000.00 \n", - "9 NaN WTIV Fasten Transition Piece 8.000000 60000.00 \n", - "10 NaN WTIV Transit 5.000000 37500.00 \n", - "11 NaN WTIV Position Onsite 2.000000 15000.00 \n", - "12 NaN WTIV Jackup 0.333333 2500.00 \n", - "13 NaN WTIV RovSurvey 1.000000 7500.00 \n", - "14 NaN WTIV Release Monopile 3.000000 22500.00 \n", - "15 NaN WTIV Upend Monopile 0.721000 5407.50 \n", - "16 NaN WTIV Lower Monopile 0.003500 26.25 \n", - "17 NaN WTIV Crane Reequip 1.000000 7500.00 \n", - "18 NaN WTIV Drive Monopile 1.500000 11250.00 \n", - "19 NaN WTIV Release Transition Piece 2.000000 15000.00 \n", - "20 NaN WTIV Crane Reequip 1.000000 7500.00 \n", - "21 NaN WTIV Lower TP 1.000000 7500.00 \n", - "22 NaN WTIV Bolt TP 4.000000 30000.00 \n", - "23 NaN WTIV Jackdown 0.333333 2500.00 \n", - "24 NaN WTIV Position Onsite 2.000000 15000.00 \n", - "\n", - " level time phase site_depth hub_height \n", - "0 ACTION 0.000000 Monopile Installation NaN NaN \n", - "1 ACTION 12.000000 Monopile Installation 25.0 120.0 \n", - "2 ACTION 20.000000 Monopile Installation 25.0 120.0 \n", - "3 ACTION 32.000000 Monopile Installation 25.0 120.0 \n", - "4 ACTION 40.000000 Monopile Installation 25.0 120.0 \n", - "5 ACTION 52.000000 Monopile Installation 25.0 120.0 \n", - "6 ACTION 60.000000 Monopile Installation 25.0 120.0 \n", - "7 ACTION 89.000000 Monopile Installation 25.0 120.0 \n", - "8 ACTION 101.000000 Monopile Installation 25.0 120.0 \n", - "9 ACTION 109.000000 Monopile Installation 25.0 120.0 \n", - "10 ACTION 114.000000 Monopile Installation NaN NaN \n", - "11 ACTION 116.000000 Monopile Installation NaN NaN \n", - "12 ACTION 116.333333 Monopile Installation 25.0 120.0 \n", - "13 ACTION 117.333333 Monopile Installation 25.0 120.0 \n", - "14 ACTION 120.333333 Monopile Installation NaN NaN \n", - "15 ACTION 121.054333 Monopile Installation 25.0 120.0 \n", - "16 ACTION 121.057833 Monopile Installation 25.0 120.0 \n", - "17 ACTION 122.057833 Monopile Installation 25.0 120.0 \n", - "18 ACTION 123.557833 Monopile Installation 25.0 120.0 \n", - "19 ACTION 125.557833 Monopile Installation NaN NaN \n", - "20 ACTION 126.557833 Monopile Installation 25.0 120.0 \n", - "21 ACTION 127.557833 Monopile Installation 25.0 120.0 \n", - "22 ACTION 131.557833 Monopile Installation 25.0 120.0 \n", - "23 ACTION 131.891167 Monopile Installation 25.0 120.0 \n", - "24 ACTION 133.891167 Monopile Installation NaN NaN " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(module.env.actions)\n", - "df.head(25) # Note the weather delay on line 7" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Including Feeder Barges" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installation CapEx: 23.02 M\n" - ] - } - ], - "source": [ - "# The MonopileInstallation module can also be configured to use a WTIV + Feeder Barge installation strategy.\n", - "# To configure this module to use feeder barges, add the 'num_feeders' and 'feeder' input to the config:\n", - "\n", - "config = {\n", - " 'site': {\n", - " 'depth': 25,\n", - " 'distance': 50\n", - " },\n", - " \n", - " 'plant': {\n", - " 'num_turbines': 50\n", - " },\n", - " \n", - " 'turbine': {\n", - " 'hub_height': 120\n", - " },\n", - " \n", - " # --- Vessels ---\n", - " 'wtiv': 'example_wtiv',\n", - " 'feeder': 'example_feeder',\n", - " 'num_feeders': 2,\n", - " \n", - " 'monopile': {\n", - " 'length': 72.1,\n", - " 'diameter': 10.2,\n", - " 'deck_space': 104.4,\n", - " 'mass': 1082.5,\n", - " 'unit_cost': 3788870\n", - " },\n", - " \n", - " 'transition_piece': {\n", - " 'deck_space': 108.9,\n", - " 'mass': 762.6,\n", - " 'unit_cost': 3431557\n", - " }\n", - "}\n", - "\n", - "module = MonopileInstallation(config)\n", - "module.run()\n", - "\n", - "print(f\"Installation CapEx: {module.installation_capex/1e6:.2f} M\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_multiplieragentactiondurationcostleveltimephaselocationsite_depth
01.0WTIVMobilize168.0000001.260000e+06ACTION0.000000Monopile InstallationNaNNaN
10.5Feeder 0Mobilize72.0000001.125000e+05ACTION0.000000Monopile InstallationNaNNaN
20.5Feeder 1Mobilize72.0000001.125000e+05ACTION0.000000Monopile InstallationNaNNaN
3NaNWTIVTransit5.0000003.750000e+04ACTION5.000000Monopile InstallationNaNNaN
4NaNFeeder 0Fasten Monopile12.0000003.750000e+04ACTION12.000000Monopile InstallationNaNNaN
5NaNFeeder 0Fasten Transition Piece8.0000002.500000e+04ACTION20.000000Monopile InstallationNaNNaN
6NaNFeeder 1Queue20.0000006.250000e+04ACTION20.000000Monopile InstallationNaNNaN
7NaNFeeder 0Transit8.3333332.604167e+04ACTION28.333333Monopile InstallationNaNNaN
8NaNFeeder 0Jackup1.6666675.208333e+03ACTION30.000000Monopile InstallationNaNNaN
9NaNWTIVDelay25.0000001.875000e+05ACTION30.000000Monopile InstallationSiteNaN
10NaNFeeder 1Fasten Monopile12.0000003.750000e+04ACTION32.000000Monopile InstallationNaNNaN
11NaNWTIVPosition Onsite2.0000001.500000e+04ACTION32.000000Monopile InstallationNaNNaN
12NaNWTIVJackup0.3333332.500000e+03ACTION32.333333Monopile InstallationNaN25.0
13NaNWTIVRovSurvey1.0000007.500000e+03ACTION33.333333Monopile InstallationNaN25.0
14NaNWTIVRelease Monopile3.0000002.250000e+04ACTION36.333333Monopile InstallationNaNNaN
15NaNWTIVUpend Monopile0.7210005.407500e+03ACTION37.054333Monopile InstallationNaN25.0
16NaNWTIVLower Monopile0.0035002.625000e+01ACTION37.057833Monopile InstallationNaN25.0
17NaNWTIVCrane Reequip1.0000007.500000e+03ACTION38.057833Monopile InstallationNaN25.0
18NaNWTIVDrive Monopile1.5000001.125000e+04ACTION39.557833Monopile InstallationNaN25.0
19NaNFeeder 1Fasten Transition Piece8.0000002.500000e+04ACTION40.000000Monopile InstallationNaNNaN
20NaNWTIVRelease Transition Piece2.0000001.500000e+04ACTION41.557833Monopile InstallationNaNNaN
21NaNFeeder 0ActiveFeeder11.5578333.611823e+04ACTION41.557833Monopile InstallationSiteNaN
22NaNWTIVCrane Reequip1.0000007.500000e+03ACTION42.557833Monopile InstallationNaN25.0
23NaNFeeder 0Jackdown1.6666675.208333e+03ACTION43.224500Monopile InstallationNaNNaN
24NaNWTIVLower TP1.0000007.500000e+03ACTION43.557833Monopile InstallationNaN25.0
\n", - "
" - ], - "text/plain": [ - " cost_multiplier agent action duration \\\n", - "0 1.0 WTIV Mobilize 168.000000 \n", - "1 0.5 Feeder 0 Mobilize 72.000000 \n", - "2 0.5 Feeder 1 Mobilize 72.000000 \n", - "3 NaN WTIV Transit 5.000000 \n", - "4 NaN Feeder 0 Fasten Monopile 12.000000 \n", - "5 NaN Feeder 0 Fasten Transition Piece 8.000000 \n", - "6 NaN Feeder 1 Queue 20.000000 \n", - "7 NaN Feeder 0 Transit 8.333333 \n", - "8 NaN Feeder 0 Jackup 1.666667 \n", - "9 NaN WTIV Delay 25.000000 \n", - "10 NaN Feeder 1 Fasten Monopile 12.000000 \n", - "11 NaN WTIV Position Onsite 2.000000 \n", - "12 NaN WTIV Jackup 0.333333 \n", - "13 NaN WTIV RovSurvey 1.000000 \n", - "14 NaN WTIV Release Monopile 3.000000 \n", - "15 NaN WTIV Upend Monopile 0.721000 \n", - "16 NaN WTIV Lower Monopile 0.003500 \n", - "17 NaN WTIV Crane Reequip 1.000000 \n", - "18 NaN WTIV Drive Monopile 1.500000 \n", - "19 NaN Feeder 1 Fasten Transition Piece 8.000000 \n", - "20 NaN WTIV Release Transition Piece 2.000000 \n", - "21 NaN Feeder 0 ActiveFeeder 11.557833 \n", - "22 NaN WTIV Crane Reequip 1.000000 \n", - "23 NaN Feeder 0 Jackdown 1.666667 \n", - "24 NaN WTIV Lower TP 1.000000 \n", - "\n", - " cost level time phase location \\\n", - "0 1.260000e+06 ACTION 0.000000 Monopile Installation NaN \n", - "1 1.125000e+05 ACTION 0.000000 Monopile Installation NaN \n", - "2 1.125000e+05 ACTION 0.000000 Monopile Installation NaN \n", - "3 3.750000e+04 ACTION 5.000000 Monopile Installation NaN \n", - "4 3.750000e+04 ACTION 12.000000 Monopile Installation NaN \n", - "5 2.500000e+04 ACTION 20.000000 Monopile Installation NaN \n", - "6 6.250000e+04 ACTION 20.000000 Monopile Installation NaN \n", - "7 2.604167e+04 ACTION 28.333333 Monopile Installation NaN \n", - "8 5.208333e+03 ACTION 30.000000 Monopile Installation NaN \n", - "9 1.875000e+05 ACTION 30.000000 Monopile Installation Site \n", - "10 3.750000e+04 ACTION 32.000000 Monopile Installation NaN \n", - "11 1.500000e+04 ACTION 32.000000 Monopile Installation NaN \n", - "12 2.500000e+03 ACTION 32.333333 Monopile Installation NaN \n", - "13 7.500000e+03 ACTION 33.333333 Monopile Installation NaN \n", - "14 2.250000e+04 ACTION 36.333333 Monopile Installation NaN \n", - "15 5.407500e+03 ACTION 37.054333 Monopile Installation NaN \n", - "16 2.625000e+01 ACTION 37.057833 Monopile Installation NaN \n", - "17 7.500000e+03 ACTION 38.057833 Monopile Installation NaN \n", - "18 1.125000e+04 ACTION 39.557833 Monopile Installation NaN \n", - "19 2.500000e+04 ACTION 40.000000 Monopile Installation NaN \n", - "20 1.500000e+04 ACTION 41.557833 Monopile Installation NaN \n", - "21 3.611823e+04 ACTION 41.557833 Monopile Installation Site \n", - "22 7.500000e+03 ACTION 42.557833 Monopile Installation NaN \n", - "23 5.208333e+03 ACTION 43.224500 Monopile Installation NaN \n", - "24 7.500000e+03 ACTION 43.557833 Monopile Installation NaN \n", - "\n", - " site_depth \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 NaN \n", - "8 NaN \n", - "9 NaN \n", - "10 NaN \n", - "11 NaN \n", - "12 25.0 \n", - "13 25.0 \n", - "14 NaN \n", - "15 25.0 \n", - "16 25.0 \n", - "17 25.0 \n", - "18 25.0 \n", - "19 NaN \n", - "20 NaN \n", - "21 NaN \n", - "22 25.0 \n", - "23 NaN \n", - "24 25.0 " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(module.env.actions)\n", - "df.head(25) # Note that there are no actions for two feeder barges interwoven into the actions list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/3. ProjectManager Introduction.ipynb b/examples/3. ProjectManager Introduction.ipynb deleted file mode 100644 index ea5ed34e..00000000 --- a/examples/3. ProjectManager Introduction.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ProjectManager\n", - "\n", - "ProjectManager is used to interact with multiple modules within ORBIT. This class allows any combination of modules to be configured and ran together to represent an entire project in ORBIT. It handles the configuration of each module and maps outputs of design modules into installation modules where necessary. This tutorial goes through how to build up a project level configuration using ProjectManager." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from ORBIT import ProjectManager" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'wtiv': 'dict | str',\n", - " 'feeder': 'dict | str (optional)',\n", - " 'num_feeders': 'int (optional)',\n", - " 'site': {'depth': 'm', 'distance': 'km', 'mean_windspeed': 'm/s'},\n", - " 'plant': {'num_turbines': 'int'},\n", - " 'turbine': {'hub_height': 'm',\n", - " 'rotor_diameter': 'm',\n", - " 'rated_windspeed': 'm/s'},\n", - " 'port': {'num_cranes': 'int (optional, default: 1)',\n", - " 'monthly_rate': 'USD/mo (optional)',\n", - " 'name': 'str (optional)'},\n", - " 'monopile_supply_chain': {'enabled': '(optional, default: False)',\n", - " 'substructure_delivery_time': 'h (optional, default: 168)',\n", - " 'num_substructures_delivered': 'int (optional: default: 1)',\n", - " 'substructure_storage': 'int (optional, default: inf)'},\n", - " 'monopile_design': {'yield_stress': 'Pa (optional)',\n", - " 'load_factor': 'float (optional)',\n", - " 'material_factor': 'float (optional)',\n", - " 'monopile_density': 'kg/m3 (optional)',\n", - " 'monopile_modulus': 'Pa (optional)',\n", - " 'monopile_tp_connection_thickness': 'm (optional)',\n", - " 'transition_piece_density': 'kg/m3 (optional)',\n", - " 'transition_piece_thickness': 'm (optional)',\n", - " 'transition_piece_length': 'm (optional)',\n", - " 'soil_coefficient': 'N/m3 (optional)',\n", - " 'air_density': 'kg/m3 (optional)',\n", - " 'weibull_scale_factor': 'float (optional)',\n", - " 'weibull_shape_factor': 'float (optional)',\n", - " 'turb_length_scale': 'm (optional)',\n", - " 'monopile_steel_cost': 'USD/t (optional)',\n", - " 'tp_steel_cost': 'USD/t (optional)'},\n", - " 'project_parameters': {'turbine_capex': '$/kW (optional, default: 1300)',\n", - " 'ncf': 'float (optional, default: 0.4)',\n", - " 'offtake_price': '$/MWh (optional, default: 80)',\n", - " 'project_lifetime': 'yrs (optional, default: 25)',\n", - " 'discount_rate': 'yearly (optional, default: .025)',\n", - " 'opex_rate': '$/kW/year (optional, default: 150)',\n", - " 'construction_insurance': '$/kW (optional, default: 44)',\n", - " 'construction_financing': '$/kW (optional, default: 183)',\n", - " 'contingency': '$/kW (optional, default: 316)',\n", - " 'commissioning': '$/kW (optional, default: 44)',\n", - " 'decommissioning': '$/kW (optional, default: 58)',\n", - " 'site_auction_price': '$ (optional, default: 100e6)',\n", - " 'site_assessment_cost': '$ (optional, default: 50e6)',\n", - " 'construction_plan_cost': '$ (optional, default: 1e6)',\n", - " 'installation_plan_cost': '$ (optional, default: 0.25e6)'},\n", - " 'design_phases': ['MonopileDesign'],\n", - " 'install_phases': ['MonopileInstallation'],\n", - " 'orbit_version': 'v1.0.7+112.g52fef86'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The compile expected configs for multiple modules within ProjectManager, use the 'compile_input_dict' method:\n", - "# In this example, we'll configure ProjectManager to run the MonopileDesign and MonopileInstallation modules.\n", - "\n", - "ProjectManager.compile_input_dict([\"MonopileDesign\", \"MonopileInstallation\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ORBIT library intialized at '/Users/nriccobo/GitHub/ORBIT/library'\n", - "Total Substructure Cost: 258.67 M\n", - "Total Installation Cost: 25.67 M\n" - ] - } - ], - "source": [ - "# For simplicity, we are going to ignore the optional 'monopile_design' and 'project_parameters' subdicts.\n", - "\n", - "config = {\n", - " 'wtiv': 'example_wtiv',\n", - " 'site': { # The inputs required for the design module and\n", - " 'depth': 20, # the installation module are combined into the 'site' subdict\n", - " 'distance': 50,\n", - " 'mean_windspeed': 9.5\n", - " },\n", - "\n", - " 'plant': {\n", - " 'num_turbines': 50\n", - " },\n", - "\n", - " 'turbine': {\n", - " 'rotor_diameter': 220,\n", - " 'hub_height': 120,\n", - " 'rated_windspeed': 13\n", - " },\n", - "\n", - " # Sizing information for the substructure are not required as they will\n", - " # be calculated by 'MonopileDesign' and passed into 'MonopileInstallation'\n", - " # automatically by 'ProjecManager'.\n", - "\n", - " # --- Module Definitions ---\n", - " 'design_phases': ['MonopileDesign'],\n", - " 'install_phases': ['MonopileInstallation'],\n", - "}\n", - "\n", - "project = ProjectManager(config)\n", - "project.run()\n", - "\n", - "print(f\"Total Substructure Cost: {project.system_capex/1e6:.2f} M\")\n", - "print(f\"Total Installation Cost: {project.installation_capex/1e6:.2f} M\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Weather" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: /var/folders/90/1lkt657x3n1cw5x65j3lfgd5406fb8/T/ipykernel_14087/1181267332.py:3\n", - "Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Substructure Cost: 258.67 M\n", - "Total Installation Cost: 31.62 M\n" - ] - } - ], - "source": [ - "# Weather can be included in the same way as an individual module:\n", - "import pandas as pd\n", - "weather = pd.read_csv(\"data/example_weather.csv\", parse_dates=['datetime']).set_index(\"datetime\")\n", - "\n", - "project = ProjectManager(config, weather=weather)\n", - "project.run()\n", - "\n", - "print(f\"Total Substructure Cost: {project.system_capex/1e6:.2f} M\")\n", - "print(f\"Total Installation Cost: {project.installation_capex/1e6:.2f} M\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Looking inside a module\n", - "Look at intermediate variables within a module in two ways.\n", - "1. Call the phase of the ProjectManager\n", - "2. Run the module on its own and call the variables\n", - "\n", - "These should yield the same results" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Monopile Cost: $258.67 M\n" - ] - } - ], - "source": [ - "# 1. Call through ProjectManager\n", - "project = ProjectManager(config)\n", - "project.run()\n", - "\n", - "print(f\"Total Monopile Cost: ${project.phases['MonopileDesign'].total_cost/1e6:.2f} M\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Monopile Cost: $258.67 M\n" - ] - } - ], - "source": [ - "# 2. Call through the module\n", - "from ORBIT.phases.design import MonopileDesign\n", - "\n", - "design = MonopileDesign(config)\n", - "design.run()\n", - "\n", - "print(f\"Total Monopile Cost: ${design.total_cost/1e6:.2f} M\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.18" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/4. Example Fixed Project.ipynb b/examples/4. Example Fixed Project.ipynb deleted file mode 100644 index 90c3beda..00000000 --- a/examples/4. Example Fixed Project.ipynb +++ /dev/null @@ -1,921 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example Fixed Project\n", - "\n", - "The rest of this tutorial uses pre compiled ORBIT configs that are stored as .yaml files in the '~/configs/ folder. There are load and save methods available in ORBIT for working with .yaml files. These example projects each exhibit different functionalities within ORBIT. Using these examples and combinations of them, most project configurations can be modeled. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "from ORBIT import ProjectManager, load_config\n", - "\n", - "weather = pd.read_csv(\"data/example_weather.csv\", parse_dates=[\"datetime\"])\\\n", - " .set_index(\"datetime\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the project configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Num turbines: 50\n", - "Turbine: SWT_6MW_154m_110m\n", - "\n", - "Site: {'depth': 22.5, 'distance': 124, 'distance_to_landfall': 35, 'mean_windspeed': 9}\n" - ] - } - ], - "source": [ - "fixed_config = load_config(\"configs/example_fixed_project.yaml\") # Configs can be loaded with absolute or relative paths\n", - "\n", - "print(type(fixed_config)) # They are loaded in as dictionaries.\n", - "\n", - "print(f\"Num turbines: {fixed_config['plant']['num_turbines']}\") # Once a configuration is loaded, different parameters can \n", - "print(f\"Turbine: {fixed_config['turbine']}\") # be accessed using dict access.\n", - "print(f\"\\nSite: {fixed_config['site']}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Phases\n", - "\n", - "This fixed project represents a generic Offshore Wind farm with 50 6MW turbines. It includes 5 design modules and 6 installation modules as seen below. This is a common set of modules to run for a fixed bottom project. This config will model the procurement and installation of monopiles, scour protection, array system, export system, offshore substation and the turbines." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Design phases: ['MonopileDesign', 'ScourProtectionDesign', 'ArraySystemDesign', 'ExportSystemDesign', 'OffshoreSubstationDesign']\n", - "\n", - "Install phases: ['ArrayCableInstallation', 'ExportCableInstallation', 'MonopileInstallation', 'OffshoreSubstationInstallation', 'ScourProtectionInstallation', 'TurbineInstallation']\n" - ] - } - ], - "source": [ - "print(f\"Design phases: {fixed_config['design_phases']}\")\n", - "print(f\"\\nInstall phases: {list(fixed_config['install_phases'].keys())}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run\n", - "\n", - "This project is always being modeled with the example weather project supplied that is representative of US East Coast wind farm locations." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ORBIT library intialized at '/Users/jnunemak/Fun/repos/ORBIT/library'\n" - ] - } - ], - "source": [ - "project = ProjectManager(fixed_config, weather=weather)\n", - "project.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Top Level Outputs\n", - "\n", - "ProjectManager offers several high level result categories:\n", - "- Installation CapEx\n", - "- System CapEx (procurement of BOS subcomponents)\n", - "- Turbine CapEx\n", - "- Soft CapEx (project management costs)\n", - "- Total CapEx\n", - "- Total installation time\n", - "- etc." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installation CapEx: 181 M\n", - "System CapEx: 257 M\n", - "Turbine CapEx: 390 M\n", - "Soft CapEx: 194 M\n", - "Total CapEx: 1173 M\n", - "\n", - "Installation Time: 12731 h\n" - ] - } - ], - "source": [ - "print(f\"Installation CapEx: {project.installation_capex/1e6:.0f} M\")\n", - "print(f\"System CapEx: {project.system_capex/1e6:.0f} M\")\n", - "print(f\"Turbine CapEx: {project.turbine_capex/1e6:.0f} M\")\n", - "print(f\"Soft CapEx: {project.soft_capex/1e6:.0f} M\")\n", - "print(f\"Total CapEx: {project.total_capex/1e6:.0f} M\")\n", - "\n", - "print(f\"\\nInstallation Time: {project.installation_time:.0f} h\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CapEx Breakdown" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Array System': 24416575.834140003,\n", - " 'Export System': 22813500.0,\n", - " 'Offshore Substation': 49739550.0,\n", - " 'Scour Protection': 5896000,\n", - " 'Substructure': 154436243.91851607,\n", - " 'Array System Installation': 19828893.780554257,\n", - " 'Export System Installation': 63231897.48006167,\n", - " 'Offshore Substation Installation': 4323839.723173516,\n", - " 'Scour Protection Installation': 19613097.60273973,\n", - " 'Substructure Installation': 28858058.87808971,\n", - " 'Turbine Installation': 44667905.25114152,\n", - " 'Turbine': 390000000,\n", - " 'Soft': 193500000,\n", - " 'Project': 151250000.0}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The breakdown of project costs by module is available at 'capex_breakdown'\n", - "project.capex_breakdown" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation Actions" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " cost_multiplier agent action duration cost \\\n", - "517 1.0 WTIV Mobilize 168.000000 1260000.0 \n", - "523 NaN WTIV Fasten Tower Section 4.000000 30000.0 \n", - "527 NaN WTIV Fasten Tower Section 4.000000 30000.0 \n", - "534 NaN WTIV Fasten Nacelle 4.000000 30000.0 \n", - "536 NaN WTIV Fasten Blade 1.500000 11250.0 \n", - "... ... ... ... ... ... \n", - "3098 NaN WTIV Attach Blade 3.500000 26250.0 \n", - "3099 NaN WTIV Release Blade 1.000000 7500.0 \n", - "3100 NaN WTIV Lift Blade 1.100000 8250.0 \n", - "3101 NaN WTIV Attach Blade 3.500000 26250.0 \n", - "3102 NaN WTIV Jackdown 0.316667 2375.0 \n", - "\n", - " level time phase phase_name \\\n", - "517 ACTION 1524.932005 TurbineInstallation NaN \n", - "523 ACTION 1528.932005 TurbineInstallation TurbineInstallation \n", - "527 ACTION 1532.932005 TurbineInstallation TurbineInstallation \n", - "534 ACTION 1536.932005 TurbineInstallation TurbineInstallation \n", - "536 ACTION 1538.432005 TurbineInstallation TurbineInstallation \n", - "... ... ... ... ... \n", - "3098 ACTION 5758.182005 TurbineInstallation TurbineInstallation \n", - "3099 ACTION 5759.182005 TurbineInstallation NaN \n", - "3100 ACTION 5760.282005 TurbineInstallation TurbineInstallation \n", - "3101 ACTION 5763.782005 TurbineInstallation TurbineInstallation \n", - "3102 ACTION 5764.098671 TurbineInstallation TurbineInstallation \n", - "\n", - " max_waveheight max_windspeed transit_speed location site_depth \\\n", - "517 NaN NaN NaN NaN NaN \n", - "523 NaN NaN NaN NaN 22.5 \n", - "527 NaN NaN NaN NaN 22.5 \n", - "534 NaN NaN NaN NaN 22.5 \n", - "536 NaN NaN NaN NaN 22.5 \n", - "... ... ... ... ... ... \n", - "3098 NaN NaN NaN NaN 22.5 \n", - "3099 NaN NaN NaN NaN NaN \n", - "3100 NaN NaN NaN NaN 22.5 \n", - "3101 NaN NaN NaN NaN 22.5 \n", - "3102 NaN NaN NaN NaN 22.5 \n", - "\n", - " hub_height \n", - "517 NaN \n", - "523 110.0 \n", - "527 110.0 \n", - "534 110.0 \n", - "536 110.0 \n", - "... ... \n", - "3098 110.0 \n", - "3099 NaN \n", - "3100 110.0 \n", - "3101 110.0 \n", - "3102 110.0 \n", - "\n", - "[1505 rows x 15 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# These logs can be sorted by phase by using DataFrame operations\n", - "\n", - "turbine_install = df.loc[df['phase']==\"TurbineInstallation\"]\n", - "turbine_install" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "action\n", - "Attach Blade 525.000000\n", - "Attach Nacelle 300.000000\n", - "Attach Tower Section 600.000000\n", - "Delay 669.000000\n", - "Fasten Blade 225.000000\n", - "Fasten Nacelle 200.000000\n", - "Fasten Tower Section 400.000000\n", - "Jackdown 15.833333\n", - "Jackup 15.833333\n", - "Lift Blade 165.000000\n", - "Lift Nacelle 55.000000\n", - "Lift Tower Section 82.500000\n", - "Mobilize 168.000000\n", - "Position Onsite 100.000000\n", - "Reequip 100.000000\n", - "Release Blade 150.000000\n", - "Release Nacelle 150.000000\n", - "Release Tower Section 300.000000\n", - "Transit 186.000000\n", - "Name: duration, dtype: float64" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Operations can also be grouped to see a total amount of time spend on each operation\n", - "\n", - "turbine_install.groupby([\"action\"]).sum()['duration']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/5. Example Floating Project.ipynb b/examples/5. Example Floating Project.ipynb deleted file mode 100644 index 68a22700..00000000 --- a/examples/5. Example Floating Project.ipynb +++ /dev/null @@ -1,760 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example Floating Project\n", - "\n", - "This tutorial uses prepared ORBIT configs that are stored as .yaml files in the `~/configs/` folder. These example projects each exhibit different functionalities within ORBIT. Using these examples and combinations of them, most project configurations can be modeled. \n", - "\n", - "Last updated: September 2024\n", - "\n", - "1. Run the example floating project and print outputs\n", - "2. Replace the anchor_type and mooring_type and print outputs \n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: /var/folders/90/1lkt657x3n1cw5x65j3lfgd5406fb8/T/ipykernel_98169/3749054979.py:9\n", - "Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format." - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "from copy import deepcopy\n", - "from pprint import pprint\n", - "from ORBIT import ProjectManager, load_config\n", - "\n", - "import warnings\n", - "warnings.filterwarnings(\"default\")\n", - "\n", - "weather = pd.read_csv(\"data/example_weather.csv\", parse_dates=[\"datetime\"])\\\n", - " .set_index(\"datetime\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Run the example floating project \n", - "\n", - "#### Load the project configuration\n", - "`~/configs/example_floating_project.yaml`" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Num turbines: 50\n", - "Turbine: 12MW_generic\n", - "\n", - "Site: {'depth': 900, 'distance': 100, 'distance_to_landfall': 100}\n" - ] - } - ], - "source": [ - "floating_config = load_config(\"configs/example_floating_project.yaml\")\n", - "\n", - "print(f\"Num turbines: {floating_config['plant']['num_turbines']}\")\n", - "print(f\"Turbine: {floating_config['turbine']}\")\n", - "print(f\"\\nSite: {floating_config['site']}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Phases" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(\"Design phases: ['ArraySystemDesign', 'ElectricalDesign', \"\n", - " \"'MooringSystemDesign', 'OffshoreFloatingSubstationDesign', \"\n", - " \"'SemiSubmersibleDesign']\")\n", - "('\\n'\n", - " \"Install phases: ['ArrayCableInstallation', 'ExportCableInstallation', \"\n", - " \"'MooredSubInstallation', 'MooringSystemInstallation', \"\n", - " \"'FloatingSubstationInstallation', 'TurbineInstallation']\")\n" - ] - } - ], - "source": [ - "print(f\"Design phases: {floating_config['design_phases']}\")\n", - "print(f\"\\nInstall phases: {list(floating_config['install_phases'].keys())}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DeprecationWarning: /Users/nriccobo/GitHub/ORBIT/ORBIT/phases/install/quayside_assembly_tow/moored.py:94\n", - "support_vessel will be deprecated and replaced with towing_vessels and ahts_vessel in the towing groups.\n", - "DeprecationWarning: /Users/nriccobo/GitHub/ORBIT/ORBIT/phases/install/quayside_assembly_tow/moored.py:94\n", - "['towing_vessl_groups]['station_keeping_vessels'] will be deprecated and replaced with ['towing_vessl_groups]['ahts_vessels'].\n" - ] - } - ], - "source": [ - "project = ProjectManager(floating_config, weather=weather)\n", - "project.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Top Level Outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installation CapEx: 521 M\n", - "System CapEx: 1444 M\n", - "Turbine CapEx: 780 M\n", - "Soft CapEx: 387 M\n", - "Total CapEx: 3283 M\n", - "\n", - "Installation Time: 41147 h\n" - ] - } - ], - "source": [ - "print(f\"Installation CapEx: {project.installation_capex/1e6:.0f} M\")\n", - "print(f\"System CapEx: {project.system_capex/1e6:.0f} M\")\n", - "print(f\"Turbine CapEx: {project.turbine_capex/1e6:.0f} M\")\n", - "print(f\"Soft CapEx: {project.soft_capex/1e6:.0f} M\")\n", - "print(f\"Total CapEx: {project.total_capex/1e6:.0f} M\")\n", - "\n", - "print(f\"\\nInstallation Time: {project.installation_time:.0f} h\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CapEx Breakdown" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Array System': 94.97179434403438,\n", - " 'Export System': 432.13532047999996,\n", - " 'Substructure': 1051.1827276666668,\n", - " 'Mooring System': 552.2987080136722,\n", - " 'Offshore Substation': 276.52514805568075,\n", - " 'Array System Installation': 105.04624474280226,\n", - " 'Export System Installation': 246.79354615177581,\n", - " 'Substructure Installation': 208.2509277379141,\n", - " 'Mooring System Installation': 83.49086757990867,\n", - " 'Offshore Substation Installation': 11.784658802638255,\n", - " 'Turbine Installation': 212.89678462709279,\n", - " 'Turbine': 1300.0,\n", - " 'Soft': 645.0,\n", - " 'Project': 252.08333333333334}" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "project.capex_breakdown_per_kw" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation Actions" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_multiplieragentactiondurationcostleveltimephaselocationsite_depthhub_heightphase_namemax_waveheightmax_windspeedtransit_speednum_vesselsnum_ahts_vessels
00.5Array Cable Installation VesselMobilize72.0000003.375000e+05ACTION0.000000ArrayCableInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
10.5Export Cable Installation VesselMobilize72.0000003.375000e+05ACTION0.000000ExportCableInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
2NaNOnshore ConstructionOnshore Construction0.0000001.665604e+06ACTION0.000000ExportCableInstallationLandfallNaNNaNNaNNaNNaNNaNNaNNaN
31.0Mooring System Installation VesselMobilize168.0000007.000000e+05ACTION0.000000MooringSystemInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
4NaNSubstation Assembly Line 1Substation Substructure Assembly0.0000000.000000e+00ACTION0.000000FloatingSubstationInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
......................................................
4521NaNExport Cable Installation VesselPull In Cable5.5000005.156250e+04ACTION12017.280762ExportCableInstallationNaNNaNNaNExportCableInstallationNaNNaNNaNNaNNaN
4522NaNExport Cable Installation VesselTerminate Cable5.5000005.156250e+04ACTION12022.780762ExportCableInstallationNaNNaNNaNExportCableInstallationNaNNaNNaNNaNNaN
4523NaNExport Cable Installation VesselTransit8.0000007.500000e+04ACTION12030.780762ExportCableInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
4524NaNExport Cable Installation VesselDelay26.0000002.437500e+05ACTION12056.780762ExportCableInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
4525NaNExport Cable Installation VesselTransit0.6956526.521739e+03ACTION12057.476414ExportCableInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" - ], - "text/plain": [ - " cost_multiplier agent \\\n", - "0 0.5 Array Cable Installation Vessel \n", - "1 0.5 Export Cable Installation Vessel \n", - "2 NaN Onshore Construction \n", - "3 1.0 Mooring System Installation Vessel \n", - "4 NaN Substation Assembly Line 1 \n", - "... ... ... \n", - "4521 NaN Export Cable Installation Vessel \n", - "4522 NaN Export Cable Installation Vessel \n", - "4523 NaN Export Cable Installation Vessel \n", - "4524 NaN Export Cable Installation Vessel \n", - "4525 NaN Export Cable Installation Vessel \n", - "\n", - " action duration cost level \\\n", - "0 Mobilize 72.000000 3.375000e+05 ACTION \n", - "1 Mobilize 72.000000 3.375000e+05 ACTION \n", - "2 Onshore Construction 0.000000 1.665604e+06 ACTION \n", - "3 Mobilize 168.000000 7.000000e+05 ACTION \n", - "4 Substation Substructure Assembly 0.000000 0.000000e+00 ACTION \n", - "... ... ... ... ... \n", - "4521 Pull In Cable 5.500000 5.156250e+04 ACTION \n", - "4522 Terminate Cable 5.500000 5.156250e+04 ACTION \n", - "4523 Transit 8.000000 7.500000e+04 ACTION \n", - "4524 Delay 26.000000 2.437500e+05 ACTION \n", - "4525 Transit 0.695652 6.521739e+03 ACTION \n", - "\n", - " time phase location site_depth \\\n", - "0 0.000000 ArrayCableInstallation NaN NaN \n", - "1 0.000000 ExportCableInstallation NaN NaN \n", - "2 0.000000 ExportCableInstallation Landfall NaN \n", - "3 0.000000 MooringSystemInstallation NaN NaN \n", - "4 0.000000 FloatingSubstationInstallation NaN NaN \n", - "... ... ... ... ... \n", - "4521 12017.280762 ExportCableInstallation NaN NaN \n", - "4522 12022.780762 ExportCableInstallation NaN NaN \n", - "4523 12030.780762 ExportCableInstallation NaN NaN \n", - "4524 12056.780762 ExportCableInstallation NaN NaN \n", - "4525 12057.476414 ExportCableInstallation NaN NaN \n", - "\n", - " hub_height phase_name max_waveheight max_windspeed \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "... ... ... ... ... \n", - "4521 NaN ExportCableInstallation NaN NaN \n", - "4522 NaN ExportCableInstallation NaN NaN \n", - "4523 NaN NaN NaN NaN \n", - "4524 NaN NaN NaN NaN \n", - "4525 NaN NaN NaN NaN \n", - "\n", - " transit_speed num_vessels num_ahts_vessels \n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "... ... ... ... \n", - "4521 NaN NaN NaN \n", - "4522 NaN NaN NaN \n", - "4523 NaN NaN NaN \n", - "4524 NaN NaN NaN \n", - "4525 NaN NaN NaN \n", - "\n", - "[4526 rows x 17 columns]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(project.actions)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mooring System Design:\n", - "{'anchor_cost': 190951.03604101657,\n", - " 'anchor_mass': 50,\n", - " 'anchor_type': 'Suction Pile',\n", - " 'line_cost': 1465945.088,\n", - " 'line_diam': 0.15,\n", - " 'line_length': 1347.376,\n", - " 'line_mass': 606.3192,\n", - " 'mooring_type': 'Catenary',\n", - " 'num_lines': 4,\n", - " 'system_cost': 331379224.80820334}\n", - "\n", - "Mooring System: $/kW\n", - "$ 552.3\n", - "$ 83.49\n" - ] - } - ], - "source": [ - "print(\"Mooring System Design:\")\n", - "pprint(project.design_results[\"mooring_system\"])\n", - "\n", - "print(\"\\nMooring System: $/kW\")\n", - "print(\"$\", round(project.capex_breakdown_per_kw['Mooring System'], 2))\n", - "\n", - "print(\"$\", round(project.capex_breakdown_per_kw['Mooring System Installation'], 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Replace anchor and mooring types " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DeprecationWarning: /Users/nriccobo/GitHub/ORBIT/ORBIT/phases/install/quayside_assembly_tow/moored.py:94\n", - "support_vessel will be deprecated and replaced with towing_vessels and ahts_vessel in the towing groups.\n", - "DeprecationWarning: /Users/nriccobo/GitHub/ORBIT/ORBIT/phases/install/quayside_assembly_tow/moored.py:94\n", - "['towing_vessl_groups]['station_keeping_vessels'] will be deprecated and replaced with ['towing_vessl_groups]['ahts_vessels'].\n" - ] - } - ], - "source": [ - "semitaut_config = deepcopy(floating_config)\n", - "semitaut_config['mooring_system_design']['anchor_type'] = 'Drag Embedment'\n", - "semitaut_config['mooring_system_design']['mooring_type'] = 'Semitaut'\n", - "\n", - "project_semitaut = ProjectManager(semitaut_config, weather=weather)\n", - "project_semitaut.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installation CapEx: 519 M\n", - "System CapEx: 1440 M\n", - "Turbine CapEx: 780 M\n", - "Soft CapEx: 387 M\n", - "Total CapEx: 3278 M\n", - "\n", - "Installation Time: 41147 h\n" - ] - } - ], - "source": [ - "print(f\"Installation CapEx: {project_semitaut.installation_capex/1e6:.0f} M\")\n", - "print(f\"System CapEx: {project_semitaut.system_capex/1e6:.0f} M\")\n", - "print(f\"Turbine CapEx: {project_semitaut.turbine_capex/1e6:.0f} M\")\n", - "print(f\"Soft CapEx: {project_semitaut.soft_capex/1e6:.0f} M\")\n", - "print(f\"Total CapEx: {project_semitaut.total_capex/1e6:.0f} M\")\n", - "\n", - "print(f\"\\nInstallation Time: {project.installation_time:.0f} h\")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Array System': 94.97179434403438,\n", - " 'Export System': 432.13532047999996,\n", - " 'Substructure': 1051.1827276666668,\n", - " 'Mooring System': 545.7798,\n", - " 'Offshore Substation': 276.3947698954073,\n", - " 'Array System Installation': 105.04624474280226,\n", - " 'Export System Installation': 246.79354615177581,\n", - " 'Substructure Installation': 208.2509277379141,\n", - " 'Mooring System Installation': 80.80888508371386,\n", - " 'Offshore Substation Installation': 11.784658802638255,\n", - " 'Turbine Installation': 212.89678462709279,\n", - " 'Turbine': 1300.0,\n", - " 'Soft': 645.0,\n", - " 'Project': 252.08333333333334}" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "project_semitaut.capex_breakdown_per_kw" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mooring System Design:\n", - "{'anchor_cost': 139426.2,\n", - " 'anchor_mass': 20,\n", - " 'anchor_type': 'Drag Embedment',\n", - " 'line_cost': 1497913.2,\n", - " 'line_diam': 0.15,\n", - " 'line_length': 1755.71,\n", - " 'line_mass': 579.8762530880001,\n", - " 'mooring_type': 'Semitaut',\n", - " 'num_lines': 4,\n", - " 'system_cost': 327467880.0}\n", - "\n", - "Mooring System: $/kW\n", - "$ 545.78\n", - "$ 80.81\n" - ] - } - ], - "source": [ - "print(\"Mooring System Design:\")\n", - "pprint(project_semitaut.design_results[\"mooring_system\"])\n", - "\n", - "print(\"\\nMooring System: $/kW\")\n", - "print(\"$\", round(project_semitaut.capex_breakdown_per_kw['Mooring System'], 2))\n", - "\n", - "print(\"$\", round(project_semitaut.capex_breakdown_per_kw['Mooring System Installation'], 2))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Example - Cable Install Configurations.ipynb b/examples/Example - Cable Install Configurations.ipynb deleted file mode 100644 index 4b2a2f46..00000000 --- a/examples/Example - Cable Install Configurations.ipynb +++ /dev/null @@ -1,887 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ORBIT Example - Cable Installation Options\n", - "\n", - "Last Updated: 07/88/2021" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from ORBIT import ProjectManager" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### ArrayCableInstallation Module" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_multiplieragentactiondurationcostleveltimephasephase_namemax_waveheightmax_windspeedtransit_speed
00.5Array Cable Installation VesselMobilize72.000000180000.000000ACTION0.000000ArrayCableInstallationNaNNaNNaNNaN
1NaNArray Cable Installation VesselLoad Cable6.00000030000.000000ACTION6.000000ArrayCableInstallationArrayCableInstallationNaNNaNNaN
2NaNArray Cable Installation VesselTransit1.7391308695.652174ACTION7.739130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
3NaNArray Cable Installation VesselPosition Onsite2.00000010000.000000ACTION9.739130ArrayCableInstallationNaNNaNNaNNaN
4NaNArray Cable Installation VesselPrepare Cable1.0000005000.000000ACTION10.739130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
5NaNArray Cable Installation VesselPull In Cable5.50000027500.000000ACTION16.239130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
6NaNArray Cable Installation VesselTerminate Cable5.50000027500.000000ACTION21.739130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
7NaNArray Cable Installation VesselLower Cable1.0000005000.000000ACTION22.739130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
8NaNArray Cable Installation VesselLay/Bury Cable6.66666733333.333333ACTION29.405797ArrayCableInstallationArrayCableInstallation2.025.011.5
9NaNArray Cable Installation VesselPrepare Cable1.0000005000.000000ACTION30.405797ArrayCableInstallationArrayCableInstallationNaNNaNNaN
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" - ], - "text/plain": [ - " cost_multiplier agent action \\\n", - "0 0.5 Array Cable Installation Vessel Mobilize \n", - "1 NaN Array Cable Installation Vessel Load Cable \n", - "2 NaN Array Cable Installation Vessel Transit \n", - "3 NaN Array Cable Installation Vessel Position Onsite \n", - "4 NaN Array Cable Installation Vessel Prepare Cable \n", - "5 NaN Array Cable Installation Vessel Pull In Cable \n", - "6 NaN Array Cable Installation Vessel Terminate Cable \n", - "7 NaN Array Cable Installation Vessel Lower Cable \n", - "8 NaN Array Cable Installation Vessel Lay/Bury Cable \n", - "9 NaN Array Cable Installation Vessel Prepare Cable \n", - "\n", - " duration cost level time phase \\\n", - "0 72.000000 180000.000000 ACTION 0.000000 ArrayCableInstallation \n", - "1 6.000000 30000.000000 ACTION 6.000000 ArrayCableInstallation \n", - "2 1.739130 8695.652174 ACTION 7.739130 ArrayCableInstallation \n", - "3 2.000000 10000.000000 ACTION 9.739130 ArrayCableInstallation \n", - "4 1.000000 5000.000000 ACTION 10.739130 ArrayCableInstallation \n", - "5 5.500000 27500.000000 ACTION 16.239130 ArrayCableInstallation \n", - "6 5.500000 27500.000000 ACTION 21.739130 ArrayCableInstallation \n", - "7 1.000000 5000.000000 ACTION 22.739130 ArrayCableInstallation \n", - "8 6.666667 33333.333333 ACTION 29.405797 ArrayCableInstallation \n", - "9 1.000000 5000.000000 ACTION 30.405797 ArrayCableInstallation \n", - "\n", - " phase_name max_waveheight max_windspeed transit_speed \n", - "0 NaN NaN NaN NaN \n", - "1 ArrayCableInstallation NaN NaN NaN \n", - "2 ArrayCableInstallation NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 ArrayCableInstallation NaN NaN NaN \n", - "5 ArrayCableInstallation NaN NaN NaN \n", - "6 ArrayCableInstallation NaN NaN NaN \n", - "7 ArrayCableInstallation NaN NaN NaN \n", - "8 ArrayCableInstallation 2.0 25.0 11.5 \n", - "9 ArrayCableInstallation NaN NaN NaN " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The configuration below can be modified to change the installation strategies utilized in ArrayCableInstallation module\n", - "# by toggling on/off which vessels are configured:\n", - "\n", - "config = {\n", - " \"array_cable_install_vessel\": \"example_cable_lay_vessel\", # This vessel will perform a simultaneous lay/bury installation strategy\n", - " # as there is no 'bury_vessel' defined in the config\n", - "\n", - "# \"array_cable_bury_vessel\": \"example_cable_lay_vessel\", # <--- Commented out. Will be ignored by the code.\n", - "# \"array_cable_trench_vessel\": \"example_cable_lay_vessel\", # <--- Commented out. Will be ignored by the code.\n", - " \n", - " \"site\": {\"distance\": 20, \"depth\": 35},\n", - " \"port\": {},\n", - " \"array_system\": {\n", - " \"system_cost\": 50e6,\n", - " \"cables\": {\n", - " \"ExampleCable\": {\n", - " \"linear_density\": 40, # t/km\n", - " \"cable_sections\": [(2, 25), (1, 25)] # (length, num) pairs. This example: 25 2km cables and 25 1km cables .\n", - " }\n", - " }\n", - " },\n", - " \n", - " \"install_phases\": [\"ArrayCableInstallation\"]\n", - "}\n", - "\n", - "# Run\n", - "project = ProjectManager(config)\n", - "project.run()\n", - "\n", - "# Outputs\n", - "df = pd.DataFrame(project.actions)\n", - "df.iloc[0:10] # Notice the action \"Lay/Bury Cable\" in row 8." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Including a separate burial vessel" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_multiplieragentactiondurationcostleveltimephasephase_namemax_waveheightmax_windspeedtransit_speed
00.5Array Cable Installation VesselMobilize72.00000180000.000000ACTION0.00000ArrayCableInstallationNaNNaNNaNNaN
10.5Array Cable Burial VesselMobilize72.00000180000.000000ACTION0.00000ArrayCableInstallationNaNNaNNaNNaN
2NaNArray Cable Installation VesselLoad Cable6.0000030000.000000ACTION6.00000ArrayCableInstallationArrayCableInstallationNaNNaNNaN
3NaNArray Cable Installation VesselTransit1.739138695.652174ACTION7.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
4NaNArray Cable Installation VesselPosition Onsite2.0000010000.000000ACTION9.73913ArrayCableInstallationNaNNaNNaNNaN
5NaNArray Cable Installation VesselPrepare Cable1.000005000.000000ACTION10.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
6NaNArray Cable Installation VesselPull In Cable5.5000027500.000000ACTION16.23913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
7NaNArray Cable Installation VesselTerminate Cable5.5000027500.000000ACTION21.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
8NaNArray Cable Installation VesselLower Cable1.000005000.000000ACTION22.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
9NaNArray Cable Installation VesselLay Cable2.0000010000.000000ACTION24.73913ArrayCableInstallationArrayCableInstallation2.025.011.5
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" - ], - "text/plain": [ - " cost_multiplier agent action \\\n", - "0 0.5 Array Cable Installation Vessel Mobilize \n", - "1 0.5 Array Cable Burial Vessel Mobilize \n", - "2 NaN Array Cable Installation Vessel Load Cable \n", - "3 NaN Array Cable Installation Vessel Transit \n", - "4 NaN Array Cable Installation Vessel Position Onsite \n", - "5 NaN Array Cable Installation Vessel Prepare Cable \n", - "6 NaN Array Cable Installation Vessel Pull In Cable \n", - "7 NaN Array Cable Installation Vessel Terminate Cable \n", - "8 NaN Array Cable Installation Vessel Lower Cable \n", - "9 NaN Array Cable Installation Vessel Lay Cable \n", - "\n", - " duration cost level time phase \\\n", - "0 72.00000 180000.000000 ACTION 0.00000 ArrayCableInstallation \n", - "1 72.00000 180000.000000 ACTION 0.00000 ArrayCableInstallation \n", - "2 6.00000 30000.000000 ACTION 6.00000 ArrayCableInstallation \n", - "3 1.73913 8695.652174 ACTION 7.73913 ArrayCableInstallation \n", - "4 2.00000 10000.000000 ACTION 9.73913 ArrayCableInstallation \n", - "5 1.00000 5000.000000 ACTION 10.73913 ArrayCableInstallation \n", - "6 5.50000 27500.000000 ACTION 16.23913 ArrayCableInstallation \n", - "7 5.50000 27500.000000 ACTION 21.73913 ArrayCableInstallation \n", - "8 1.00000 5000.000000 ACTION 22.73913 ArrayCableInstallation \n", - "9 2.00000 10000.000000 ACTION 24.73913 ArrayCableInstallation \n", - "\n", - " phase_name max_waveheight max_windspeed transit_speed \n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 ArrayCableInstallation NaN NaN NaN \n", - "3 ArrayCableInstallation NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "5 ArrayCableInstallation NaN NaN NaN \n", - "6 ArrayCableInstallation NaN NaN NaN \n", - "7 ArrayCableInstallation NaN NaN NaN \n", - "8 ArrayCableInstallation NaN NaN NaN \n", - "9 ArrayCableInstallation 2.0 25.0 11.5 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config = {\n", - " \"array_cable_install_vessel\": \"example_cable_lay_vessel\", # This vessel will now lay the cable but will not bury it.\n", - " \"array_cable_bury_vessel\": \"example_cable_lay_vessel\", # This vessel will now complete the burial process separate from the installation vessel.\n", - "# \"array_cable_trench_vessel\": \"example_cable_lay_vessel\", # <--- Commented out. Will be ignored by the code.\n", - " \n", - " \"site\": {\"distance\": 20, \"depth\": 35},\n", - " \"port\": {},\n", - " \"array_system\": {\n", - " \"system_cost\": 50e6,\n", - " \"cables\": {\n", - " \"ExampleCable\": {\n", - " \"linear_density\": 40, # t/km\n", - " \"cable_sections\": [(2, 25), (1, 25)] # (length, num) pairs. This example: 25 2km cables and 25 1km cables .\n", - " }\n", - " }\n", - " },\n", - " \n", - " \"install_phases\": [\"ArrayCableInstallation\"]\n", - "}\n", - "\n", - "# Run\n", - "project = ProjectManager(config)\n", - "project.run()\n", - "\n", - "# Outputs\n", - "df = pd.DataFrame(project.actions)\n", - "df.iloc[0:10]\n", - "\n", - "# There is an additional vessel mobilization in Row 1 and Row 9 is now just \"Lay Cable\"\n", - "# The burial process now occurs separate from the installation vessel." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Including a Trenching Vessel" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_multiplieragentactiondurationcostleveltimephasephase_namemax_waveheightmax_windspeedtransit_speed
00.5Array Cable Installation VesselMobilize72.00000180000.000000ACTION0.00000ArrayCableInstallationNaNNaNNaNNaN
10.5Array Cable Burial VesselMobilize72.00000180000.000000ACTION0.00000ArrayCableInstallationNaNNaNNaNNaN
20.5Array Cable Trench VesselMobilize72.00000180000.000000ACTION0.00000ArrayCableInstallationNaNNaNNaNNaN
3NaNArray Cable Trench VesselTransit1.739138695.652174ACTION1.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
4NaNArray Cable Trench VesselPosition Onsite2.0000010000.000000ACTION3.73913ArrayCableInstallationNaNNaNNaNNaN
5NaNArray Cable Trench VesselDig Trench19.3000096500.000000ACTION23.03913ArrayCableInstallationArrayCableInstallation2.025.011.5
6NaNArray Cable Trench VesselPosition Onsite2.0000010000.000000ACTION25.03913ArrayCableInstallationNaNNaNNaNNaN
7NaNArray Cable Trench VesselDig Trench19.3000096500.000000ACTION44.33913ArrayCableInstallationArrayCableInstallation2.025.011.5
8NaNArray Cable Trench VesselPosition Onsite2.0000010000.000000ACTION46.33913ArrayCableInstallationNaNNaNNaNNaN
9NaNArray Cable Trench VesselDig Trench19.3000096500.000000ACTION65.63913ArrayCableInstallationArrayCableInstallation2.025.011.5
\n", - "
" - ], - "text/plain": [ - " cost_multiplier agent action \\\n", - "0 0.5 Array Cable Installation Vessel Mobilize \n", - "1 0.5 Array Cable Burial Vessel Mobilize \n", - "2 0.5 Array Cable Trench Vessel Mobilize \n", - "3 NaN Array Cable Trench Vessel Transit \n", - "4 NaN Array Cable Trench Vessel Position Onsite \n", - "5 NaN Array Cable Trench Vessel Dig Trench \n", - "6 NaN Array Cable Trench Vessel Position Onsite \n", - "7 NaN Array Cable Trench Vessel Dig Trench \n", - "8 NaN Array Cable Trench Vessel Position Onsite \n", - "9 NaN Array Cable Trench Vessel Dig Trench \n", - "\n", - " duration cost level time phase \\\n", - "0 72.00000 180000.000000 ACTION 0.00000 ArrayCableInstallation \n", - "1 72.00000 180000.000000 ACTION 0.00000 ArrayCableInstallation \n", - "2 72.00000 180000.000000 ACTION 0.00000 ArrayCableInstallation \n", - "3 1.73913 8695.652174 ACTION 1.73913 ArrayCableInstallation \n", - "4 2.00000 10000.000000 ACTION 3.73913 ArrayCableInstallation \n", - "5 19.30000 96500.000000 ACTION 23.03913 ArrayCableInstallation \n", - "6 2.00000 10000.000000 ACTION 25.03913 ArrayCableInstallation \n", - "7 19.30000 96500.000000 ACTION 44.33913 ArrayCableInstallation \n", - "8 2.00000 10000.000000 ACTION 46.33913 ArrayCableInstallation \n", - "9 19.30000 96500.000000 ACTION 65.63913 ArrayCableInstallation \n", - "\n", - " phase_name max_waveheight max_windspeed transit_speed \n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 ArrayCableInstallation NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "5 ArrayCableInstallation 2.0 25.0 11.5 \n", - "6 NaN NaN NaN NaN \n", - "7 ArrayCableInstallation 2.0 25.0 11.5 \n", - "8 NaN NaN NaN NaN \n", - "9 ArrayCableInstallation 2.0 25.0 11.5 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config = {\n", - " \"array_cable_install_vessel\": \"example_cable_lay_vessel\", # This vessel will lay the cable but will not bury it.\n", - " \"array_cable_bury_vessel\": \"example_cable_lay_vessel\", # This vessel will complete the burial process separate from the installation vessel.\n", - " \"array_cable_trench_vessel\": \"example_cable_lay_vessel\", # This vessel will complete the trenching process prior to the other two vessels beginning their work.\n", - " \n", - " \"site\": {\"distance\": 20, \"depth\": 35},\n", - " \"port\": {},\n", - " \"array_system\": {\n", - " \"system_cost\": 50e6,\n", - " \"cables\": {\n", - " \"ExampleCable\": {\n", - " \"linear_density\": 40, # t/km\n", - " \"cable_sections\": [(2, 25), (1, 25)] # (length, num) pairs. This example: 25 2km cables and 25 1km cables .\n", - " }\n", - " }\n", - " },\n", - " \n", - " \"install_phases\": [\"ArrayCableInstallation\"]\n", - "}\n", - "\n", - "# Run\n", - "project = ProjectManager(config)\n", - "project.run()\n", - "\n", - "# Outputs\n", - "df = pd.DataFrame(project.actions)\n", - "df.iloc[0:10]\n", - "\n", - "# There are now three vessel mobilizations at the beginning of the installation.\n", - "# The first process to be completed is the trenching (\"Dig Trench\"). After this is completed the other vessels will begin their tasks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Example - Cash Flow.ipynb b/examples/Example - Cash Flow.ipynb deleted file mode 100644 index 09b18dd1..00000000 --- a/examples/Example - Cash Flow.ipynb +++ /dev/null @@ -1,540 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ORBIT Example - Cash Flow and NPV\n", - "\n", - "Last Updated: 07/28/2021" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# This notebook provides an example of the cash flow and net present value functionality in ORBIT.\n", - "\n", - "import os\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from ORBIT import ProjectManager, load_config\n", - "\n", - "weather = pd.read_csv(\"data/example_weather.csv\", parse_dates=[\"datetime\"])\\\n", - " .set_index(\"datetime\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Load the Project Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "config = load_config(\"configs/example_fixed_project.yaml\")\n", - "\n", - "# config['install_phases'] = {\n", - "# 'ArrayCableInstallation': 0,\n", - "# 'ExportCableInstallation': 2000,\n", - "# 'MonopileInstallation': ('ScourProtectionInstallation', 0.5),\n", - "# 'OffshoreSubstationInstallation': 0,\n", - "# 'ScourProtectionInstallation': 0,\n", - "# 'TurbineInstallation': ('MonopileInstallation', 0.1)\n", - "# }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this example, a project with the following phases will be configured:\n", - "\n", - "- ProjectDevelopment\n", - "- MonopileDesign\n", - "- ArraySystemDesign\n", - "- ExportSystemDesign\n", - "- OffshoreSubstationDesign\n", - "- ArrayCableInstallation\n", - "- ExportCableInstallation\n", - "- MonopileInstallation\n", - "- OffshoreSubstationInstallation\n", - "- TurbineInstallation\n", - "\n", - "The configuration below represents a \"complete\" project that will be able to produce\n", - "power when the requisite pieces are done being installed. As each array string is able to\n", - "generate power, the project will begin to generate additional revenue and incur\n", - "O&M costs." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ORBIT library intialized at '/Users/jnunemak/Fun/repos/ORBIT/library'\n" - ] - } - ], - "source": [ - "project = ProjectManager(config, weather=weather)\n", - "project.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NPV" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Net Present Value: 426.31 M\n" - ] - } - ], - "source": [ - "# In addition to the other results shown in previous examples, the NPV of the project is available:\n", - "\n", - "print(f\"Net Present Value: {project.npv/1e6:.2f} M\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Project Progress + String Energization Points\n", - "\n", - "The \"progress points\" of the project are tracked in the output below. These are used to determine when array strings and turbines can be energized and revenue begins." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('Offshore Substation', 120.0925357142857),\n", - " ('Array String', 313.9859420289855),\n", - " ('Array String', 618.4459420289855),\n", - " ('Array String', 843.9059420289856),\n", - " ('Array String', 1253.1092753623188),\n", - " ('Substructure', 1496.5764009525235),\n", - " ('Substructure', 1516.3403019050465),\n", - " ('Substructure', 1534.1042028575694),\n", - " ('Substructure', 1551.8681038100924),\n", - " ('Substructure', 1569.6320047626155),\n", - " ('Substructure', 1627.3959057151385),\n", - " ('Substructure', 1675.1598066676615),\n", - " ('Array String', 1688.5692753623189),\n", - " ('Turbine', 1791.015338095949),\n", - " ('Turbine', 1842.1986714292825),\n", - " ('Substructure', 1888.7237076201845),\n", - " ('Turbine', 1893.382004762616),\n", - " ('Substructure', 1906.4876085727078),\n", - " ('Array String', 1918.029275362319),\n", - " ('Substructure', 1924.2515095252309),\n", - " ('Substructure', 1942.015410477754),\n", - " ('Turbine', 1944.5653380959493),\n", - " ('Substructure', 1959.7793114302772),\n", - " ('Substructure', 1977.5432123828004),\n", - " ('Turbine', 1997.7486714292827)]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "project.progress.data[:25]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([2626.8153260869567,\n", - " 2626.8153260869567,\n", - " 3208.7320047626126,\n", - " 3866.13200476261,\n", - " 4333.532004762608,\n", - " 4829.932004762606,\n", - " 5156.032004762603,\n", - " 5620.432004762601,\n", - " 5764.09867142927],\n", - " [6, 6, 6, 6, 6, 6, 6, 6, 2])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "project.progress.energize_points" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Format__: [string installation times], [number of turbines energized]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Monthly Cash Flow\n", - "\n", - "The monthly cash flow is shown below.\n", - "\n", - "- Revenue is generated as each turbine and associated array string is powered and the export system has been installed.\n", - "- A simple generation model is implemented for now (constant NCF), however this is an area for future development.\n", - "- The expenses from each installation phase are collected and totaled per month.\n", - "- As turbines are powered, the project begins accruing additional operating expenses." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ExpensesRevenueCash Flow
01.159244e+070.0-1.159244e+07
19.592865e+060.0-9.592865e+06
27.878374e+070.0-7.878374e+07
31.748031e+071681920.0-1.579839e+07
41.231171e+072522880.0-9.788830e+06
51.000148e+074204800.0-5.796680e+06
68.155250e+065045760.0-3.109490e+06
78.677000e+067008000.0-1.669000e+06
83.750000e+067008000.03.258000e+06
93.750000e+067008000.03.258000e+06
103.750000e+067008000.03.258000e+06
113.750000e+067008000.03.258000e+06
123.750000e+067008000.03.258000e+06
133.750000e+067008000.03.258000e+06
143.750000e+067008000.03.258000e+06
153.750000e+067008000.03.258000e+06
163.750000e+067008000.03.258000e+06
173.750000e+067008000.03.258000e+06
183.750000e+067008000.03.258000e+06
193.750000e+067008000.03.258000e+06
203.750000e+067008000.03.258000e+06
213.750000e+067008000.03.258000e+06
223.750000e+067008000.03.258000e+06
233.750000e+067008000.03.258000e+06
243.750000e+067008000.03.258000e+06
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" - ], - "text/plain": [ - " Expenses Revenue Cash Flow\n", - "0 1.159244e+07 0.0 -1.159244e+07\n", - "1 9.592865e+06 0.0 -9.592865e+06\n", - "2 7.878374e+07 0.0 -7.878374e+07\n", - "3 1.748031e+07 1681920.0 -1.579839e+07\n", - "4 1.231171e+07 2522880.0 -9.788830e+06\n", - "5 1.000148e+07 4204800.0 -5.796680e+06\n", - "6 8.155250e+06 5045760.0 -3.109490e+06\n", - "7 8.677000e+06 7008000.0 -1.669000e+06\n", - "8 3.750000e+06 7008000.0 3.258000e+06\n", - "9 3.750000e+06 7008000.0 3.258000e+06\n", - "10 3.750000e+06 7008000.0 3.258000e+06\n", - "11 3.750000e+06 7008000.0 3.258000e+06\n", - "12 3.750000e+06 7008000.0 3.258000e+06\n", - "13 3.750000e+06 7008000.0 3.258000e+06\n", - "14 3.750000e+06 7008000.0 3.258000e+06\n", - "15 3.750000e+06 7008000.0 3.258000e+06\n", - "16 3.750000e+06 7008000.0 3.258000e+06\n", - "17 3.750000e+06 7008000.0 3.258000e+06\n", - "18 3.750000e+06 7008000.0 3.258000e+06\n", - "19 3.750000e+06 7008000.0 3.258000e+06\n", - "20 3.750000e+06 7008000.0 3.258000e+06\n", - "21 3.750000e+06 7008000.0 3.258000e+06\n", - "22 3.750000e+06 7008000.0 3.258000e+06\n", - "23 3.750000e+06 7008000.0 3.258000e+06\n", - "24 3.750000e+06 7008000.0 3.258000e+06" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(list(zip(\n", - " project.monthly_expenses.values(),\n", - " project.monthly_revenue.values(),\n", - " project.cash_flow.values()\n", - ")), columns=[\"Expenses\", \"Revenue\", \"Cash Flow\"])\n", - "\n", - "df.head(25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cash Flow Figure\n", - "\n", - "Play around with the start dates of the configuration above. As the dates are moved around, the underlying expenses and generation will shift, affecting the net present value of the project." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(6, 4), dpi=200)\n", - "axis = fig.add_subplot(111)\n", - "\n", - "last = 24\n", - "\n", - "df.loc[:last, \"Cash Flow\"].plot(kind='bar', ax=axis)\n", - "\n", - "## Formatting\n", - "_ = axis.axhline(0, color='k', lw=0.5)\n", - "\n", - "# Axis Labels\n", - "axis.set_xlabel(\"Month\")\n", - "\n", - "xticks = []\n", - "for i, tick in enumerate(axis.get_xticklabels()):\n", - " tick.set_rotation(0)\n", - " tick.set_fontsize(8)\n", - " xticks.append(tick)\n", - " \n", - "# xticks = [str(item.get_text()) for item in axis.get_xticklabels()]\n", - "xticks[-2] = \"...\"\n", - "xticks[-1] = str(df.index.max())\n", - "\n", - "_ = axis.set_xticklabels(xticks)\n", - "\n", - "axis.set_ylabel(\"Cash Flow ($M)\")\n", - "axis.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:.0f}M\".format(int(x) / 1e6)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Example - Dependent Phases.ipynb b/examples/Example - Dependent Phases.ipynb deleted file mode 100644 index 8b5ae593..00000000 --- a/examples/Example - Dependent Phases.ipynb +++ /dev/null @@ -1,908 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ORBIT Example - Dependent Phases\n", - "\n", - "Last Updated: 07/28/2020\n", - "\n", - "The start times for phases in ORBIT can be defined relative to other phases. This is often used to simulate an installation phase that is dependent on an earlier installation phase. For example, the turbine installation for fixed bottom substructures can't happen until the substructures are installed. The phases can be scheduled such that the turbine installation starts when 50% of the monopiles have been installed." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from ORBIT import ProjectManager" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Simple Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# For this example we will start with a simple project with only two phases:\n", - "# - MonopileInstallation\n", - "# - TurbineInstallation\n", - "\n", - "# In the config below, the installation phases are defined in a list. They will run\n", - "# in the code sequentially. ie, TurbineInstallation will start at the timestep that\n", - "# MonopileInstallation ends.\n", - "\n", - "config = {\n", - " \"site\": {\n", - " \"depth\": 20,\n", - " \"distance\": 40\n", - " },\n", - " \n", - " \"plant\": {\"num_turbines\": 50},\n", - " \"turbine\": \"SWT_6MW_154m_110m\",\n", - " \"port\": {\"num_cranes\": 1},\n", - " \n", - " \"monopile\": {\n", - " \"unit_cost\": 5e6,\n", - " \"length\": 80,\n", - " \"diameter\": 8,\n", - " \"deck_space\": 1000,\n", - " \"mass\": 1000,\n", - " },\n", - " \n", - " \"transition_piece\": {\n", - " \"unit_cost\": 3e6,\n", - " \"deck_space\": 300,\n", - " \"mass\": 500,\n", - " },\n", - " \n", - " \"MonopileInstallation\": {\"wtiv\": \"example_wtiv\"},\n", - " \"TurbineInstallation\": {\"wtiv\": \"example_wtiv\"},\n", - " \n", - " 'install_phases': ['MonopileInstallation', 'TurbineInstallation']\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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785NaNWTIVFasten Tower Section4.030000.0ACTION2032.00TurbineInstallation20.0110.0TurbineInstallation
786NaNWTIVFasten Tower Section4.030000.0ACTION2036.00TurbineInstallation20.0110.0TurbineInstallation
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790NaNWTIVFasten Blade1.511250.0ACTION2044.50TurbineInstallation20.0110.0TurbineInstallation
791NaNWTIVFasten Tower Section4.030000.0ACTION2048.50TurbineInstallation20.0110.0TurbineInstallation
792NaNWTIVFasten Tower Section4.030000.0ACTION2052.50TurbineInstallation20.0110.0TurbineInstallation
793NaNWTIVFasten Nacelle4.030000.0ACTION2056.50TurbineInstallation20.0110.0TurbineInstallation
794NaNWTIVFasten Blade1.511250.0ACTION2058.00TurbineInstallation20.0110.0TurbineInstallation
795NaNWTIVFasten Blade1.511250.0ACTION2059.50TurbineInstallation20.0110.0TurbineInstallation
796NaNWTIVFasten Blade1.511250.0ACTION2061.00TurbineInstallation20.0110.0TurbineInstallation
797NaNWTIVFasten Tower Section4.030000.0ACTION2065.00TurbineInstallation20.0110.0TurbineInstallation
798NaNWTIVFasten Tower Section4.030000.0ACTION2069.00TurbineInstallation20.0110.0TurbineInstallation
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189NaNWTIVFasten Monopile12.090000.0ACTION498.8360MonopileInstallation20.0110.0MonopileInstallation
1901.0WTIVMobilize168.01260000.0ACTION506.7875TurbineInstallationNaNNaNNaN
191NaNWTIVFasten Transition Piece8.060000.0ACTION506.8360MonopileInstallation20.0110.0MonopileInstallation
192NaNWTIVFasten Tower Section4.030000.0ACTION510.7875TurbineInstallation20.0110.0TurbineInstallation
193NaNWTIVFasten Tower Section4.030000.0ACTION514.7875TurbineInstallation20.0110.0TurbineInstallation
....................................
2245NaNWTIVAttach Blade3.526250.0ACTION3943.3875TurbineInstallation20.0110.0TurbineInstallation
2246NaNWTIVRelease Blade1.07500.0ACTION3944.3875TurbineInstallationNaNNaNNaN
2247NaNWTIVLift Blade1.18250.0ACTION3945.4875TurbineInstallation20.0110.0TurbineInstallation
2248NaNWTIVAttach Blade3.526250.0ACTION3948.9875TurbineInstallation20.0110.0TurbineInstallation
2249NaNWTIVJackdown0.32250.0ACTION3949.2875TurbineInstallation20.0110.0TurbineInstallation
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2061 rows \u00d7 11 columns

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" - ], - "text/plain": [ - " cost_multiplier agent action duration cost \\\n", - "189 NaN WTIV Fasten Monopile 12.0 90000.0 \n", - "190 1.0 WTIV Mobilize 168.0 1260000.0 \n", - "191 NaN WTIV Fasten Transition Piece 8.0 60000.0 \n", - "192 NaN WTIV Fasten Tower Section 4.0 30000.0 \n", - "193 NaN WTIV Fasten Tower Section 4.0 30000.0 \n", - "... ... ... ... ... ... \n", - "2245 NaN WTIV Attach Blade 3.5 26250.0 \n", - "2246 NaN WTIV Release Blade 1.0 7500.0 \n", - "2247 NaN WTIV Lift Blade 1.1 8250.0 \n", - "2248 NaN WTIV Attach Blade 3.5 26250.0 \n", - "2249 NaN WTIV Jackdown 0.3 2250.0 \n", - "\n", - " level time phase site_depth hub_height \\\n", - "189 ACTION 498.8360 MonopileInstallation 20.0 110.0 \n", - "190 ACTION 506.7875 TurbineInstallation NaN NaN \n", - "191 ACTION 506.8360 MonopileInstallation 20.0 110.0 \n", - "192 ACTION 510.7875 TurbineInstallation 20.0 110.0 \n", - "193 ACTION 514.7875 TurbineInstallation 20.0 110.0 \n", - "... ... ... ... ... ... \n", - "2245 ACTION 3943.3875 TurbineInstallation 20.0 110.0 \n", - "2246 ACTION 3944.3875 TurbineInstallation NaN NaN \n", - "2247 ACTION 3945.4875 TurbineInstallation 20.0 110.0 \n", - "2248 ACTION 3948.9875 TurbineInstallation 20.0 110.0 \n", - "2249 ACTION 3949.2875 TurbineInstallation 20.0 110.0 \n", - "\n", - " phase_name \n", - "189 MonopileInstallation \n", - "190 NaN \n", - "191 MonopileInstallation \n", - "192 TurbineInstallation \n", - "193 TurbineInstallation \n", - "... ... \n", - "2245 TurbineInstallation \n", - "2246 NaN \n", - "2247 TurbineInstallation \n", - "2248 TurbineInstallation \n", - "2249 TurbineInstallation \n", - "\n", - "[2061 rows x 11 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "project = ProjectManager(config)\n", - "project.run()\n", - "\n", - "df = pd.DataFrame(project.actions)\n", - "\n", - "monopiles = df.loc[df[\"phase\"]==\"MonopileInstallation\"] # Filter actions table to the MonopileInstallation phase.\n", - "halfway_point = max(monopiles[\"time\"]) / 4 # Find the halway point of the MonopileInstallation phase.\n", - "\n", - "df.loc[df[\"time\"] > halfway_point - 10] # Display the total actions table starting from 10 hours prior to the halfway point.\n", - " # Notice the \"Mobilize\" action for the TurbineInstallation phase. This marks the beginning of\n", - " # the TurbineInstallation phase." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Other Examples\n", - "\n", - "The examples below are not complete configurations but showcase the flexibility of dependent phases." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Multiple dependent phases\n", - "config = {\n", - " \n", - " # ...\n", - " \n", - " 'install_phases': {\n", - " 'MonopileInstallation': 0, # MonopileInstallation will start at timestep 0\n", - " 'ScourProtectionInstallation': (\"MonopileInstallation\", 0.8), # ScourProtectionInstallation will start when MonopileInstallation is 80% complete\n", - " 'TurbineInstallation': (\"MonopileInstallation\", 0.5) # TurbineInstallation will start when MonopileInstallation is 50% complete\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Multiple dependent phases with start dates\n", - "config = {\n", - " \n", - " # ...\n", - " \n", - " 'install_phases': {\n", - " 'MonopileInstallation': \"04/01/2010\", # MonopileInstallation will start on April 1st, 2010\n", - " 'ScourProtectionInstallation': (\"MonopileInstallation\", 0.8), # ScourProtectionInstallation will start when MonopileInstallation is 80% complete\n", - " 'TurbineInstallation': (\"MonopileInstallation\", 0.5) # TurbineInstallation will start when MonopileInstallation is 50% complete\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Chained dependent phases\n", - "config = {\n", - " \n", - " # ...\n", - "\n", - " \"install_phases\": {\n", - " \"ScourProtectionInstallation\": 0, # ScourProtectionInstallation will start at timestep 0\n", - " \"MonopileInstallation\": (\"ScourProtectionInstallation\", 0.1), # MonopileInstallation will start when the above is 10% complete\n", - " \"TurbineInstallation\": (\"MonopileInstallation\", 0.5) # TurbineInstallation will start whent he above is 50% complete\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Multiple chains\n", - "config = {\n", - " \n", - " # ...\n", - "\n", - " \"install_phases\": {\n", - " \"ScourProtectionInstallation\": 0, # ScourProtectionInstallation will start at timestep 0\n", - " \"MonopileInstallation\": (\"ScourProtectionInstallation\", 0.1), # MonopileInstallation will start when the above phase is 10% complete\n", - " \"TurbineInstallation\": (\"MonopileInstallation\", 0.5), # TurbineInstallation will start when the above phase is 50% complete\n", - " \"ArrayCableInstallation\": (\"MonopileInstallation\", 0.25), # ArrayCableInstallation will start when the Monopiles are 25% complete\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Example - Modifying Library Assets.ipynb b/examples/Example - Modifying Library Assets.ipynb deleted file mode 100644 index 91d23c50..00000000 --- a/examples/Example - Modifying Library Assets.ipynb +++ /dev/null @@ -1,216 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example - Library Assets\n", - "\n", - "ORBIT stores the inputs associated with vessels, cables and turbines in a library in order to allow for modeling of discrete options of these inputs as well as to keep the input configurations cleaner. For example, all of the inputs related to the WTIV that we have been using to this point, are stored in the 'example_wtiv.yaml' file in the ORBIT library." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Configuring the library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ORBIT library intialized at '/Users/jnunemak/Fun/repos/ORBIT/examples/library'\n" - ] - } - ], - "source": [ - "# By default, ORBIT initializes the library at '~/ORBIT/library/'\n", - "# The library location can also be changed by using the following:\n", - "\n", - "import os\n", - "from ORBIT.core.library import initialize_library\n", - "initialize_library(os.path.join(os.getcwd(), \"library\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# This allows for proprietary vessel/cable/turbine information to be stored outside of the model repo.\n", - "\n", - "# Note: The library functions will search for library assets at an external library first, but will\n", - "# search the internal ORBIT library if it is not found. This means that the example library files\n", - "# don't need to be copied to an external library to be used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Vessels" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Navigate to '~/ORBIT/library/vessels/example_wtiv.yaml':" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "crane_specs:\n", - " max_hook_height: 100 # m\n", - " max_lift: 1200 # t\n", - " max_windspeed: 15 # m/s\n", - "jacksys_specs:\n", - " leg_length: 110 # m\n", - " max_depth: 75 # m\n", - " max_extension: 85 # m\n", - " speed_above_depth: 1 # m/min\n", - " speed_below_depth: 2.5 # m/min\n", - "storage_specs:\n", - " max_cargo: 8000 # t\n", - " max_deck_load: 15 # t/m^2\n", - " max_deck_space: 4000 # m^2\n", - "transport_specs:\n", - " max_waveheight: 3 # m\n", - " max_windspeed: 20 # m/s\n", - " transit_speed: 10 # km/h\n", - "vessel_specs:\n", - " day_rate: 180000 # USD/day\n", - " mobilization_days: 7 # days\n", - " mobilization_mult: 1 # Mobilization multiplier applied to 'day_rate'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# This vessel file defines a generic WTIV that could be used for installation of turbines, substructures, etc.\n", - "# The vessel file is organized by different subcomponents, eg. crane, jacking system, storage.\n", - "\n", - "# The weather constraints for the vessel are also defined in 'transport_specs' and 'crane_specs'\n", - "# These constraints are applied to underlying processes that the vessel performs in the simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Navigate to '~/ORBIT/library/vessels/example_feeder.yaml':" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "crane_specs:\n", - " max_lift: 500 # t\n", - "jacksys_specs:\n", - " leg_length: 85 # m\n", - " max_depth: 40 # m\n", - " max_extension: 60 # m\n", - " speed_above_depth: 0.5 # m/min\n", - " speed_below_depth: 0.5 # m/min\n", - "storage_specs:\n", - " max_cargo: 1200 # t\n", - " max_deck_load: 8 # t/m^2\n", - " max_deck_space: 1000 # m^2\n", - "transport_specs:\n", - " max_waveheight: 2.5 # m\n", - " max_windspeed: 20 # m/s\n", - " transit_speed: 6 # km/h\n", - "vessel_specs:\n", - " day_rate: 75000 # USD/day\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Turbines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Turbine files contain information on a given turbine and the associated subcomponents.\n", - "# See below for an example 6MW turbine" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "blade:\n", - " deck_space: 100 # m^2\n", - " length: 75 # m\n", - " mass: 100 # t\n", - "hub_height: 110 # m\n", - "nacelle:\n", - " deck_space: 200 # m^2\n", - " mass: 360 # t\n", - "name: SWT-6MW-154\n", - "rotor_diameter: 154 # m\n", - "tower:\n", - " deck_space: 36 # m^2\n", - " sections: 2 # n\n", - " length: 110 # m\n", - " mass: 150 # t\n", - "turbine_rating: 6 # MW\n", - "rated_windspeed: 13 # m/s\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# These inputs will affect which vessels are able to install the turbine as well as the underyling process times.\n", - "# Deck space is a measure of the area that the component would take up on a transportation vessel." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Example - Parametric Manager.ipynb b/examples/Example - Parametric Manager.ipynb deleted file mode 100644 index 6165d711..00000000 --- a/examples/Example - Parametric Manager.ipynb +++ /dev/null @@ -1,511 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ORBIT Example - ParametricManager\n", - "\n", - "ParametricManager provides a similar interface into ORBIT as ProjectManager but allows for some (or all) inputs to be parameterized. This class is useful for quickly exploring how a module or project scales with certain inputs." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from ORBIT.phases.design import MonopileDesign\n", - "\n", - "from ORBIT import ParametricManager\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monopile Design Example\n", - "Perform a parametric sweep of site depth and mean wind speed to see the effects on monopile capex." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'site': {'depth': 'm', 'mean_windspeed': 'm/s'},\n", - " 'plant': {'num_turbines': 'int'},\n", - " 'turbine': {'rotor_diameter': 'm',\n", - " 'hub_height': 'm',\n", - " 'rated_windspeed': 'm/s'},\n", - " 'monopile_design': {'yield_stress': 'Pa (optional)',\n", - " 'load_factor': 'float (optional)',\n", - " 'material_factor': 'float (optional)',\n", - " 'monopile_density': 'kg/m3 (optional)',\n", - " 'monopile_modulus': 'Pa (optional)',\n", - " 'monopile_tp_connection_thickness': 'm (optional)',\n", - " 'transition_piece_density': 'kg/m3 (optional)',\n", - " 'transition_piece_thickness': 'm (optional)',\n", - " 'transition_piece_length': 'm (optional)',\n", - " 'soil_coefficient': 'N/m3 (optional)',\n", - " 'air_density': 'kg/m3 (optional)',\n", - " 'weibull_scale_factor': 'float (optional)',\n", - " 'weibull_shape_factor': 'float (optional)',\n", - " 'turb_length_scale': 'm (optional)',\n", - " 'monopile_steel_cost': 'USD/t (optional)',\n", - " 'tp_steel_cost': 'USD/t (optional)'}}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For this example we will look at the MonopileDesign module.\n", - "MonopileDesign.expected_config" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# The following inputs are the only \"required\" inputs for the MonopileDesign module.\n", - "# Thee 'site' inputs are commented out as they will be defined as parametric inputs below.\n", - "\n", - "base_config = {\n", - "# \"site\": {\n", - "# \"depth\": 20,\n", - "# \"mean_windspeed\": 8\n", - "# }\n", - " \"turbine\": \"12MW_generic\",\n", - " \"plant\": {\n", - " \"num_turbines\": 50\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Parametric inputs:\n", - "\n", - "parameters = {\n", - " \"site.depth\": [20, 40, 60], # The dot-notation allows you to access nested dictionaries\n", - " \"site.mean_windspeed\": [8, 9, 10] # These inputs correspond to the 'depth' and 'mean_windspeed' inputs above, which are nested\n", - "} # in the 'site' dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Desired results:\n", - "# Since there are so many available results in ORBIT, you have to tell ParametricManager which ones\n", - "# you are interested in for this parametric run. The syntax for this always follows the\n", - "# 'lambda run: run.{output}' format. The {output} can be any output available for the configured module\n", - "\n", - "results = {\n", - " \"capex\": lambda run: run.total_cost\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ORBIT library intialized at '/Users/nriccobo/GitHub/ORBIT/library'\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " site.depth site.mean_windspeed monopile_design.soil_coefficient \\\n", - "0 40 10 4000000 \n", - "1 40 10 5000000 \n", - "2 40 9 4500000 \n", - "3 40 8 4500000 \n", - "4 60 9 4000000 \n", - "5 40 9 4000000 \n", - "6 40 8 5000000 \n", - "7 60 10 5000000 \n", - "8 20 9 4500000 \n", - "9 20 10 4500000 \n", - "\n", - " capex \n", - "0 2.779403e+08 \n", - "1 2.744995e+08 \n", - "2 2.599891e+08 \n", - "3 2.442139e+08 \n", - "4 3.373573e+08 \n", - "5 2.616899e+08 \n", - "6 2.428396e+08 \n", - "7 3.541935e+08 \n", - "8 1.915154e+08 \n", - "9 2.036411e+08 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# If you are configuring many parameters it can take a long time to run, especially if weather is turned on.\n", - "# To get an idea of how long it'll take, you can run the .preview() method:\n", - "\n", - "parameters = {\n", - " \"site.depth\": [20, 40, 60],\n", - " \"site.mean_windspeed\": [8, 9, 10],\n", - " \"monopile_design.soil_coefficient\": [4000000, 4500000, 5000000]\n", - "}\n", - "\n", - "parametric = ParametricManager(base_config, parameters, results, module=MonopileDesign, product=True)\n", - "parametric.preview()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Example - Using HVDC or HVAC.ipynb b/examples/Example - Using HVDC or HVAC.ipynb deleted file mode 100644 index 4618d10e..00000000 --- a/examples/Example - Using HVDC or HVAC.ipynb +++ /dev/null @@ -1,359 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f5c53391-7926-4fbe-bbd2-53c4d1bf1dd5", - "metadata": {}, - "source": [ - "### ORBIT Example - Using HVDC or HVAC Example" - ] - }, - { - "cell_type": "markdown", - "id": "e77f95a5-e7bb-4367-8969-c3f179b862a4", - "metadata": {}, - "source": [ - "Component or technology decisions have an impact on a wind project's CapEx. This example will provide a basic setup to compare how different project sizes, export cable types (HVDC or HVDC), distances to shore, etc. effect project costs, this is a very useful tool.\n", - "\n", - "*NOTE: This example uses a test module: `ElectricalDesign` and therefore you must set your branch to dev*" - ] - }, - { - "cell_type": "markdown", - "id": "f472fe73-2ebd-4754-b693-81e2f1d14a77", - "metadata": {}, - "source": [ - "### Import Dependencies" - ] - }, - { - "cell_type": "markdown", - "id": "9ffde11c-6f22-4c15-8b60-3f06e0c2c00e", - "metadata": {}, - "source": [ - "Include path to ORBIT repo and import parametric manager and any modules used for analysis. For this example, we will be running the `ElectricalDesign` module." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "a906c1f3-c0a7-4285-abf5-882d4d5f89e7", - "metadata": {}, - "outputs": [], - "source": [ - "from ORBIT.phases.design import ElectricalDesign\n", - "\n", - "from ORBIT import ProjectManager, ParametricManager\n", - "\n", - "import numpy as np\n", - "from copy import deepcopy\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "id": "b6e56587-72fc-445c-b289-d4d6b19a3079", - "metadata": {}, - "source": [ - "### Create Config" - ] - }, - { - "cell_type": "markdown", - "id": "3792651c-c192-4b1a-ac76-8452dd155b63", - "metadata": {}, - "source": [ - "Config must include all required variables except those you plan to vary. In this example, we will be manually vary the cable type and then use the `ParametricManager` to vary cable type and plant capacity." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "2cc12a2f-6192-40a3-9876-346495655bc9", - "metadata": {}, - "outputs": [], - "source": [ - "base_config = {\n", - " 'export_cable_install_vessel': 'example_cable_lay_vessel',\n", - " 'site': {\n", - " 'distance': 100,\n", - " 'depth': 20,\n", - " 'distance_to_landfall': 50\n", - " },\n", - " 'plant': {\n", - " 'capacity': 1000\n", - " },\n", - " 'turbine': \"12MW_generic\",\n", - " 'oss_install_vessel': 'example_heavy_lift_vessel',\n", - " 'feeder': 'future_feeder',\n", - " 'design_phases': [\n", - " 'ElectricalDesign',\n", - " ],\n", - " 'install_phases': [\n", - " 'ExportCableInstallation',\n", - " 'OffshoreSubstationInstallation'\n", - " ],\n", - "\n", - "# Commented out because we will vary these manually\n", - "# 'export_system_design': {\n", - "# 'cables': 'XLPE_500mm_220kV',\n", - "# }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### HVAC Export Cables\n", - "\n", - "Add HVAC export cables to the config " - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OffshoreSubstationInstallation:\n", - "\t Warning: 'Feeder 0' Cargo Mass Capacity Exceeded\n", - "Total CapEx per kW: $2507.12 \n" - ] - }, - { - "data": { - "text/plain": [ - "{'Export System': 301153600.0,\n", - " 'Offshore Substation': 47655941.13840648,\n", - " 'Export System Installation': 48563809.98096469,\n", - " 'Offshore Substation Installation': 3098929.2998477924,\n", - " 'Turbine': 1310400000,\n", - " 'Soft': 645000000,\n", - " 'Project': 151250000.0}" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvac_config = deepcopy(base_config)\n", - "\n", - "hvac_config[\"export_system_design\"] = {\n", - " 'cables': 'XLPE_1000mm_220kV',\n", - " }\n", - "\n", - "hvac_project = ProjectManager(hvac_config)\n", - "hvac_project.run()\n", - "\n", - "print(f\"Total CapEx per kW: ${hvac_project.total_capex_per_kw:.2f} \")\n", - "\n", - "hvac_project.capex_breakdown" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### HVDC Export Cables\n", - "\n", - "Add HVDC export cables to the config" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OffshoreSubstationInstallation:\n", - "\t Warning: 'Feeder 0' Cargo Mass Capacity Exceeded\n", - "Total CapEx per kW: $2370.48 \n" - ] - }, - { - "data": { - "text/plain": [ - "{'Export System': 87801120.0,\n", - " 'Offshore Substation': 160151200.0,\n", - " 'Export System Installation': 12778131.303180292,\n", - " 'Offshore Substation Installation': 3098929.2998477924,\n", - " 'Turbine': 1310400000,\n", - " 'Soft': 645000000,\n", - " 'Project': 151250000.0}" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvdc_config = deepcopy(base_config)\n", - "\n", - "hvdc_config[\"export_system_design\"] = {\n", - " 'cables': 'HVDC_2000mm_320kV',\n", - " }\n", - "\n", - "hvdc_project = ProjectManager(hvdc_config)\n", - "hvdc_project.run()\n", - "\n", - "print(f\"Total CapEx per kW: ${hvdc_project.total_capex_per_kw:.2f} \")\n", - "\n", - "hvdc_project.capex_breakdown" - ] - }, - { - "cell_type": "markdown", - "id": "47a9f8f7-b725-4857-8277-a38d5470cedd", - "metadata": {}, - "source": [ - "## Use Parametric Manager to compare HVDC and HVAC\n", - "From the two examples above, we see that HVDC is more cost effective than HVAC. Note that the HVDC export system (cables) costs nearly 30% of the HVAC cables. However, the Offshore Substation (OSS) is over 3x the cost of an HVAC OSS. To compare this sensitivity and see if there is an point that these technologies cross we'll use `ParametricManager` and sweep each cable for a range of plant capacities. " - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "5cc31e1a-f39b-44ad-97ce-3ebf96486fa4", - "metadata": {}, - "outputs": [], - "source": [ - "parameters = {\n", - " 'export_system_design.cables': ['XLPE_1000mm_220kV', 'HVDC_2000mm_320kV'],\n", - " 'plant.capacity': np.arange(100,2100,100)\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "7775629d-afd8-4ce2-bfd4-a70c7f5149a4", - "metadata": {}, - "source": [ - "### Define Outputs of Interest" - ] - }, - { - "cell_type": "markdown", - "id": "786d8e8f-a748-4286-87a6-922c47e4f902", - "metadata": {}, - "source": [ - "Here in the results dictionary, define which variables you would like reported in the output data frame. " - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "0d3536a9-0cc9-4351-a0ac-b25e244fcb26", - "metadata": {}, - "outputs": [], - "source": [ - "results = {\n", - " 'cable_cost': lambda run: run.total_cable_cost,\n", - " 'oss_cost': lambda run: run.substation_cost,\n", - " 'num_cables': lambda run: run.num_cables,\n", - " 'num_substations': lambda run: run.num_substations,\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "23688759-8861-42a5-b749-3650d64a6215", - "metadata": {}, - "source": [ - "### Run ParametricManager and See Results" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "9d32906d-d907-4bd6-9714-33f994aa0061", - "metadata": {}, - "outputs": [], - "source": [ - "parametric = ParametricManager(base_config, parameters, results, module = ElectricalDesign, product=True)\n", - "parametric.run()\n", - "parametric.results" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "data": { - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Put results into a Dataframe\n", - "df = pd.DataFrame(parametric.results)\n", - "\n", - "# Plot results\n", - "fig = plt.figure(figsize=(6,4), dpi=200)\n", - "ax = fig.subplots(1)\n", - "\n", - "hvac_df = df[df['export_system_design.cables'] == 'XLPE_1000mm_220kV']\n", - "hvdc_df = df[df['export_system_design.cables'] == 'HVDC_2000mm_320kV']\n", - "\n", - "ax.plot(hvac_df[\"plant.capacity\"],\n", - " (hvac_df[\"cable_cost\"] + hvac_df[\"oss_cost\"])/1e6,\n", - " label='HVAC')\n", - "\n", - "ax.plot(hvdc_df[\"plant.capacity\"],\n", - " (hvdc_df[\"cable_cost\"] + hvdc_df[\"oss_cost\"])/1e6,\n", - " label='HVDC')\n", - "\n", - "ax.set_ylabel(\"CapEx [$M]\")\n", - "ax.set_xlabel(\"Capacity [MW]\")\n", - "ax.legend()\n", - "ax.grid()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This plot shows that for a project that has less than 700MW of capacity you should use HVAC. But for projects greater than 700MW should use HVDC, if only considering the CapEx." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Example - Using Monopile or GBF Intallation.ipynb b/examples/Example - Using Monopile or GBF Intallation.ipynb deleted file mode 100644 index 6d106889..00000000 --- a/examples/Example - Using Monopile or GBF Intallation.ipynb +++ /dev/null @@ -1,1810 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example - Using Monopile or GBF Installation\n", - "\n", - "This tutorial uses pre compiled ORBIT configs that are stored as .yaml files in the '~/configs/ folder. There are load and save methods available in ORBIT for working with .yaml files. \n", - "\n", - "This specific notebook runs the turbine and substructure installation of a project using different intallation methods: \n", - "- Monopile and Turbine Installation (Heavy Lift Vessel for Monopile Installation, WTIV for Turbine Installation)\n", - "- Gravity-Based Foundation Intallation (Substructure-Turbine Assembly Tow-out, no WTIV)\n", - "- Gravity-Based Foundation and Turbine Intallation (Substructure Tow-out, WTIV for Turbine Installation)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: C:\\Users\\dmulash\\AppData\\Local\\Temp\\1\\ipykernel_8540\\3577394900.py:6\n", - "Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format." - ] - } - ], - "source": [ - "import os\n", - "import pandas as pd\n", - "from ORBIT import ProjectManager, load_config\n", - "import copy\n", - "\n", - "weather = pd.read_csv(\"data/example_weather.csv\", parse_dates=[\"datetime\"])\\\n", - " .set_index(\"datetime\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the project configuration for the Gravity-Based Foundation Intallation (Substructure-Turbine Assembly Tow-out, no WTIV)\n", - "This configuration represents a substructure-turbine assembly tow-out using a gravity-based foundation, with no WTIV involved in the installation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "gbf_no_wtiv_config = load_config(\"configs/example_gravity-based_project.yaml\") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project configuration for the Gravity-Based Foundation and Turbine Intallation (Substructure Tow-out, WTIV for Turbine Installation)\n", - "This modified configuration uses a substructure assembly tow-out using a gravity-based foundation and a WTIV for turbine installation, while retaining all other inputs from the original Gravity-Based project setup." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a copy to avoid modifying the original gbf_no_wtiv_config\n", - "gbf_with_wtiv_config = copy.deepcopy(gbf_no_wtiv_config)\n", - "\n", - "# Add 'wtiv' key if not already set\n", - "if \"wtiv\" not in gbf_with_wtiv_config:\n", - " gbf_with_wtiv_config[\"wtiv\"] = \"example_wtiv\"\n", - "\n", - "# Add 'TurbineInstallation' to install_phases if not already present\n", - "if \"TurbineInstallation\" not in gbf_with_wtiv_config[\"install_phases\"]:\n", - " gbf_with_wtiv_config[\"install_phases\"][\"TurbineInstallation\"] = 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project configuration for the Monopile and Turbine Installation (Heavy Lift Vessel for Monopile Installation, WTIV for Turbine Installation)\n", - "This modified configuration uses a Heavy Lift Vessel for monopile installation and a WTIV for turbine installation, while retaining all other inputs from the original Gravity-Based project setup." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a copy to avoid modifying the original gbf_no_wtiv_config\n", - "monopile_config = copy.deepcopy(gbf_no_wtiv_config)\n", - "\n", - "# Add 'wtiv' key if not already set\n", - "if \"wtiv\" not in monopile_config:\n", - " monopile_config[\"wtiv\"] = \"example_wtiv\"\n", - "\n", - "# Add 'MonopileDesign' to design_phases if not already present\n", - "if \"MonopileDesign\" not in monopile_config[\"design_phases\"]:\n", - " monopile_config[\"design_phases\"].append(\"MonopileDesign\")\n", - "\n", - "# Add 'MonopileInstallation' to install_phases if not already present\n", - "if \"MonopileInstallation\" not in monopile_config[\"install_phases\"]:\n", - " monopile_config[\"install_phases\"][\"MonopileInstallation\"] = 0\n", - "\n", - "# Add 'TurbineInstallation' to install_phases if not already present\n", - "if \"TurbineInstallation\" not in monopile_config[\"install_phases\"]:\n", - " monopile_config[\"install_phases\"][\"TurbineInstallation\"] = 0\n", - "\n", - "# Add 'ScourProtectionDesign' to design_phases if not already present\n", - "if \"ScourProtectionDesign\" not in monopile_config[\"design_phases\"]:\n", - " monopile_config[\"design_phases\"].append(\"ScourProtectionDesign\")\n", - "\n", - "# Ensure 'scour_protection_design' exists\n", - "if \"scour_protection_design\" not in monopile_config:\n", - " monopile_config[\"scour_protection_design\"] = {}\n", - "\n", - "# Set 'cost_per_tonne' only if not already defined\n", - "if \"cost_per_tonne\" not in monopile_config[\"scour_protection_design\"]:\n", - " monopile_config[\"scour_protection_design\"][\"cost_per_tonne\"] = 40\n", - "\n", - "# Add 'ScourProtectionInstallation' to install_phases if not already present\n", - "if \"ScourProtectionInstallation\" not in monopile_config[\"install_phases\"]:\n", - " monopile_config[\"install_phases\"][\"ScourProtectionInstallation\"] = 0\n", - "\n", - "# Remove 'GravityBasedInstallation' from install_phases if it exists\n", - "monopile_config[\"install_phases\"].pop(\"GravityBasedInstallation\", None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Each Project Has Different Installation Phases\n", - "\n", - "The 'TurbineInstallation' module is only required for projects involving a WTIV. The 'GravityBasedInstallation' module offers flexibility—if a WTIV is not specified in the configuration file, it models the tow-out of a fully assembled substructure and turbine; if a WTIV is present, it models the tow-out of the substructure alone.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Monopile and Turbine Installation (Heavy Lift Vessel for Monopile Installation, WTIV for Turbine Installation)\n", - "Install phases: ['ArrayCableInstallation', 'ExportCableInstallation', 'OffshoreSubstationInstallation', 'MonopileInstallation', 'TurbineInstallation', 'ScourProtectionInstallation']\n", - "\n", - "Gravity-Based Foundation Intallation (Substructure-Turbine Assembly Tow-out, no WTIV)\n", - "Install phases: ['ArrayCableInstallation', 'ExportCableInstallation', 'GravityBasedInstallation', 'OffshoreSubstationInstallation']\n", - "\n", - "Gravity-Based Foundation and Turbine Intallation (Substructure Tow-out, WTIV for Turbine Installation)\n", - "Install phases: ['ArrayCableInstallation', 'ExportCableInstallation', 'GravityBasedInstallation', 'OffshoreSubstationInstallation', 'TurbineInstallation']\n", - "\n" - ] - } - ], - "source": [ - "print(f\"Monopile and Turbine Installation (Heavy Lift Vessel for Monopile Installation, WTIV for Turbine Installation)\")\n", - "print(f\"Install phases: {list(monopile_config['install_phases'].keys())}\\n\")\n", - "print(f\"Gravity-Based Foundation Intallation (Substructure-Turbine Assembly Tow-out, no WTIV)\")\n", - "print(f\"Install phases: {list(gbf_no_wtiv_config['install_phases'].keys())}\\n\")\n", - "print(f\"Gravity-Based Foundation and Turbine Intallation (Substructure Tow-out, WTIV for Turbine Installation)\")\n", - "print(f\"Install phases: {list(gbf_with_wtiv_config['install_phases'].keys())}\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run Three Cases\n", - "\n", - "This project is always being modeled with the example weather project supplied that is representative of US East Coast wind farm locations." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ORBIT library intialized at 'C:\\esteyco-no-cost-extension\\ORBIT\\library'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DeprecationWarning: C:\\esteyco-no-cost-extension\\ORBIT\\ORBIT\\manager.py:730\n", - "landfall dictionary will be deprecated and moved into [export_system_design][landfall].DeprecationWarning: C:\\esteyco-no-cost-extension\\ORBIT\\ORBIT\\phases\\install\\cable_install\\export.py:84\n", - "landfall dictionary will be deprecated and moved into [export_system][landfall]." - ] - } - ], - "source": [ - "project_monopile = ProjectManager(monopile_config, weather=weather)\n", - "project_monopile.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DeprecationWarning: C:\\esteyco-no-cost-extension\\ORBIT\\ORBIT\\phases\\install\\quayside_assembly_tow\\gravity_base.py:91\n", - "support_vessel will be deprecated and replaced with towing_vessels and ahts_vessel in the towing groups.\n", - "DeprecationWarning: C:\\esteyco-no-cost-extension\\ORBIT\\ORBIT\\phases\\install\\quayside_assembly_tow\\gravity_base.py:91\n", - "['towing_vessl_groups]['station_keeping_vessels'] will be deprecated and replaced with ['towing_vessl_groups]['ahts_vessels'].\n" - ] - } - ], - "source": [ - "project_gbf_no_wtiv = ProjectManager(gbf_no_wtiv_config, weather=weather)\n", - "project_gbf_no_wtiv.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "project_gbf_with_wtiv = ProjectManager(gbf_with_wtiv_config, weather=weather)\n", - "project_gbf_with_wtiv.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CapEx Breakdown" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CapEx ComponentMonopiles + WTIVGBF-Turbine Assembly Tow-outGBF Tow-out + WTIV
0Array System50,455,75750,455,75750,455,757
1Array System Installation58,448,33558,448,33558,448,335
2Export System358,235,289358,235,289358,235,289
3Export System Installation24,502,08224,502,08224,502,082
4Offshore Substation307,307,330307,307,330307,307,330
5Offshore Substation Installation5,095,6005,095,6005,095,600
6Project185,582,083185,582,083185,582,083
7Scour Protection6,618,00000
8Scour Protection Installation14,748,98900
9Soft576,480,426459,880,708510,384,883
10Substructure554,716,392285,000,000285,000,000
11Substructure Installation53,356,40350,175,12632,559,297
12Turbine1,275,000,0001,275,000,0001,275,000,000
13Turbine Installation95,293,403095,293,403
14Total3,565,840,0903,059,682,3113,187,864,060
\n", - "
" - ], - "text/plain": [ - " CapEx Component Monopiles + WTIV \\\n", - "0 Array System 50,455,757 \n", - "1 Array System Installation 58,448,335 \n", - "2 Export System 358,235,289 \n", - "3 Export System Installation 24,502,082 \n", - "4 Offshore Substation 307,307,330 \n", - "5 Offshore Substation Installation 5,095,600 \n", - "6 Project 185,582,083 \n", - "7 Scour Protection 6,618,000 \n", - "8 Scour Protection Installation 14,748,989 \n", - "9 Soft 576,480,426 \n", - "10 Substructure 554,716,392 \n", - "11 Substructure Installation 53,356,403 \n", - "12 Turbine 1,275,000,000 \n", - "13 Turbine Installation 95,293,403 \n", - "14 Total 3,565,840,090 \n", - "\n", - " GBF-Turbine Assembly Tow-out GBF Tow-out + WTIV \n", - "0 50,455,757 50,455,757 \n", - "1 58,448,335 58,448,335 \n", - "2 358,235,289 358,235,289 \n", - "3 24,502,082 24,502,082 \n", - "4 307,307,330 307,307,330 \n", - "5 5,095,600 5,095,600 \n", - "6 185,582,083 185,582,083 \n", - "7 0 0 \n", - "8 0 0 \n", - "9 459,880,708 510,384,883 \n", - "10 285,000,000 285,000,000 \n", - "11 50,175,126 32,559,297 \n", - "12 1,275,000,000 1,275,000,000 \n", - "13 0 95,293,403 \n", - "14 3,059,682,311 3,187,864,060 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The breakdown of project costs by module is available at 'capex_breakdown'\n", - "\n", - "df = pd.DataFrame({\n", - " 'Monopiles + WTIV': pd.Series(project_monopile.capex_breakdown),\n", - " 'GBF-Turbine Assembly Tow-out': project_gbf_no_wtiv.capex_breakdown,\n", - " 'GBF Tow-out + WTIV': pd.Series(project_gbf_with_wtiv.capex_breakdown)\n", - "}).fillna(0)\n", - "\n", - "# Add Total row\n", - "df.loc['Total'] = df.sum()\n", - "\n", - "# Move index to a column\n", - "df = df.reset_index().rename(columns={'index': 'CapEx Component'})\n", - "\n", - "# Create a copy for display with formatting (as strings)\n", - "df_display = df.copy()\n", - "\n", - "# Format numeric columns with commas (leave 'CapEx Component' alone)\n", - "for col in df_display.columns[1:]:\n", - " df_display[col] = df_display[col].apply(lambda x: f\"{x:,.0f}\")\n", - "\n", - "# Display the formatted version (but original df remains numeric)\n", - "df_display" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_capex_comparison(df, num_turbines, project_capacity_mw, top_limit=4000):\n", - " import pandas as pd\n", - " import matplotlib.pyplot as plt\n", - " import matplotlib.ticker as ticker\n", - " import numpy as np\n", - "\n", - " # Clean column names and remove total row\n", - " df.columns = df.columns.str.strip()\n", - " df = df[~df['CapEx Component'].str.lower().str.contains('total')]\n", - "\n", - " # Melt to long format\n", - " df_long = df.melt(id_vars='CapEx Component', var_name='Configuration', value_name='CapEx ($)')\n", - " \n", - " # Clean and convert CapEx values to float\n", - " df_long['CapEx ($)'] = df_long['CapEx ($)'].replace(',', '', regex=True).astype(float)\n", - "\n", - " # Convert to million USD\n", - " df_long['CapEx (Million USD)'] = df_long['CapEx ($)'] / 1e6\n", - "\n", - " # Pivot: Configuration as index, components as columns (Million USD)\n", - " pivot_musd = df_long.pivot(index='Configuration', columns='CapEx Component', values='CapEx (Million USD)').fillna(0)\n", - "\n", - " # Set the order of configurations explicitly\n", - " desired_order = [\"Monopiles + WTIV\", \"GBF-Turbine Assembly Tow-out\", \"GBF Tow-out + WTIV\"]\n", - " pivot_musd = pivot_musd.reindex(desired_order)\n", - "\n", - " capacity_kw = project_capacity_mw * 1000\n", - "\n", - " # Colors for components\n", - " colors = plt.get_cmap('tab20').colors\n", - " component_order = pivot_musd.columns.tolist()\n", - " color_map = {component: colors[i % len(colors)] for i, component in enumerate(component_order)}\n", - "\n", - " # Plot with black edges on bars\n", - " fig, ax = plt.subplots(figsize=(14, 10))\n", - " bottoms = np.zeros(len(pivot_musd))\n", - " bar_width = 0.7 # Slightly thinner bars\n", - "\n", - " for comp in component_order:\n", - " vals = pivot_musd[comp].values\n", - " bars = ax.bar(pivot_musd.index, vals, bottom=bottoms, width=bar_width,\n", - " color=color_map[comp], edgecolor='black', linewidth=0.8, label=comp)\n", - " # Update bottoms for next stack\n", - " bottoms += vals\n", - "\n", - " # Add text inside each stacked segment if $/kW >= 40\n", - " for i, val_musd in enumerate(vals):\n", - " if val_musd == 0:\n", - " continue\n", - " val_usd = val_musd * 1e6\n", - " val_per_kw = val_usd / capacity_kw\n", - " val_per_wtg_musd = val_musd / num_turbines\n", - "\n", - " if val_per_kw >= 40:\n", - " y_pos = bars[i].get_y() + bars[i].get_height() / 2\n", - " text = (\n", - " f\"${val_musd:,.1f}M | \"\n", - " f\"${val_per_kw:,.0f}/kW | \"\n", - " f\"${val_per_wtg_musd:,.2f}M/WTG\"\n", - " )\n", - " ax.text(i, y_pos, text, ha='center', va='center', fontsize=8, color='black')\n", - "\n", - " # Add total values text on top of bars\n", - " total_musd = pivot_musd.sum(axis=1)\n", - " for i, total in enumerate(total_musd):\n", - " total_usd = total * 1e6\n", - " total_per_kw = total_usd / capacity_kw\n", - " total_per_wtg_musd = total / num_turbines\n", - " text = (\n", - " f\"Total:\\n\"\n", - " f\"${total:,.1f}M | \"\n", - " f\"${total_per_kw:,.0f}/kW | \"\n", - " f\"${total_per_wtg_musd:,.2f}M/WTG\"\n", - " )\n", - " ax.text(i, total * 1.005, text, ha='center', va='bottom', fontsize=8, color='black', fontweight='bold')\n", - "\n", - " # Format y-axis ticks with commas\n", - " ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{x:,.0f}'))\n", - "\n", - " plt.ylabel('CapEx ($ Million)', fontweight='bold')\n", - " plt.xlabel('Configuration', fontweight='bold')\n", - " plt.title('CapEx Breakdown by Component')\n", - " plt.ylim(0, top_limit)\n", - " plt.xticks()\n", - " plt.grid(axis='y', linestyle='--', alpha=0.7)\n", - "\n", - " plt.legend(title='CapEx Component', bbox_to_anchor=(1.01, 1), loc='upper left')\n", - " plt.tight_layout()\n", - " #plt.savefig(\"results/capex_comparison.png\", dpi=300)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_capex_comparison(df_display, 50, 750, top_limit=4000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Substructure and Turbine Installation CapEx Breakdown" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CapEx ComponentMonopiles + WTIVGBF-Turbine Assembly Tow-outGBF Tow-out + WTIV
0Substructure Installation53,356,40350,175,12632,559,297
1Turbine Installation95,293,403095,293,403
2Total148,649,80750,175,126127,852,700
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" - ], - "text/plain": [ - " CapEx Component Monopiles + WTIV GBF-Turbine Assembly Tow-out \\\n", - "0 Substructure Installation 53,356,403 50,175,126 \n", - "1 Turbine Installation 95,293,403 0 \n", - "2 Total 148,649,807 50,175,126 \n", - "\n", - " GBF Tow-out + WTIV \n", - "0 32,559,297 \n", - "1 95,293,403 \n", - "2 127,852,700 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Filter only the rows of interest\n", - "subset_df = df[df['CapEx Component'].isin(['Substructure Installation', 'Turbine Installation'])].copy()\n", - "\n", - "# Add Total row (sum of these two rows)\n", - "total_row = {\n", - " 'CapEx Component': 'Total',\n", - "}\n", - "for col in subset_df.columns[1:]:\n", - " total_row[col] = subset_df[col].sum()\n", - "subset_df = pd.concat([subset_df, pd.DataFrame([total_row])], ignore_index=True)\n", - "\n", - "# Optional: format for display (with commas)\n", - "subset_display = subset_df.copy()\n", - "for col in subset_display.columns[1:]:\n", - " subset_display[col] = subset_display[col].apply(lambda x: f\"{x:,.0f}\")\n", - "\n", - "# Show formatted version\n", - "subset_display" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation Actions\n", - "\n", - "Display the installation sequences from each configuration file (just for substructure and turbine), and show total durations by installation step." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_multiplieragentactiondurationcostleveltimephasesite_depthhub_heightphase_nameper_triplocationmax_waveheightmax_windspeedtransit_speed
31.0WTIVMobilize168.0000002.800000e+06ACTION0.000000MonopileInstallationNaNNaNNaNNaNNaNNaNNaNNaN
11NaNWTIVFasten Monopile12.0000002.000000e+05ACTION12.000000MonopileInstallation40.0150.0MonopileInstallation3.0NaNNaNNaNNaN
20NaNWTIVFasten Transition Piece8.0000001.333333e+05ACTION20.000000MonopileInstallation40.0150.0MonopileInstallation3.0NaNNaNNaNNaN
31NaNWTIVFasten Monopile12.0000002.000000e+05ACTION32.000000MonopileInstallation40.0150.0MonopileInstallation3.0NaNNaNNaNNaN
35NaNWTIVFasten Transition Piece8.0000001.333333e+05ACTION40.000000MonopileInstallation40.0150.0MonopileInstallation3.0NaNNaNNaNNaN
...................................................
2285NaNWTIVRelease Transition Piece2.0000003.333333e+04ACTION2843.966890MonopileInstallationNaNNaNNaNNaNNaNNaNNaNNaN
2286NaNWTIVCrane Reequip1.0000001.666667e+04ACTION2844.966890MonopileInstallation40.0150.0MonopileInstallation3.0NaNNaNNaNNaN
2287NaNWTIVLower TP1.0000001.666667e+04ACTION2845.966890MonopileInstallation40.0150.0MonopileInstallation3.0NaNNaNNaNNaN
2290NaNWTIVBolt TP4.0000006.666667e+04ACTION2849.966890MonopileInstallation40.0150.0MonopileInstallation3.0NaNNaNNaNNaN
2292NaNWTIVJackdown0.4333337.222222e+03ACTION2850.400223MonopileInstallation40.0150.0MonopileInstallation3.0NaNNaNNaNNaN
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821 rows × 16 columns

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5NaNSubstructure Assembly Line 1Substructure Assembly0.0000000.000000ACTION0.000000GravityBasedInstallationNaNNaNNaNNaNNaNNaNNaNNaN
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1819NaNMulti-Purpose AHTS VesselPump Ballast12.00000053593.500000ACTION5632.714286GravityBasedInstallationNaNNaNNaNNaNNaNNaNNaNNaN
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2285NaNMulti-Purpose AHTS VesselPump Ballast12.00000053593.500000ACTION3932.571429GravityBasedInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
2289NaNMulti-Purpose AHTS VesselGrout GBF6.00000026796.750000ACTION3938.571429GravityBasedInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
2290NaNTowing Group 1Positioning Support24.00000037516.000000ACTION3938.571429GravityBasedInstallationNaNNaNNaN1.0NaNsiteNaNNaNNaN
2295NaNTowing Group 1Transit8.57142940195.714286ACTION3947.142857GravityBasedInstallationNaNNaNNaN3.00.0NaNNaNNaNNaN
2296NaNMulti-Purpose AHTS VesselTransit8.57142938281.071429ACTION3947.142857GravityBasedInstallationNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" - ], - "text/plain": [ - " cost_multiplier agent \\\n", - "1 NaN Substructure Assembly Line 1 \n", - "2 NaN Substructure Assembly Line 2 \n", - "3 NaN Substructure Assembly Line 1 \n", - "4 NaN Substructure Assembly Line 1 \n", - "5 NaN Substructure Assembly Line 2 \n", - "... ... ... \n", - "2285 NaN Multi-Purpose AHTS Vessel \n", - "2289 NaN Multi-Purpose AHTS Vessel \n", - "2290 NaN Towing Group 1 \n", - "2295 NaN Towing Group 1 \n", - "2296 NaN Multi-Purpose AHTS Vessel \n", - "\n", - " action duration cost \\\n", - "1 Substructure Assembly 0.000000 0.000000 \n", - "2 Substructure Assembly 0.000000 0.000000 \n", - "3 Move GBF from Wet Storage to Assembly Storage 0.000000 0.000000 \n", - "4 Substructure Assembly 0.000000 0.000000 \n", - "5 Move GBF from Wet Storage to Assembly Storage 0.000000 0.000000 \n", - "... ... ... ... \n", - "2285 Pump Ballast 12.000000 53593.500000 \n", - "2289 Grout GBF 6.000000 26796.750000 \n", - "2290 Positioning Support 24.000000 37516.000000 \n", - "2295 Transit 8.571429 40195.714286 \n", - "2296 Transit 8.571429 38281.071429 \n", - "\n", - " level time phase site_depth hub_height \\\n", - "1 ACTION 0.000000 GravityBasedInstallation NaN NaN \n", - "2 ACTION 0.000000 GravityBasedInstallation NaN NaN \n", - "3 ACTION 0.000000 GravityBasedInstallation NaN NaN \n", - "4 ACTION 0.000000 GravityBasedInstallation NaN NaN \n", - "5 ACTION 0.000000 GravityBasedInstallation NaN NaN \n", - "... ... ... ... ... ... \n", - "2285 ACTION 3932.571429 GravityBasedInstallation NaN NaN \n", - "2289 ACTION 3938.571429 GravityBasedInstallation NaN NaN \n", - "2290 ACTION 3938.571429 GravityBasedInstallation NaN NaN \n", - "2295 ACTION 3947.142857 GravityBasedInstallation NaN NaN \n", - "2296 ACTION 3947.142857 GravityBasedInstallation NaN NaN \n", - "\n", - " phase_name num_vessels num_ahts_vessels location max_waveheight \\\n", - "1 NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN \n", - "5 NaN NaN NaN NaN NaN \n", - "... ... ... ... ... ... \n", - "2285 NaN NaN NaN NaN NaN \n", - "2289 NaN NaN NaN NaN NaN \n", - "2290 NaN 1.0 NaN site NaN \n", - "2295 NaN 3.0 0.0 NaN NaN \n", - "2296 NaN NaN NaN NaN NaN \n", - "\n", - " max_windspeed transit_speed \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "... ... ... \n", - "2285 NaN NaN \n", - "2289 NaN NaN \n", - "2290 NaN NaN \n", - "2295 NaN NaN \n", - "2296 NaN NaN \n", - "\n", - "[591 rows x 17 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "action\n", - "Delay 1310.000000\n", - "Delay: No Substructure Storage Available 7248.286715\n", - "Delay: Not enough vessels for gravity foundations 1400.000000\n", - "Grout GBF 300.000000\n", - "Mobilize 72.000000\n", - "Move GBF from Wet Storage to Assembly Storage 0.000000\n", - "Position Substructure 250.000000\n", - "Positioning Support 2518.571429\n", - "Pump Ballast 600.000000\n", - "ROV Survey 50.000000\n", - "Substructure Assembly 0.000000\n", - "Tow Substructure 1000.000000\n", - "Transit 445.714286\n", - "Name: duration, dtype: float64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(project_gbf_with_wtiv.actions) \n", - "project_gbf_with_wtiv_turbine_install = df.loc[df['phase']==\"TurbineInstallation\"]\n", - "project_gbf_with_wtiv_gbf_install = df.loc[df['phase']==\"GravityBasedInstallation\"]\n", - "display(project_gbf_with_wtiv_gbf_install)\n", - "project_gbf_with_wtiv_gbf_install.groupby([\"action\"]).sum()['duration']" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.18" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/available_outputs.ipynb b/examples/available_outputs.ipynb new file mode 100644 index 00000000..9a8d2fac --- /dev/null +++ b/examples/available_outputs.ipynb @@ -0,0 +1,1788 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "72de6548", + "metadata": {}, + "source": [ + "(outputs-tutorial)=\n", + "# Available Outputs\n", + "\n", + "Using `ProjectManager` to run a collection of ORBIT design and installation models representing a\n", + "partial or complete offshore wind project installation enables a variety of project-level metrics\n", + "to be calculated that are not available in individual models. The outputs of each model are also\n", + "made directly available by access to the model itself or in aggregate form for all project-level\n", + "outputs available via the `ProjectManager` API.\n", + "\n", + "## Model Setup\n", + "\n", + "Before diving in, we will import all the packages and functionality we'll need, and run a project\n", + "that can highlight the project's metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1d944125", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ORBIT library intialized at '/Users/rhammond/GitHub_Public/ORBIT/library'\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "from pprint import pprint\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from ORBIT import ProjectManager, load_config\n", + "\n", + "# Ensure the correct examples directory is used when running this in docs or in examples\n", + "here = Path(\".\").resolve()\n", + "example_dir = here.parents[1] / \"examples\" if here.stem == \"tutorials\" else here\n", + "\n", + "config = load_config(example_dir / \"configs/example_fixed_project.yaml\")\n", + "project = ProjectManager(config)\n", + "project.run()" + ] + }, + { + "cell_type": "markdown", + "id": "d1148e2a", + "metadata": {}, + "source": [ + "## Project Details\n", + "\n", + "### Model Design Results\n", + "\n", + "The `design_results` object is dictionary mapping between phase names and the dictionary of outputs\n", + "used by `ProjectManger` to pass into other phases or calculate further metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fdd629ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'array_system': {'cables': {'XLPE_400mm_33kV': {'cable_sections': [(np.float64(18.167599841100003),\n", + " 2),\n", + " (np.float64(1.5499999999999998),\n", + " 26),\n", + " (np.float64(16.668268330900002),\n", + " 2),\n", + " (np.float64(15.1700628098),\n", + " 2),\n", + " (np.float64(13.6733546329),\n", + " 2),\n", + " (np.float64(12.1786979112),\n", + " 2),\n", + " (np.float64(10.6869570569),\n", + " 2),\n", + " (np.float64(9.1995576081),\n", + " 2),\n", + " (np.float64(7.719024368),\n", + " 2),\n", + " (np.float64(6.2502739666),\n", + " 2),\n", + " (np.float64(4.8042278786),\n", + " 2),\n", + " (np.float64(3.4102823061),\n", + " 2),\n", + " (np.float64(2.1733914114),\n", + " 2)],\n", + " 'linear_density': 35}},\n", + " 'system_cost': np.float64(102201973.42800242)},\n", + " 'export_system': {'cable': {'cable_power': np.float64(118.25312628604732),\n", + " 'linear_density': 50,\n", + " 'number': 6,\n", + " 'sections': [38.0225]},\n", + " 'landfall': {'interconnection_distance': 3},\n", + " 'system_cost': np.float64(120566381.745)},\n", + " 'monopile': {'deck_space': np.float64(52.99375729119361),\n", + " 'diameter': np.float64(7.279681125653349),\n", + " 'embedment_length': np.float64(28.427684886165633),\n", + " 'length': np.float64(60.927684886165636),\n", + " 'mass': np.float64(945.1252825808575),\n", + " 'moment': np.float64(11.603459241851628),\n", + " 'thickness': np.float64(0.07914681125653349),\n", + " 'unit_cost': np.float64(3436475.5274639977)},\n", + " 'num_substations': 1,\n", + " 'offshore_substation_substructure': {'deck_space': 1,\n", + " 'length': 32.5,\n", + " 'mass': np.float64(1588.3736050922225),\n", + " 'type': 'Monopile',\n", + " 'unit_cost': np.float64(4453431.000000001)},\n", + " 'offshore_substation_topside': {'deck_space': 1,\n", + " 'mass': np.float64(2941.5),\n", + " 'unit_cost': np.float64(284644466.3)},\n", + " 'scour_protection': {'cost_per_tonne': 40, 'tonnes_per_substructure': 2520},\n", + " 'transition_piece': {'deck_space': np.float64(55.32346835436127),\n", + " 'diameter': np.float64(7.4379747481664165),\n", + " 'length': 25,\n", + " 'mass': np.float64(396.3315423930208),\n", + " 'thickness': np.float64(0.07914681125653349),\n", + " 'unit_cost': np.float64(3933986.8897931245)}}\n" + ] + } + ], + "source": [ + "pprint(project.design_results)" + ] + }, + { + "cell_type": "markdown", + "id": "ebcd2f59", + "metadata": {}, + "source": [ + "### Project Parameterizaions\n", + "\n", + "Below is brief example showing the basic project parameterizations that are availabe.\n", + "\n", + "- `num_turbines`: the number of turbines.\n", + "- `turbine_rating`: the rating of an individual turbine, in MW.\n", + "- `capacity`: The total project capacity, in MW.\n", + "- `project_time`: The total project installation time, including all delays, in hours." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f6f31b27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of turbines: 50\n", + "Turbine Rating: 12.00\n", + "Project Capacity (MW): 600.00\n", + "Project Installation Time (days): 244.4\n" + ] + } + ], + "source": [ + "print(f\"Number of turbines: {project.num_turbines}\")\n", + "print(f\"Turbine Rating: {project.turbine_rating:.2f}\")\n", + "print(f\"Project Capacity (MW): {project.capacity:,.2f}\")\n", + "print(f\"Project Installation Time (days): {project.project_time / 24:,.1f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3529a6e0", + "metadata": {}, + "source": [ + "### Event Timing\n", + "\n", + "The `installation_time` provides the sum total installation time of all phases, in hours, without\n", + "accounting for timing overlaps, whereas the `project_days` provides the total number of days between\n", + "the start and completion of the project." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ffb35771", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Installation Time: 617 days\n", + "Total Elapsed Time: 245 days\n" + ] + } + ], + "source": [ + "print(f\"Total Installation Time: {project.installation_time / 24:.0f} days\")\n", + "print(f\"Total Elapsed Time: {project.project_days} days\")" + ] + }, + { + "cell_type": "markdown", + "id": "46e7a615", + "metadata": {}, + "source": [ + "## All Outputs At Once\n", + "\n", + "The `outputs` method provides a dictionary mapping all the major project costs and timing details\n", + "in a single view. There are two parameters that can be passed to provide further details that will\n", + "not be demonstrated. For further details on any of these metrics, please refer to that metric's\n", + "section.\n", + "\n", + "- `include_logs`: include the full project installation action logs if `True`.\n", + "- `npv_details`: include the `cash_flow`, `monthly_revenue`, and `monthly_expenses`, if `True`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "744d62c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'bos_capex': np.float64(1132540951.6863625),\n", + " 'bos_capex_per_kw': np.float64(1887.5682528106042),\n", + " 'capex_breakdown': {'Array System': np.float64(102201973.42800242),\n", + " 'Array System Installation': np.float64(79379421.20414363),\n", + " 'Export System': np.float64(120566381.745),\n", + " 'Export System Installation': np.float64(21241335.52231314),\n", + " 'Offshore Substation': np.float64(289097897.3),\n", + " 'Offshore Substation Installation': np.float64(4246272.594230157),\n", + " 'Onshore Substation': 0,\n", + " 'Project': 355000000,\n", + " 'Scour Protection': 5040000,\n", + " 'Scour Protection Installation': np.float64(12590534.090916954),\n", + " 'Soft': np.float64(582210063.6211),\n", + " 'Substructure': np.float64(368523120.86285615),\n", + " 'Substructure Installation': np.float64(45447221.26925003),\n", + " 'Turbine': 900000000,\n", + " 'Turbine Installation': np.float64(84206793.66964997)},\n", + " 'capex_breakdown_per_kw': {'Array System': np.float64(170.33662238000403),\n", + " 'Array System Installation': np.float64(132.29903534023939),\n", + " 'Export System': np.float64(200.943969575),\n", + " 'Export System Installation': np.float64(35.4022258705219),\n", + " 'Offshore Substation': np.float64(481.82982883333335),\n", + " 'Offshore Substation Installation': np.float64(7.077120990383595),\n", + " 'Onshore Substation': 0.0,\n", + " 'Project': 591.6666666666666,\n", + " 'Scour Protection': 8.4,\n", + " 'Scour Protection Installation': np.float64(20.98422348486159),\n", + " 'Soft': np.float64(970.3501060351666),\n", + " 'Substructure': np.float64(614.2052014380936),\n", + " 'Substructure Installation': np.float64(75.74536878208337),\n", + " 'Turbine': 1500.0,\n", + " 'Turbine Installation': np.float64(140.34465611608329)},\n", + " 'capex_detailed_soft_capex_breakdown': {'Array System': np.float64(102201973.42800242),\n", + " 'Array System Installation': np.float64(79379421.20414363),\n", + " 'Commissioning': np.float64(27456720.944393165),\n", + " 'Construction Financing': np.float64(247580745.80896264),\n", + " 'Construction Insurance': np.float64(49422097.6999077),\n", + " 'Decommissioning': np.float64(49422315.67010078),\n", + " 'Export System': np.float64(120566381.745),\n", + " 'Export System Installation': np.float64(21241335.52231314),\n", + " 'Installation Contingency': np.float64(85253494.53092383),\n", + " 'Offshore Substation': np.float64(289097897.3),\n", + " 'Offshore Substation Installation': np.float64(4246272.594230157),\n", + " 'Onshore Substation': 0,\n", + " 'Procurement Contingency': np.float64(123074688.96681187),\n", + " 'Project': 355000000,\n", + " 'Scour Protection': 5040000,\n", + " 'Scour Protection Installation': np.float64(12590534.090916954),\n", + " 'Substructure': np.float64(368523120.86285615),\n", + " 'Substructure Installation': np.float64(45447221.26925003),\n", + " 'Turbine': 900000000,\n", + " 'Turbine Installation': np.float64(84206793.66964997)},\n", + " 'capex_detailed_soft_capex_breakdown_per_kw': {'Array System': np.float64(170.33662238000403),\n", + " 'Array System Installation': np.float64(132.29903534023939),\n", + " 'Commissioning': np.float64(45.76120157398861),\n", + " 'Construction Financing': np.float64(412.6345763482711),\n", + " 'Construction Insurance': np.float64(82.37016283317949),\n", + " 'Decommissioning': np.float64(82.37052611683463),\n", + " 'Export System': np.float64(200.943969575),\n", + " 'Export System Installation': np.float64(35.4022258705219),\n", + " 'Installation Contingency': np.float64(142.08915755153973),\n", + " 'Offshore Substation': np.float64(481.82982883333335),\n", + " 'Offshore Substation Installation': np.float64(7.077120990383595),\n", + " 'Onshore Substation': 0.0,\n", + " 'Procurement Contingency': np.float64(205.1244816113531),\n", + " 'Project': 591.6666666666666,\n", + " 'Scour Protection': 8.4,\n", + " 'Scour Protection Installation': np.float64(20.98422348486159),\n", + " 'Substructure': np.float64(614.2052014380936),\n", + " 'Substructure Installation': np.float64(75.74536878208337),\n", + " 'Turbine': 1500.0,\n", + " 'Turbine Installation': np.float64(140.34465611608329)},\n", + " 'installation_capex': np.float64(247111578.35050386),\n", + " 'installation_capex_per_kw': np.float64(411.8526305841731),\n", + " 'installation_time': np.float64(14807.249087613796),\n", + " 'npv': np.float64(1493975142.6594274),\n", + " 'onshore_substation_capex': 0,\n", + " 'onshore_substation_capex_kw': None,\n", + " 'overnight_capex': np.float64(1785429373.3358586),\n", + " 'overnight_capex_per_kw': np.float64(2975.715622226431),\n", + " 'project_capex': 355000000,\n", + " 'project_capex_per_kw': 591.6666666666666,\n", + " 'project_time': np.float64(5865.619557282503),\n", + " 'soft_capex': np.float64(582210063.6211),\n", + " 'soft_capex_breakdown': {'Commissioning': np.float64(27456720.944393165),\n", + " 'Construction Financing': np.float64(247580745.80896264),\n", + " 'Construction Insurance': np.float64(49422097.6999077),\n", + " 'Decommissioning': np.float64(49422315.67010078),\n", + " 'Installation Contingency': np.float64(85253494.53092383),\n", + " 'Procurement Contingency': np.float64(123074688.96681187)},\n", + " 'soft_capex_breakdown_per_kw': {'Commissioning': np.float64(45.76120157398861),\n", + " 'Construction Financing': np.float64(412.6345763482711),\n", + " 'Construction Insurance': np.float64(82.37016283317949),\n", + " 'Decommissioning': np.float64(82.37052611683463),\n", + " 'Installation Contingency': np.float64(142.08915755153973),\n", + " 'Procurement Contingency': np.float64(205.1244816113531)},\n", + " 'soft_capex_per_kw': np.float64(970.3501060351666),\n", + " 'supply_chain_capex': 0,\n", + " 'supply_chain_capex_kw': 0,\n", + " 'system_capex': np.float64(885429373.3358586),\n", + " 'system_capex_per_kw': np.float64(1475.715622226431),\n", + " 'total_capex': np.float64(2969751015.3074627),\n", + " 'total_capex_per_kw': np.float64(4949.585025512438),\n", + " 'turbine_capex': 900000000,\n", + " 'turbine_capex_per_kw': 1500}\n" + ] + } + ], + "source": [ + "pprint(project.outputs())" + ] + }, + { + "cell_type": "markdown", + "id": "da04549b", + "metadata": {}, + "source": [ + "## CapEx\n", + "\n", + "This section will start from the total CapEx, and work backwards demonstrating how to access\n", + "the various CapEx breakouts and breakdowns.\n", + "\n", + "### Total CapEx\n", + "\n", + "The `total_capex` is the sum of the BOS, soft, and project CapEx numbers (details in following\n", + "sections). This represents the complete project costs including all upfront costs, financing,\n", + "procurement and installation of BOS subsystems and the procurement costs of the turbines.\n", + "\n", + ":::{note}\n", + "ORBIT doesn't explicity model the procurement of turbines, however the Turbine CapEx is included\n", + "within `project.total_capex`. To configure the cost of the turbines, `turbine_capex` can be passed\n", + "into the `project_parameters` section of an ORBIT configuration.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fb740ed4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total CapEx (millions, USD): 2,969.75\n", + "Total CapEx (USD) per kW: 4,949.59\n" + ] + } + ], + "source": [ + "print(f\"Total CapEx (millions, USD): {project.total_capex / 1e6:,.2f}\")\n", + "print(f\"Total CapEx (USD) per kW: {project.total_capex_per_kw:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d6c62f3d", + "metadata": {}, + "source": [ + "### Categorical CapEx Breakdowns\n", + "\n", + "The `capex_breakdown` property provides a dictionary of all the procurement, installation, soft,\n", + "and project costs associated with a project. Below we will print out the dictionary keys and\n", + "the values in millions USD." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "46cb02f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Array System: $102.20 (millions, USD)\n", + " Export System: $120.57 (millions, USD)\n", + " Substructure: $368.52 (millions, USD)\n", + " Offshore Substation: $289.10 (millions, USD)\n", + " Scour Protection: $ 5.04 (millions, USD)\n", + " Array System Installation: $ 79.38 (millions, USD)\n", + " Export System Installation: $ 21.24 (millions, USD)\n", + " Substructure Installation: $ 45.45 (millions, USD)\n", + " Offshore Substation Installation: $ 4.25 (millions, USD)\n", + " Scour Protection Installation: $ 12.59 (millions, USD)\n", + " Turbine Installation: $ 84.21 (millions, USD)\n", + " Onshore Substation: $ 0.00 (millions, USD)\n", + " Turbine: $900.00 (millions, USD)\n", + " Soft: $582.21 (millions, USD)\n", + " Project: $355.00 (millions, USD)\n" + ] + } + ], + "source": [ + "for name, capex in project.capex_breakdown.items():\n", + " print(f\"{name:>35}: ${capex / 1e6:6,.2f} (millions, USD)\")" + ] + }, + { + "cell_type": "markdown", + "id": "29d5439f", + "metadata": {}, + "source": [ + "Like in the previous examples, the `capex_breakdown_per_kw` will provide each category's associated\n", + "costs as a capacity normalized value." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cbc2f84b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Array System: $ 170.34 (USD/kW)\n", + " Export System: $ 200.94 (USD/kW)\n", + " Substructure: $ 614.21 (USD/kW)\n", + " Offshore Substation: $ 481.83 (USD/kW)\n", + " Scour Protection: $ 8.40 (USD/kW)\n", + " Array System Installation: $ 132.30 (USD/kW)\n", + " Export System Installation: $ 35.40 (USD/kW)\n", + " Substructure Installation: $ 75.75 (USD/kW)\n", + " Offshore Substation Installation: $ 7.08 (USD/kW)\n", + " Scour Protection Installation: $ 20.98 (USD/kW)\n", + " Turbine Installation: $ 140.34 (USD/kW)\n", + " Onshore Substation: $ 0.00 (USD/kW)\n", + " Turbine: $1,500.00 (USD/kW)\n", + " Soft: $ 970.35 (USD/kW)\n", + " Project: $ 591.67 (USD/kW)\n" + ] + } + ], + "source": [ + "for name, capex in project.capex_breakdown_per_kw.items():\n", + " print(f\"{name:>35}: ${capex:8,.2f} (USD/kW)\")" + ] + }, + { + "cell_type": "markdown", + "id": "5aac8851", + "metadata": {}, + "source": [ + "### BOS CapEx\n", + "\n", + "The balance-of-system (BOS) CapEx (`bos_capex`) is the sum of the system and installation CapEx,\n", + "and is one of the core outputs of the ORBIT module." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b89fbb52", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BOS CapEx (millions, USD): 1,132.54\n", + "BOS CapEx (USD) per kW: 1,887.57\n" + ] + } + ], + "source": [ + "print(f\"BOS CapEx (millions, USD): {project.bos_capex / 1e6:,.2f}\")\n", + "print(f\"BOS CapEx (USD) per kW: {project.bos_capex_per_kw:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e59284f9", + "metadata": {}, + "source": [ + "### System CapEx\n", + "\n", + "The `system_capex` property provides the total procurement costs for all modeled systems, whether\n", + "the costs were user inputs, or the results of design models. This value will not change unless\n", + "the design or plant's properties (e.g., distance to shore, depth, or number of turbines) change.\n", + "\n", + "In addition, `system_capex_per_kw` provies the capacity-normalized CapEx for the project." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "79de4eed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "System (procurement) CapEx (millions, USD): 885.43\n", + "System (procurement) CapEx (USD) per kW: 1,475.72\n" + ] + } + ], + "source": [ + "print(f\"System (procurement) CapEx (millions, USD): {project.system_capex / 1e6:,.2f}\")\n", + "print(f\"System (procurement) CapEx (USD) per kW: {project.system_capex_per_kw:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "fa329bc4", + "metadata": {}, + "source": [ + "To view the individual component system costs, users can inspect the `system_costs` dictionary where\n", + "costs are summarized by each modeled or input system." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5a636796", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ArrayCableInstallation: $102.20 (millions, USD)\n", + " ExportCableInstallation: $120.57 (millions, USD)\n", + " MonopileInstallation: $368.52 (millions, USD)\n", + " OffshoreSubstationInstallation: $289.10 (millions, USD)\n", + " ScourProtectionInstallation: $ 5.04 (millions, USD)\n" + ] + } + ], + "source": [ + "for name, capex in project.system_costs.items():\n", + " print(f\"{name:>35}: ${capex / 1e6:6,.2f} (millions, USD)\")" + ] + }, + { + "cell_type": "markdown", + "id": "d86ce7b9", + "metadata": {}, + "source": [ + "### Installation Capex\n", + "\n", + "Installation CapEx is a dynamic result based on the installation simulation and the timing\n", + "associated with each subsystem installation, day rates of any vessels/ports and any accrued weather\n", + "delays.\n", + "\n", + "In addition, `installation_capex_per_kw` provies the capacity-normalized CapEx for the project.\n", + "Below we will print out the dictionary keys and the values in millions USD." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3fd1d588", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Installation CapEx (millions, USD): 247.11\n", + "Installation CapEx (USD) per kW: 411.85\n" + ] + } + ], + "source": [ + "print(f\"Installation CapEx (millions, USD): {project.installation_capex / 1e6:,.2f}\")\n", + "print(f\"Installation CapEx (USD) per kW: {project.installation_capex_per_kw:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "f116b847", + "metadata": {}, + "source": [ + "To view the individual component installation costs, users can inspect the `installation_costs`\n", + "dictionary where costs are summarized by each modeled system. Below we will print out the dictionary\n", + "keys and the values in millions USD." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "17a27e24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ArrayCableInstallation: $ 79.38 (millions, USD)\n", + " ExportCableInstallation: $ 21.24 (millions, USD)\n", + " MonopileInstallation: $ 45.45 (millions, USD)\n", + " OffshoreSubstationInstallation: $ 4.25 (millions, USD)\n", + " ScourProtectionInstallation: $ 12.59 (millions, USD)\n", + " TurbineInstallation: $ 84.21 (millions, USD)\n" + ] + } + ], + "source": [ + "for name, capex in project.installation_costs.items():\n", + " print(f\"{name:>35}: ${capex / 1e6:6,.2f} (millions, USD)\")" + ] + }, + { + "cell_type": "markdown", + "id": "2cd9fa46", + "metadata": {}, + "source": [ + "### Turbine CapEx\n", + "\n", + "The `turbine_capex` is directly derived from the user inputs, and if none are provided, it is\n", + "assumed to be $1,300 USD/kW." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "75bf8fbd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Turbine CapEx (millions, USD): 900.00\n", + "Turbine CapEx (USD) per kW: 1,500.00\n" + ] + } + ], + "source": [ + "print(f\"Turbine CapEx (millions, USD): {project.turbine_capex / 1e6:,.2f}\")\n", + "print(f\"Turbine CapEx (USD) per kW: {project.turbine_capex_per_kw:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "011184eb", + "metadata": {}, + "source": [ + "### Project CapEx\n", + "\n", + "Project CapEx (`project.project_capex`) includes the costs associated with\n", + "the lease area, the development of the construction operations plan and any\n", + "environmental review and other upfront project costs. There are default values\n", + "for all of these subcategories, however the values can also be overridden in the\n", + "`project_parameters` subdict." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "57b617a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Turbine CapEx (millions, USD): 355.00\n", + "Turbine CapEx (USD) per kW: 591.67\n" + ] + } + ], + "source": [ + "print(f\"Turbine CapEx (millions, USD): {project.project_capex / 1e6:,.2f}\")\n", + "print(f\"Turbine CapEx (USD) per kW: {project.project_capex_per_kw:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "95257081", + "metadata": {}, + "source": [ + "### Soft CapEx\n", + "\n", + "Soft CapEx (`project.soft_capex`) represents additional project level costs\n", + "associated with commissioning, decommissioning and financing of the project.\n", + "The cost factors can be input in the `project_parameters` subdict of an ORBIT\n", + "configuration. The default cost factors for these categories are derived from the\n", + "[2018 Cost of Wind Energy Review](https://www.nlr.gov/docs/fy20osti/74598.pdf)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "79267541", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Soft CapEx (millions, USD): 582.21\n", + "Soft CapEx (USD) per kW: 970.35\n" + ] + } + ], + "source": [ + "print(f\"Soft CapEx (millions, USD): {project.soft_capex / 1e6:,.2f}\")\n", + "print(f\"Soft CapEx (USD) per kW: {project.soft_capex_per_kw:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "076e07d0", + "metadata": {}, + "source": [ + "The soft CapEx can also be broken down using both the `soft_capex_breakdown` and the `capex_detailed_soft_capex_breakdown`, which also provide a capacity-noramlized variation by adding\n", + "`_per_kw` to the end of either (not shown in this demonstration). The primary difference (as shown\n", + "below) is that the `capex_detailed_soft_capex_breakdown` metric provides the capex breakdown with\n", + "the additional soft capex breakdown." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d83813da", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Construction Insurance: $ 49.42 (millions,USD)\n", + " Decommissioning: $ 49.42 (millions,USD)\n", + " Commissioning: $ 27.46 (millions,USD)\n", + " Procurement Contingency: $ 123.07 (millions,USD)\n", + " Installation Contingency: $ 85.25 (millions,USD)\n", + " Construction Financing: $ 247.58 (millions,USD)\n" + ] + } + ], + "source": [ + "for name, capex in project.soft_capex_breakdown.items():\n", + " print(f\"{name:>35}: ${capex / 1e6:8,.2f} (millions,USD)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "8af1b169", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Array System: $ 102.20 (millions,USD)\n", + " Export System: $ 120.57 (millions,USD)\n", + " Substructure: $ 368.52 (millions,USD)\n", + " Offshore Substation: $ 289.10 (millions,USD)\n", + " Scour Protection: $ 5.04 (millions,USD)\n", + " Array System Installation: $ 79.38 (millions,USD)\n", + " Export System Installation: $ 21.24 (millions,USD)\n", + " Substructure Installation: $ 45.45 (millions,USD)\n", + " Offshore Substation Installation: $ 4.25 (millions,USD)\n", + " Scour Protection Installation: $ 12.59 (millions,USD)\n", + " Turbine Installation: $ 84.21 (millions,USD)\n", + " Onshore Substation: $ 0.00 (millions,USD)\n", + " Turbine: $ 900.00 (millions,USD)\n", + " Project: $ 355.00 (millions,USD)\n", + " Construction Insurance: $ 49.42 (millions,USD)\n", + " Decommissioning: $ 49.42 (millions,USD)\n", + " Commissioning: $ 27.46 (millions,USD)\n", + " Procurement Contingency: $ 123.07 (millions,USD)\n", + " Installation Contingency: $ 85.25 (millions,USD)\n", + " Construction Financing: $ 247.58 (millions,USD)\n" + ] + } + ], + "source": [ + "for name, capex in project.capex_detailed_soft_capex_breakdown.items():\n", + " print(f\"{name:>35}: ${capex / 1e6:8,.2f} (millions,USD)\")" + ] + }, + { + "cell_type": "markdown", + "id": "d302cb00", + "metadata": {}, + "source": [ + "The soft CapEx values are also available as independent values:\n", + "\n", + "- `construction_insurance_capex`\n", + "- `commissioning_capex`\n", + "- `decommissioning_capex`\n", + "- `procurement_contingency_capex`\n", + "- `installation_contingency_capex`\n", + "- `construction_financing_capex`" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e2f890cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Construction Insurance CapEx (millions, USD): 49.42\n", + "Commissioning CapEx (millions, USD): 27.46\n", + "Decommissioning CapEx (millions, USD): 49.42\n", + "Procurement Contingency CapEx (millions, USD): 123.07\n", + "Installation Contingency CapEx (millions, USD): 85.25\n", + "Construction Financing CapEx (millions, USD): 247.58\n" + ] + } + ], + "source": [ + "print(f\"Construction Insurance CapEx (millions, USD): {project.construction_insurance_capex() / 1e6:,.2f}\")\n", + "print(f\"Commissioning CapEx (millions, USD): {project.commissioning_capex() / 1e6:,.2f}\")\n", + "print(f\"Decommissioning CapEx (millions, USD): {project.decommissioning_capex() / 1e6:,.2f}\")\n", + "print(f\"Procurement Contingency CapEx (millions, USD): {project.procurement_contingency_capex() / 1e6:,.2f}\")\n", + "print(f\"Installation Contingency CapEx (millions, USD): {project.installation_contingency_capex() / 1e6:,.2f}\")\n", + "print(f\"Construction Financing CapEx (millions, USD): {project.construction_financing_capex() / 1e6:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5ec81560", + "metadata": {}, + "source": [ + "### All Other CapEx Categories\n", + "\n", + "#### Supply Chain CapEx\n", + "\n", + "The supply chain CapEx (`supply_chain_capex`) directly captures the user-provided\n", + "`supply_chain_capex` from the `project_parameters` section of the project configuration. This\n", + "value should encompass any project-level investements in supply chain development, port upgrade,\n", + "community benefit agreements, fisheries mitigation funds, community or research initiatives, and\n", + "US-built vessels." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a5b84c69", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Supply Chain CapEx (millions, USD): 0.00\n", + "Supply Chain CapEx (USD) per kW: 0.00\n" + ] + } + ], + "source": [ + "print(f\"Supply Chain CapEx (millions, USD): {project.supply_chain_capex / 1e6:,.2f}\")\n", + "print(f\"Supply Chain CapEx (USD) per kW: {project.supply_chain_capex_per_kw:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "f7ea395a", + "metadata": {}, + "source": [ + "#### Onshore Substation CapEx\n", + "\n", + "The CapEx associated with onshore substation as prescribed by the `ElectricalDesign`" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "49b20214", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Turbine CapEx (millions, USD): 900.00\n", + "Turbine CapEx (USD) per kW: 1,500.00\n" + ] + } + ], + "source": [ + "print(f\"Turbine CapEx (millions, USD): {project.turbine_capex / 1e6:,.2f}\")\n", + "print(f\"Turbine CapEx (USD) per kW: {project.turbine_capex_per_kw:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0ae7657d", + "metadata": {}, + "source": [ + "#### Overnight CapEx\n", + "\n", + "The `overnight_capex` provides the overnight capital cost (system and turbine CapEx) of the project." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "e3a82336", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overnight CapEx (millions, USD): 1,785.43\n" + ] + } + ], + "source": [ + "print(f\"Overnight CapEx (millions, USD): {project.overnight_capex / 1e6:,.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a0eb444d", + "metadata": {}, + "source": [ + "## Logging\n", + "\n", + "The installation logs can produced in varying details from high-level phase start and end dates, and\n", + "all the way down to the detailed installation logics. This section will go through the methods\n", + "provided to access these data and demonstrate some simple ways of displaying it conveniently.\n", + "\n", + "### Installation Progress\n", + "\n", + "The `progress_summary` provides an aggregated view of the `progress_logs` to show the number\n", + "of completed component installations for each month in the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c0287378", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: {'Array String': 1,\n", + " 'Offshore Substation': 1,\n", + " 'Substructure': 15,\n", + " 'Turbine': 8},\n", + " 2: {'Array String': 1, 'Substructure': 18, 'Turbine': 10},\n", + " 3: {'Substructure': 17, 'Turbine': 8},\n", + " 4: {'Array String': 2, 'Turbine': 10},\n", + " 5: {'Array String': 1, 'Export System': 1, 'Turbine': 8},\n", + " 6: {'Array String': 2, 'Turbine': 6},\n", + " 7: {'Array String': 1},\n", + " 8: {'Array String': 1},\n", + " 9: {}}\n" + ] + } + ], + "source": [ + "pprint(project.progress_summary)" + ] + }, + { + "cell_type": "markdown", + "id": "36e977ea", + "metadata": {}, + "source": [ + "The `project_logs` provides a list of the when a component installation was completed using the\n", + "total number of hours since the start of the simulation.\n", + "\n", + "As an example, this looks like the following:\n", + "\n", + "```python\n", + "[\n", + " ('Offshore Substation', 88.0925357142857),\n", + " ('Turbine', 97.7933333333333),\n", + " ('Substructure', 130.14586018219498),\n", + " ('Substructure', 147.89172036438998),\n", + " ('Turbine', 150.18666666666658),\n", + " ...\n", + "]\n", + "```\n", + "\n", + "### Phase timing\n", + "\n", + "The `phase_dates` provides access to the starting and ending time of each installation phase as\n", + "a dictionary. In the following example, we will convert this data into a Pandas DataFrame with\n", + "datetime formatting, and produce a Gantt chart to highlight where the phases occur relative to\n", + "each other." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d58074e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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startend
ExportCableInstallation2010-03-25 08:00:002010-05-22 15:25:00
ArrayCableInstallation2010-01-01 00:00:002010-09-02 09:37:00
MonopileInstallation2010-01-01 00:00:002010-03-30 10:53:00
OffshoreSubstationInstallation2010-01-01 00:00:002010-01-04 16:05:00
ScourProtectionInstallation2010-01-01 00:00:002010-02-23 06:56:00
TurbineInstallation2010-01-01 00:00:002010-06-18 20:16:00
\n", + "
" + ], + "text/plain": [ + " start end\n", + "ExportCableInstallation 2010-03-25 08:00:00 2010-05-22 15:25:00\n", + "ArrayCableInstallation 2010-01-01 00:00:00 2010-09-02 09:37:00\n", + "MonopileInstallation 2010-01-01 00:00:00 2010-03-30 10:53:00\n", + "OffshoreSubstationInstallation 2010-01-01 00:00:00 2010-01-04 16:05:00\n", + "ScourProtectionInstallation 2010-01-01 00:00:00 2010-02-23 06:56:00\n", + "TurbineInstallation 2010-01-01 00:00:00 2010-06-18 20:16:00" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame.from_dict(project.phase_dates).T\n", + "df.start = pd.to_datetime(df.start)\n", + "df.end = pd.to_datetime(df.end)\n", + "df = df.sort_values(\"start\", ascending=False)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "cfd95776", + "metadata": {}, + "source": [ + "Below, we can see the installation timing is not quite realistic given the WTIV is used for the\n", + "monopile, turbine, and OSS installations and the cabling vessel is used for both the array and\n", + "export cabling installations. For both vessels, there should not be overlapping installations\n", + "unless multiple vessels are made available for these actions." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e751ccf2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 4))\n", + "ax = fig.add_subplot(111)\n", + "\n", + "ax.barh(y=df.index, width=df.end - df.start, left=df.start);\n", + "\n", + "annotation = (\n", + " f\"Total Installation Time: {project.installation_time / 24:.0f} days\\n\"\n", + " f\"Total Elapsed Time: {project.project_days} days\"\n", + ")\n", + "ax.text(\n", + " pd.to_datetime(\"2010-05-06\"), 3, annotation,\n", + " bbox={\"boxstyle\": \"square\", \"fc\": (0.9, 0.9, 0.9, 0.9), \"linewidth\": 0.5},\n", + " ha=\"left\", va=\"center\", size=12,\n", + ")\n", + "\n", + "ax.grid(axis=\"x\")\n", + "ax.set_axisbelow(True)\n", + "ax.set_xlim(pd.to_datetime(\"2009-12\"), pd.to_datetime(\"2010-10\"))\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "e56facf8", + "metadata": {}, + "source": [ + "### Detailed Event Timing\n", + "\n", + "The `actions` property provides access to a JSON-style list of every step taken during the\n", + "installation simulation. It is highly recommended to convert this to a Pandas DataFrame or similar\n", + "for inspection. Below, we will walk through some basic filtering of these data." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "1f14e0c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cost_multiplieragentactiondurationcostleveltimephasephase_namesite_depthhub_heightper_triplocationmax_waveheightmax_windspeedtransit_speed
00.5Array Cable Installation VesselMobilize72.0361756.5ACTION0.0ArrayCableInstallationNaNNaNNaNNaNNaNNaNNaNNaN
11.0WTIVMobilize168.02800000.0ACTION0.0MonopileInstallationNaNNaNNaNNaNNaNNaNNaNNaN
20.5Heavy Lift VesselMobilize72.0936918.0ACTION0.0OffshoreSubstationInstallationNaNNaNNaNNaNNaNNaNNaNNaN
30.5Feeder 0Mobilize72.0220858.5ACTION0.0OffshoreSubstationInstallationNaNNaNNaNNaNNaNNaNNaNNaN
40.5SPI VesselMobilize72.0224860.5ACTION0.0ScourProtectionInstallationNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " cost_multiplier agent action duration \\\n", + "0 0.5 Array Cable Installation Vessel Mobilize 72.0 \n", + "1 1.0 WTIV Mobilize 168.0 \n", + "2 0.5 Heavy Lift Vessel Mobilize 72.0 \n", + "3 0.5 Feeder 0 Mobilize 72.0 \n", + "4 0.5 SPI Vessel Mobilize 72.0 \n", + "\n", + " cost level time phase phase_name \\\n", + "0 361756.5 ACTION 0.0 ArrayCableInstallation NaN \n", + "1 2800000.0 ACTION 0.0 MonopileInstallation NaN \n", + "2 936918.0 ACTION 0.0 OffshoreSubstationInstallation NaN \n", + "3 220858.5 ACTION 0.0 OffshoreSubstationInstallation NaN \n", + "4 224860.5 ACTION 0.0 ScourProtectionInstallation NaN \n", + "\n", + " site_depth hub_height per_trip location max_waveheight max_windspeed \\\n", + "0 NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN \n", + "\n", + " transit_speed \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(project.actions)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "bb01e627", + "metadata": {}, + "source": [ + "Using the data frame we can filter produce vessel timing summaries for a single phase or a single\n", + "vessel, or any combination of vessels and phases. Below is a demonstration of filtering the time\n", + "spent in various activities during the monopile installation. From an operational standpoint, this\n", + "provides insight into what actions take the longest or cost the most, and can provide a means to\n", + "identify room for innovation or process efficiencies. Please see the\n", + "[project manager phase timing tutorial](#phase-dependent-timing) for more information about\n", + "customizing timing dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5e378669", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
  durationcost
agentaction  
WTIVBolt TP200.003,333,333.33
Crane Reequip100.001,666,666.67
Drive Monopile75.001,250,000.00
Fasten Monopile600.0010,000,000.00
Fasten Transition Piece400.006,666,666.67
Jackdown15.83263,888.89
Jackup15.83263,888.89
Lower Monopile0.162,708.33
Lower TP50.00833,333.33
Mobilize168.002,800,000.00
Position Onsite100.001,666,666.67
Release Monopile150.002,500,000.00
Release Transition Piece100.001,666,666.67
RovSurvey50.00833,333.33
Transit235.603,926,666.67
Upend Monopile30.46507,730.71
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mp_install = df.loc[df.phase.eq(\"MonopileInstallation\")]\n", + "mp_vessel_summary = (\n", + " mp_install[[\"agent\", \"action\", \"duration\", \"cost\"]]\n", + " .groupby([\"agent\", \"action\"])\n", + " .sum()\n", + " .style\n", + " .format(\"{:,.2f}\")\n", + ")\n", + "mp_vessel_summary" + ] + }, + { + "cell_type": "markdown", + "id": "f777eaea", + "metadata": {}, + "source": [ + "## Cash Flow and Net Present Value\n", + "\n", + "The `ProjectManager` includes a basic cash flow and net present value (NPV) model. The project must\n", + "have the array, export, and substation installation models configured for this model to be\n", + "applicable. The model will find the point in the project logs where the substation and export\n", + "cable installations were completed and where each completed string of array cables was installed.\n", + "When all three of these conditions are met, the project can begin to generate energy and produce\n", + "revenue. The revenue generation is then superimposed on the monthly spend of the installation\n", + "models for the `project.cash_flow`. Please note this assumes a fixed operational expenditure (OpEx).\n", + "\n", + "The NPV of the project can then be calculated and is available through `npv`. The underlying\n", + "financial assumptions for this model are also contained within the `project_parameters` section of\n", + "the ORBIT configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "fc05bd45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NPV: $1,493.98 (millions, USD)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(f\"NPV: ${project.npv / 1e6:,.2f} (millions, USD)\")" + ] + }, + { + "cell_type": "markdown", + "id": "b9f4e828", + "metadata": {}, + "source": [ + "Below, we highlight the first 12 months of the project cash flow. In the 10th month we can see that\n", + "there are no more installation costs, and the project produces the same values for each field\n", + "until the end of the project." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "62f58e96", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 monthly_opexmonthly_expensesmonthly_revenuecash_flow
00.0046,066,981.590.00-46,066,981.59
10.0035,066,816.850.00-35,066,816.85
20.0033,022,561.610.00-33,022,561.61
30.0027,488,874.200.00-27,488,874.20
44,500,000.0030,140,360.568,409,600.00-21,730,760.56
56,300,000.0019,454,128.7711,773,440.00-7,680,688.77
67,200,000.0015,594,619.2313,455,360.00-2,139,259.23
77,500,000.0014,520,561.7214,016,000.00-504,561.72
87,500,000.008,078,377.0114,016,000.005,937,622.99
97,500,000.007,500,000.0014,016,000.006,516,000.00
107,500,000.007,500,000.0014,016,000.006,516,000.00
117,500,000.007,500,000.0014,016,000.006,516,000.00
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.concat(\n", + " [\n", + " pd.DataFrame(project.monthly_opex.values(), columns=[\"monthly_opex\"]),\n", + " pd.DataFrame(project.monthly_expenses.values(), columns=[\"monthly_expenses\"]),\n", + " pd.DataFrame(project.monthly_revenue.values(), columns=[\"monthly_revenue\"]),\n", + " pd.DataFrame(project.cash_flow.values(), columns=[\"cash_flow\"]),\n", + " ],\n", + " axis=1\n", + ").head(12).style.format(\"{:,.2f}\")" + ] + } + ], + "metadata": { + "jupytext": { + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.19.1" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "source_map": [ + 12, + 28, + 44, + 53, + 55, + 66, + 71, + 79, + 82, + 94, + 96, + 115, + 118, + 126, + 129, + 134, + 137, + 144, + 147, + 157, + 160, + 165, + 168, + 179, + 182, + 188, + 191, + 198, + 201, + 211, + 214, + 224, + 227, + 234, + 239, + 242, + 253, + 260, + 271, + 274, + 280, + 283, + 289, + 291, + 304, + 306, + 331, + 337, + 344, + 364, + 372, + 375, + 385, + 395, + 411, + 413, + 419 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cable_installation.ipynb b/examples/cable_installation.ipynb new file mode 100644 index 00000000..c6e80474 --- /dev/null +++ b/examples/cable_installation.ipynb @@ -0,0 +1,1025 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7f1c7c2c", + "metadata": {}, + "source": [ + "# Cable Laying and Burying\n", + "\n", + "This guide will demonstrate the use of a combined cable laying and burying vessel compared to using\n", + "separate cable laying and burying vessels. Here we will focus on the array cabling, but the same\n", + "logic applies to the export cables." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bf8ca019", + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import pandas as pd\n", + "\n", + "from ORBIT import ProjectManager" + ] + }, + { + "cell_type": "markdown", + "id": "76354809", + "metadata": {}, + "source": [ + "Below, we set up a base configuration using an imagined cable and sections (25 each of 1km and 2km cable sections) designed for simplicity." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0a607a62", + "metadata": {}, + "outputs": [], + "source": [ + "base_config = {\n", + " \"site\": {\"distance\": 20, \"depth\": 35},\n", + " \"array_system\": {\n", + " \"system_cost\": 50e6,\n", + " \"cables\": {\n", + " \"ExampleCable\": {\n", + " \"linear_density\": 40,\n", + " \"cable_sections\": [(2, 25), (1, 25)]\n", + " }\n", + " }\n", + " },\n", + " \"install_phases\": [\"ArrayCableInstallation\"]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "786ed6e0", + "metadata": {}, + "source": [ + "## Single Cable Laying and Burying Process\n", + "\n", + "Now we can add a cable laying vessel that will simultaneously lay and bury cables by defining the\n", + "`array_cable_install_vessel`. For export cables, this is the `export_cable_install_vessel`. We will\n", + "create and run the project for later results comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e5ad6587", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ORBIT library intialized at '/Users/rhammond/GitHub_Public/ORBIT/library'\n" + ] + } + ], + "source": [ + "config_combined = deepcopy(base_config)\n", + "config_combined[\"array_cable_install_vessel\"] = \"example_cable_lay_vessel\"\n", + "\n", + "project_combined = ProjectManager(config_combined)\n", + "project_combined.run()" + ] + }, + { + "cell_type": "markdown", + "id": "2a7f9980", + "metadata": {}, + "source": [ + "## Separate Cable Laying and Burying Processes\n", + "\n", + "Using the same base configuration, we can now signal to the simulation to use a separate cable\n", + "laying and burying process by defining both the `array_cable_install_vessel` and\n", + "`array_cable_bury_vessel`. Note that the laying and combined vessel configuration keys are the same,\n", + "so that a separate input is only required when the cable burying vessel is utilized. Similar to the\n", + "above example, the export cable burying vessel is `export_cable_bury_vessel`.\n", + "\n", + "Even though the vessel is the same, by defining both vessel keys, we indicate that the processes\n", + "should be separated." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "12c1634f", + "metadata": {}, + "outputs": [], + "source": [ + "config_separate = deepcopy(base_config)\n", + "config_separate[\"array_cable_install_vessel\"] = \"example_cable_lay_vessel\"\n", + "config_separate[\"array_cable_bury_vessel\"] = \"example_cable_lay_vessel\"\n", + "\n", + "project_separate = ProjectManager(config_separate)\n", + "project_separate.run()" + ] + }, + { + "cell_type": "markdown", + "id": "9bb884fd", + "metadata": {}, + "source": [ + "## Including a Trenching Vessel\n", + "\n", + "A third option is to also define a cable trenching vessel that digs out the trench for the cable\n", + "to lie in prior to the cable laying. This is often required for rocky soil types. Similar to the\n", + "separate process, we simply define the trenching vessel to activate the separated process using\n", + "the `array_cable_trench_vessel` key or `export_cable_trench_vessel` for export cables." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "409698b6", + "metadata": {}, + "outputs": [], + "source": [ + "config_separate_with_trench = deepcopy(base_config)\n", + "config_separate_with_trench[\"array_cable_install_vessel\"] = \"example_cable_lay_vessel\"\n", + "config_separate_with_trench[\"array_cable_bury_vessel\"] = \"example_cable_lay_vessel\"\n", + "config_separate_with_trench[\"array_cable_trench_vessel\"] = \"example_cable_lay_vessel\"\n", + "\n", + "# Run\n", + "project_separate_with_trench = ProjectManager(config_separate_with_trench)\n", + "project_separate_with_trench.run()" + ] + }, + { + "cell_type": "markdown", + "id": "9084601c", + "metadata": {}, + "source": [ + "## Viewing the results\n", + "\n", + "Below we show the combined process for laying and burying the first cable. Note the \"action\"\n", + "column contains the \"Lay/Bury\" action to indicate the combined process." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "78ef1baa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cost_multiplieragentactiondurationcostleveltimephasephase_namemax_waveheightmax_windspeedtransit_speed
3NaNArray Cable Installation VesselPosition Onsite2.020097.583333ACTION9.73913ArrayCableInstallationNaNNaNNaNNaN
4NaNArray Cable Installation VesselPrepare Cable1.010048.791667ACTION10.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
5NaNArray Cable Installation VesselPull In Cable5.555268.354167ACTION16.23913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
6NaNArray Cable Installation VesselTerminate Cable5.555268.354167ACTION21.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
7NaNArray Cable Installation VesselLower Cable1.010048.791667ACTION22.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
8NaNArray Cable Installation VesselLay/Bury Cable32.0321561.333333ACTION54.73913ArrayCableInstallationArrayCableInstallation2.025.011.5
9NaNArray Cable Installation VesselPrepare Cable1.010048.791667ACTION55.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
10NaNArray Cable Installation VesselPull In Cable5.555268.354167ACTION61.23913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
11NaNArray Cable Installation VesselTerminate Cable5.555268.354167ACTION66.73913ArrayCableInstallationArrayCableInstallationNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " cost_multiplier agent action \\\n", + "3 NaN Array Cable Installation Vessel Position Onsite \n", + "4 NaN Array Cable Installation Vessel Prepare Cable \n", + "5 NaN Array Cable Installation Vessel Pull In Cable \n", + "6 NaN Array Cable Installation Vessel Terminate Cable \n", + "7 NaN Array Cable Installation Vessel Lower Cable \n", + "8 NaN Array Cable Installation Vessel Lay/Bury Cable \n", + "9 NaN Array Cable Installation Vessel Prepare Cable \n", + "10 NaN Array Cable Installation Vessel Pull In Cable \n", + "11 NaN Array Cable Installation Vessel Terminate Cable \n", + "\n", + " duration cost level time phase \\\n", + "3 2.0 20097.583333 ACTION 9.73913 ArrayCableInstallation \n", + "4 1.0 10048.791667 ACTION 10.73913 ArrayCableInstallation \n", + "5 5.5 55268.354167 ACTION 16.23913 ArrayCableInstallation \n", + "6 5.5 55268.354167 ACTION 21.73913 ArrayCableInstallation \n", + "7 1.0 10048.791667 ACTION 22.73913 ArrayCableInstallation \n", + "8 32.0 321561.333333 ACTION 54.73913 ArrayCableInstallation \n", + "9 1.0 10048.791667 ACTION 55.73913 ArrayCableInstallation \n", + "10 5.5 55268.354167 ACTION 61.23913 ArrayCableInstallation \n", + "11 5.5 55268.354167 ACTION 66.73913 ArrayCableInstallation \n", + "\n", + " phase_name max_waveheight max_windspeed transit_speed \n", + "3 NaN NaN NaN NaN \n", + "4 ArrayCableInstallation NaN NaN NaN \n", + "5 ArrayCableInstallation NaN NaN NaN \n", + "6 ArrayCableInstallation NaN NaN NaN \n", + "7 ArrayCableInstallation NaN NaN NaN \n", + "8 ArrayCableInstallation 2.0 25.0 11.5 \n", + "9 ArrayCableInstallation NaN NaN NaN \n", + "10 ArrayCableInstallation NaN NaN NaN \n", + "11 ArrayCableInstallation NaN NaN NaN " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_combined = pd.DataFrame(project_combined.actions)\n", + "df_combined.iloc[3:12]" + ] + }, + { + "cell_type": "markdown", + "id": "7cef009a", + "metadata": {}, + "source": [ + "Now, we demonstrate the separate process by combining the separate laying and burying steps taken\n", + "for the first cable. Note that we have to concatenate two separate sections of the actions log\n", + "to highlight this process. For each process the vessel has to \"Position Onsite\", then go on\n", + "with the separate logic. For the burying process, this is much simpler than the intial laying\n", + "and cable connection." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "552f5267", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cost_multiplieragentactiondurationcostleveltimephasephase_namemax_waveheightmax_windspeedtransit_speed
4NaNArray Cable Installation VesselPosition Onsite2.00020097.583333ACTION9.739130ArrayCableInstallationNaNNaNNaNNaN
5NaNArray Cable Installation VesselPrepare Cable1.00010048.791667ACTION10.739130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
6NaNArray Cable Installation VesselPull In Cable5.50055268.354167ACTION16.239130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
7NaNArray Cable Installation VesselTerminate Cable5.50055268.354167ACTION21.739130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
8NaNArray Cable Installation VesselLower Cable1.00010048.791667ACTION22.739130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
9NaNArray Cable Installation VesselLay Cable5.00050243.958333ACTION27.739130ArrayCableInstallationArrayCableInstallation2.025.011.5
10NaNArray Cable Installation VesselPrepare Cable1.00010048.791667ACTION28.739130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
11NaNArray Cable Installation VesselPull In Cable5.50055268.354167ACTION34.239130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
12NaNArray Cable Installation VesselTerminate Cable5.50055268.354167ACTION39.739130ArrayCableInstallationArrayCableInstallationNaNNaNNaN
455NaNArray Cable Burial VesselPosition Onsite2.00020097.583333ACTION1548.978261ArrayCableInstallationNaNNaNNaNNaN
456NaNArray Cable Burial VesselBury Cable4.82548485.419792ACTION1553.803261ArrayCableInstallationArrayCableInstallation2.025.011.5
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" + ], + "text/plain": [ + " cost_multiplier agent action \\\n", + "4 NaN Array Cable Installation Vessel Position Onsite \n", + "5 NaN Array Cable Installation Vessel Prepare Cable \n", + "6 NaN Array Cable Installation Vessel Pull In Cable \n", + "7 NaN Array Cable Installation Vessel Terminate Cable \n", + "8 NaN Array Cable Installation Vessel Lower Cable \n", + "9 NaN Array Cable Installation Vessel Lay Cable \n", + "10 NaN Array Cable Installation Vessel Prepare Cable \n", + "11 NaN Array Cable Installation Vessel Pull In Cable \n", + "12 NaN Array Cable Installation Vessel Terminate Cable \n", + "455 NaN Array Cable Burial Vessel Position Onsite \n", + "456 NaN Array Cable Burial Vessel Bury Cable \n", + "\n", + " duration cost level time phase \\\n", + "4 2.000 20097.583333 ACTION 9.739130 ArrayCableInstallation \n", + "5 1.000 10048.791667 ACTION 10.739130 ArrayCableInstallation \n", + "6 5.500 55268.354167 ACTION 16.239130 ArrayCableInstallation \n", + "7 5.500 55268.354167 ACTION 21.739130 ArrayCableInstallation \n", + "8 1.000 10048.791667 ACTION 22.739130 ArrayCableInstallation \n", + "9 5.000 50243.958333 ACTION 27.739130 ArrayCableInstallation \n", + "10 1.000 10048.791667 ACTION 28.739130 ArrayCableInstallation \n", + "11 5.500 55268.354167 ACTION 34.239130 ArrayCableInstallation \n", + "12 5.500 55268.354167 ACTION 39.739130 ArrayCableInstallation \n", + "455 2.000 20097.583333 ACTION 1548.978261 ArrayCableInstallation \n", + "456 4.825 48485.419792 ACTION 1553.803261 ArrayCableInstallation \n", + "\n", + " phase_name max_waveheight max_windspeed transit_speed \n", + "4 NaN NaN NaN NaN \n", + "5 ArrayCableInstallation NaN NaN NaN \n", + "6 ArrayCableInstallation NaN NaN NaN \n", + "7 ArrayCableInstallation NaN NaN NaN \n", + "8 ArrayCableInstallation NaN NaN NaN \n", + "9 ArrayCableInstallation 2.0 25.0 11.5 \n", + "10 ArrayCableInstallation NaN NaN NaN \n", + "11 ArrayCableInstallation NaN NaN NaN \n", + "12 ArrayCableInstallation NaN NaN NaN \n", + "455 NaN NaN NaN NaN \n", + "456 ArrayCableInstallation 2.0 25.0 11.5 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_separate = pd.DataFrame(project_separate.actions)\n", + "pd.concat((df_separate.iloc[4:13], df_separate.iloc[455:457]))\n" + ] + }, + { + "cell_type": "markdown", + "id": "53a4bf06", + "metadata": {}, + "source": [ + "Similar to the above, when we add trenching as a separate step, we have three discrete stages to\n", + "combine to demonstrate the trenching, laying, and burying for the first cable." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c78cc13b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cost_multiplieragentactiondurationcostleveltimephasephase_namemax_waveheightmax_windspeedtransit_speed
4NaNArray Cable Trench VesselPosition Onsite2.00020097.583333ACTION3.739130ArrayCableInstallationNaNNaNNaNNaN
5NaNArray Cable Trench VesselDig Trench19.300193941.679167ACTION23.039130ArrayCableInstallationArrayCableInstallation2.025.011.5
107NaNArray Cable Installation VesselPosition Onsite2.00020097.583333ACTION828.217391ArrayCableInstallationNaNNaNNaNNaN
108NaNArray Cable Installation VesselPrepare Cable1.00010048.791667ACTION829.217391ArrayCableInstallationArrayCableInstallationNaNNaNNaN
109NaNArray Cable Installation VesselPull In Cable5.50055268.354167ACTION834.717391ArrayCableInstallationArrayCableInstallationNaNNaNNaN
110NaNArray Cable Installation VesselTerminate Cable5.50055268.354167ACTION840.217391ArrayCableInstallationArrayCableInstallationNaNNaNNaN
111NaNArray Cable Installation VesselLower Cable1.00010048.791667ACTION841.217391ArrayCableInstallationArrayCableInstallationNaNNaNNaN
112NaNArray Cable Installation VesselLay Cable5.00050243.958333ACTION846.217391ArrayCableInstallationArrayCableInstallation2.025.011.5
113NaNArray Cable Installation VesselPrepare Cable1.00010048.791667ACTION847.217391ArrayCableInstallationArrayCableInstallationNaNNaNNaN
114NaNArray Cable Installation VesselPull In Cable5.50055268.354167ACTION852.717391ArrayCableInstallationArrayCableInstallationNaNNaNNaN
115NaNArray Cable Installation VesselTerminate Cable5.50055268.354167ACTION858.217391ArrayCableInstallationArrayCableInstallationNaNNaNNaN
558NaNArray Cable Burial VesselPosition Onsite2.00020097.583333ACTION2367.456522ArrayCableInstallationNaNNaNNaNNaN
559NaNArray Cable Burial VesselBury Cable4.82548485.419792ACTION2372.281522ArrayCableInstallationArrayCableInstallation2.025.011.5
\n", + "
" + ], + "text/plain": [ + " cost_multiplier agent action \\\n", + "4 NaN Array Cable Trench Vessel Position Onsite \n", + "5 NaN Array Cable Trench Vessel Dig Trench \n", + "107 NaN Array Cable Installation Vessel Position Onsite \n", + "108 NaN Array Cable Installation Vessel Prepare Cable \n", + "109 NaN Array Cable Installation Vessel Pull In Cable \n", + "110 NaN Array Cable Installation Vessel Terminate Cable \n", + "111 NaN Array Cable Installation Vessel Lower Cable \n", + "112 NaN Array Cable Installation Vessel Lay Cable \n", + "113 NaN Array Cable Installation Vessel Prepare Cable \n", + "114 NaN Array Cable Installation Vessel Pull In Cable \n", + "115 NaN Array Cable Installation Vessel Terminate Cable \n", + "558 NaN Array Cable Burial Vessel Position Onsite \n", + "559 NaN Array Cable Burial Vessel Bury Cable \n", + "\n", + " duration cost level time phase \\\n", + "4 2.000 20097.583333 ACTION 3.739130 ArrayCableInstallation \n", + "5 19.300 193941.679167 ACTION 23.039130 ArrayCableInstallation \n", + "107 2.000 20097.583333 ACTION 828.217391 ArrayCableInstallation \n", + "108 1.000 10048.791667 ACTION 829.217391 ArrayCableInstallation \n", + "109 5.500 55268.354167 ACTION 834.717391 ArrayCableInstallation \n", + "110 5.500 55268.354167 ACTION 840.217391 ArrayCableInstallation \n", + "111 1.000 10048.791667 ACTION 841.217391 ArrayCableInstallation \n", + "112 5.000 50243.958333 ACTION 846.217391 ArrayCableInstallation \n", + "113 1.000 10048.791667 ACTION 847.217391 ArrayCableInstallation \n", + "114 5.500 55268.354167 ACTION 852.717391 ArrayCableInstallation \n", + "115 5.500 55268.354167 ACTION 858.217391 ArrayCableInstallation \n", + "558 2.000 20097.583333 ACTION 2367.456522 ArrayCableInstallation \n", + "559 4.825 48485.419792 ACTION 2372.281522 ArrayCableInstallation \n", + "\n", + " phase_name max_waveheight max_windspeed transit_speed \n", + "4 NaN NaN NaN NaN \n", + "5 ArrayCableInstallation 2.0 25.0 11.5 \n", + "107 NaN NaN NaN NaN \n", + "108 ArrayCableInstallation NaN NaN NaN \n", + "109 ArrayCableInstallation NaN NaN NaN \n", + "110 ArrayCableInstallation NaN NaN NaN \n", + "111 ArrayCableInstallation NaN NaN NaN \n", + "112 ArrayCableInstallation 2.0 25.0 11.5 \n", + "113 ArrayCableInstallation NaN NaN NaN \n", + "114 ArrayCableInstallation NaN NaN NaN \n", + "115 ArrayCableInstallation NaN NaN NaN \n", + "558 NaN NaN NaN NaN \n", + "559 ArrayCableInstallation 2.0 25.0 11.5 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_separate_with_trench = pd.DataFrame(project_separate_with_trench.actions)\n", + "pd.concat((\n", + " df_separate_with_trench.iloc[4:6],\n", + " df_separate_with_trench.iloc[107:116],\n", + " df_separate_with_trench.iloc[558:560],\n", + "))" + ] + } + ], + "metadata": { + "jupytext": { + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.19.1" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "source_map": [ + 12, + 20, + 26, + 30, + 44, + 52, + 58, + 71, + 78, + 87, + 96, + 103, + 106, + 114, + 118, + 123 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/configs/example_fixed_project.yaml b/examples/configs/example_fixed_project.yaml index ffbcade6..0a0c9ae3 100644 --- a/examples/configs/example_fixed_project.yaml +++ b/examples/configs/example_fixed_project.yaml @@ -10,9 +10,6 @@ plant: row_spacing: 7 substation_distance: 1 turbine_spacing: 7 -landfall: - interconnection_distance: 3 - trench_length: 2 turbine: 12MW_generic # Vessels array_cable_install_vessel: example_cable_lay_vessel @@ -32,6 +29,9 @@ array_system_design: export_system_design: cables: XLPE_500mm_132kV percent_added_length: 0.0 + landfall: + interconnection_distance: 3 + trench_length: 2 scour_protection_design: cost_per_tonne: 40 scour_protection_depth: 1 diff --git a/examples/configs/example_gravity-based_project.yaml b/examples/configs/example_gravity_based_project.yaml similarity index 84% rename from examples/configs/example_gravity-based_project.yaml rename to examples/configs/example_gravity_based_project.yaml index f679d195..b75c8625 100644 --- a/examples/configs/example_gravity-based_project.yaml +++ b/examples/configs/example_gravity_based_project.yaml @@ -10,34 +10,31 @@ plant: row_spacing: 7 substation_distance: 1 turbine_spacing: 7 -landfall: - interconnection_distance: 3 - trench_length: 2 turbine: 15MW_generic # Vessels support_vessel: example_support_vessel towing_vessel: example_towing_vessel towing_vessel_groups: towing_vessels: 3 - station_keeping_vessels: 2 + ahts_vessels: 2 array_cable_install_vessel: example_cable_lay_vessel export_cable_install_vessel: example_cable_lay_vessel export_cable_bury_vessel: example_cable_lay_vessel oss_install_vessel: example_heavy_lift_vessel spi_vessel: example_scour_protection_vessel ahts_vessel: example_ahts_vessel -#wtiv: example_wtiv # GBF installation without WTIV +# wtiv: example_wtiv # GBF installation without WTIV # Substructure -substructure: - unit_cost: 5700000 - takt_time: 0 - #towing_speed: 6.5, considered on towing vessel file. +substructure: + unit_cost: 5700000 + takt_time: 0 + # towing_speed: 6.5, considered on towing vessel file. port: monthly_rate: 520700 # This is the turbine assembly crane monthly rate. The port rental costs are put in as line items in the LCOE calc (where CapEx breakdown is updated) to include the summed port & quayside cost assumed at 8 months. This 8 months includeds 6 months takt time and 2 months storage (why takt is set to 0). sub_assembly_lines: 2 # how many you can produce in parallel - turbine_assembly_cranes: 2 - sub_storage: 1 - assembly_storage: 1 + turbine_assembly_cranes: 2 + sub_storage: 1 + assembly_storage: 1 # Module Specific OffshoreSubstationInstallation: feeder: example_heavy_feeder @@ -49,6 +46,9 @@ array_system_design: export_system_design: cables: XLPE_1000mm_220kV percent_added_length: 0.5 + landfall: + interconnection_distance: 3 + trench_length: 2 # Configured Phases design_phases: # Do not have a design phase for GBFs, we input unit_cost diff --git a/examples/configs/example_separate_monopile_turbine_vessel.yaml b/examples/configs/example_separate_monopile_turbine_vessel.yaml new file mode 100644 index 00000000..0abd1fab --- /dev/null +++ b/examples/configs/example_separate_monopile_turbine_vessel.yaml @@ -0,0 +1,69 @@ +# Site + Plant Parameters +site: + depth: 40 # 34 COE + distance: 120 + distance_to_landfall: 50 + mean_windspeed: 9 +plant: + layout: grid + num_turbines: 50 # 40 COE + row_spacing: 7 + substation_distance: 1 + turbine_spacing: 7 +turbine: 15MW_generic +# Vessels +wtiv: example_wtiv +support_vessel: example_support_vessel +towing_vessel: example_towing_vessel +towing_vessel_groups: + towing_vessels: 3 + ahts_vessels: 2 +array_cable_install_vessel: example_cable_lay_vessel +export_cable_install_vessel: example_cable_lay_vessel +export_cable_bury_vessel: example_cable_lay_vessel +oss_install_vessel: example_heavy_lift_vessel +spi_vessel: example_scour_protection_vessel +# Substructure +substructure: + unit_cost: 5700000 + takt_time: 0 + # towing_speed: 6.5, considered on towing vessel file. +port: + monthly_rate: 520700 # This is the turbine assembly crane monthly rate. The port rental costs are put in as line items in the LCOE calc (where CapEx breakdown is updated) to include the summed port & quayside cost assumed at 8 months. This 8 months includeds 6 months takt time and 2 months storage (why takt is set to 0). + sub_assembly_lines: 2 # how many you can produce in parallel + turbine_assembly_cranes: 2 + sub_storage: 1 + assembly_storage: 1 +# Module Specific +scour_protection_design: + cost_per_tonne: 40 +OffshoreSubstationInstallation: + feeder: example_heavy_feeder + num_feeders: 1 +array_system_design: + cables: + - XLPE_185mm_66kV + - XLPE_630mm_66kV +export_system_design: + cables: XLPE_1000mm_220kV + percent_added_length: 0.5 + landfall: + interconnection_distance: 3 + trench_length: 2 +# Configured Phases +design_phases: +- MonopileDesign +- ScourProtectionDesign +- ArraySystemDesign +- ExportSystemDesign +- OffshoreSubstationDesign +install_phases: + ArrayCableInstallation: 0 + ExportCableInstallation: 2000 + TurbineInstallation: 0 + OffshoreSubstationInstallation: 0 + ScourProtectionInstallation: 0 + MonopileInstallation: 0 +# Project Inputs +project_parameters: + turbine_capex: 1700 diff --git a/examples/cost_curves.ipynb b/examples/cost_curves.ipynb index 8620b1af..8067103c 100644 --- a/examples/cost_curves.ipynb +++ b/examples/cost_curves.ipynb @@ -1,1406 +1,1406 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cost Curve Creator\n", - "\n", - "This notebook enables fitting curves to ORBIT models to create a cost function that can be\n", - "embedded in NRWAL.\n", - "A variety of curve and surface fitting options are available, and new ones can be added easily.\n", - "There are also tools for visualizing the ORBIT data and fitted curves.\n", - "\n", - "## Dependencies\n", - "\n", - "- ORBIT\n", - "- ipympl enables interactive matplotlib elements in jupyter notebooks via the `%matplotlib widget` magic command below\n", - "\n", - "## Instructions\n", - "\n", - "Follow the steps below to configure and run the notebook.\n", - "\n", - "1. Create a basic ORBIT configuration file including at least the following sections:\n", - "- site\n", - "- turbine\n", - "- plant\n", - "\n", - "2. Configure the notebook by setting the following variables in the \"Configuration\" section:\n", - "- `BASE_CONFIG`: the ORBIT config file at a given path\n", - "- `DEPTHS`: a list of water depths to use for cost curves\n", - "- `MEAN_WIND_SPEED`: a list of mean wind speed to use for cost curves\n", - "- Add any additional global parameter ranges\n", - "\n", - "3. Run the notebook to establish a first-pass fit for the ORBIT data. This will also plot the ORBIT\n", - "data and curves.\n", - "\n", - "4. Refine the curve fits by swapping the curve-fit function from the options available in\n", - "the \"Curve Fit Library\" section\n", - "\n", - "## Practical Guidance\n", - "\n", - "This notebook specifically models spatially varying costs typically related to water depth\n", - "but in some cases other variables are considered. The same methods could be used to model\n", - "the cost relationship for other variables. The general workflow is to first create a parameterized\n", - "ORBIT model and obtain the cost as a function of the variables of interest.\n", - "Then, fit a curve or surface to the data by starting with the linear options.\n", - "Plot the data and curve fits to evaluate whether the linear forms are sufficient.\n", - "If not, move to the quadratic or higher order curve fits.\n", - "\n", - "A class `CostFunction` is provided to simplify running the ORBIT parameterization, fit the\n", - "curves to the data, and visualize the results. An example is given below to instantiate the\n", - "class:\n", - "```python\n", - "cost_function = CostFunction(\n", - " config={\"design_phases\": [\"MonopileDesign\"]},\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " \"site.mean_windspeed\": MEAN_WIND_SPEED,\n", - " },\n", - " results={\n", - " \"monopile_unit_cost\": lambda run: run.design_results[\"monopile\"][\"unit_cost\"],\n", - " }\n", - ")\n", - "```\n", - "\n", - "The config parameter is a dictionary containing additional configuration parameters to add to\n", - "the basic ORBIT configuration provided through the input file created in Step 1 in the instructions.\n", - "Any parameters given in the `CostFunction` config will be added to the base configuration or\n", - "overwritten if they already exist. The parameters dictionary contains the variables to be varied\n", - "in the cost function via `ORBIT.ParametricManager`, and the results dictionary sets the results\n", - "variables from ORBIT. Each of these dictionaries are passed directly to the\n", - "`ORBIT.ParametricManager` class.\n", - "\n", - "The fitted curves are saved on the `CostFunction` object and multiple types can exist at the\n", - "same time. Two versions of one type cannot be saved at the same time. To create a curve fit,\n", - "call one of the curve fit methods on the `CostFunction` instance. Then, an attribute is saved\n", - "on the instance with the curve fit type.\n", - "\n", - "Considerations:\n", - "- The `CostFunction` class supports parameterizations of at-most 2 variables.\n", - "- One instance of the `CostFunction` class can be used to fit multiple curves for a single\n", - " cost model.\n", - "- A new `CostFunction` instance should be created for each cost model.\n", - "\n", - "### Plotting API for 2D vs 3D plots\n", - "The `CostFunction` class handles 2D and 3D data seamlessly by using the x and z parameters for 2D\n", - "and adding y for 3D. The appropriate matplotlib API is used depending if the data is 2D or 3D.\n", - "From the calling script, be sure to configure the Axes that is given to `CostFunction.plot` with\n", - "the correct settings for 3D as listed in the table below.\n", - "\n", - "| Matplotlib setting | 2D | 3D |\n", - "|---------------------|----|----|\n", - "| Independent axis labels | `ax.set_xlabel()` | `ax.set_xlabel()`, `ax.set_zlabel()` |\n", - "| Dependent axis label | `ax.set_ylabel()` | `ax.set_zlabel()` |\n", - "\n", - "### Template workflow\n", - "\n", - "The following code block provides a template for creating a cost function for a model with\n", - "two independent parameters.\n", - "\n", - "```python\n", - "\n", - "# Create the CostFunction object with the ORBIT configuration for the parameterization\n", - "cost_function = CostFunction(\n", - " config={\n", - " \"design_phases\": [\"Design\"],\n", - " },\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " \"site.mean_windspeed\": MEAN_WIND_SPEED,\n", - " },\n", - " results={\n", - " \"system_cost\": lambda run: run.design_results[\"system\"][\"system_cost\"],\n", - " }\n", - ")\n", - "\n", - "# Run ORBIT via ORBIT.ParametricManager\n", - "cost_function.run()\n", - "\n", - "# Fit two curves (surfaces since there are two independent parameters) to the data.\n", - "# After running the following two commands, the CostFunction object will have two related\n", - "# attributes that store the curve fits.\n", - "cost_function.fit_curve(\"linear_2d\")\n", - "cost_function.fit_curve(\"quadratic_2d\")\n", - "\n", - "# Plot the data and curves\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(1, 1, 1)\n", - "ax.set_title(\"Depth vs mean wind speed\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Mean wind speed (m/s)\")\n", - "ax.set_zlabel(\"Cost ($)\")\n", - "cost_function.plot(ax, plot_data=True)\n", - "cost_function.plot(ax, plot_curves=[\"linear_1d\", \"quadratic_1d\"]) # These curves must have been generated first\n", - "# alternatively, the two lines above could be combined into a single line:\n", - "# cost_function.plot(ax, plot_data=True, plot_curves=[\"linear_1d\", \"quadratic_1d\"])\n", - "\n", - "# Export the curve function to a NRWAL-compatible file\n", - "cost_function.export(\"design.yaml\", \"design_system\")\n", - "```\n", - "\n", - "### Adding a new curve type\n", - "\n", - "There are a number of curve fit options in the `CostFunction` class, and more can be added by\n", - "creating a new method and connecting it in some key places in the class.\n", - "First, create a new method on the `CostFunction` class that follows the naming convention of\n", - "`{curve_type}_{dimension}` where `curve_type` is the name of the type of function like\n", - "\"exponential\" or \"linear\" and `dimension` is the number of independent variables the curve.\n", - "The function should return the fitted curve evaluated at the data points given to fit the curve.\n", - "A generic function signature is given below:\n", - "```python\n", - "class CostFunction:\n", - "\n", - " def curvetype_dimension(self):\n", - "\n", - " # Such as:\n", - " def linear_1d(self):\n", - "```\n", - "\n", - "To fit a curve to the data for one independent variable, it is recommended to use the\n", - "`scipy.optimize.curve_fit` function via the `Curves` class.\n", - "In general, a one-dimensional curve fit function will follow the form given below.\n", - "By setting the curve fit function `f`, you define the shape of the curve and set\n", - "the order of the coefficients in `self.coeffs` since they are returned in the order they are\n", - "given in the function signature.\n", - "The `Curves.polynomal_eval` function is available to easily evaluate polynomial curves, but other\n", - "curve-types can be evaluated by simply plugging in the data points (`self.x`) to the fitted\n", - "function.\n", - "\n", - "```python\n", - "# Define a function for a prototype curve; this is where you define the shape of the curve\n", - "def f(x, a, b):\n", - " return a * x + b\n", - "\n", - "# Call the scipy.optimize.curve_fit function and get the coefficients as a Numpy array\n", - "# Note that `self.x` and `self.z` are given since the CostFunction class adds a y\n", - "# only when there are more independent variables.\n", - "self.coeffs = Curves.fit(f, self.x, self.z)\n", - "\n", - "# Evaluate the curve at the data points (self.x)\n", - "self._linear_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)\n", - "```\n", - "\n", - "A two-dimensional curve (surface) fit will typically follow a similar process, as should below.\n", - "For these types, it is recommended to use the `numpy.linalg.lstsq` function.\n", - "First, reshape the data into a new array with each element containing the three-dimensional\n", - "data points.\n", - "Then, stack the data into a column matrix in the form of the equation that you're implementing.\n", - "See the comments in the code block for more information.\n", - "Evaluate the curve at the data points (`self.x`, `self.y`) by stating the form of the curve\n", - "with the coefficients from the curve fit.\n", - "\n", - "```python\n", - " # Reshape the data into a new array with each element containing the three-dimensional data points\n", - " data_to_fit = np.array(list(zip(self.x, self.y, self.z)))\n", - "\n", - " # Stack the data into a column matrix in the form of the equation that you're implementing.\n", - " # Here, the equation is z = ax + by + c and data_to_fit[:,0] are the x values,\n", - " # data_to_fit[:,1] are the y values. The third column is all ones to account for the constant\n", - " # term.\n", - " A = np.c_[\n", - " data_to_fit[:,0],\n", - " data_to_fit[:,1],\n", - " np.ones(data_to_fit.shape[0])\n", - " ]\n", - "\n", - " # Fit the curve to the data; the data is the cost and these are always `self.z` which is data_to_fit[:,2]\n", - " self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2])\n", - "\n", - " # Evaluate it on the same points as the input data\n", - " self._linear_2d_curve = self.coeffs[0]*self.x + self.coeffs[1]*self.y + self.coeffs[2]\n", - "```\n", - "\n", - "Finally, save the coefficients to `self.coeffs`, save the evaluated curve to\n", - "`self._{curve_type}_{dimension}_curve`, and add the corresponding if-statements\n", - "in `CostFunction.plot` and `CostFunction.export`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib widget\n", - "\n", - "from copy import deepcopy\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from scipy import stats, optimize, linalg\n", - "import yaml\n", - "\n", - "from ORBIT import (\n", - " ParametricManager,\n", - " load_config,\n", - ")\n", - "\n", - "import matplotlib as mpl\n", - "mpl.rcParams[\"figure.autolayout\"] = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuration\n", - "\n", - "Replace any of these throughout the notebook to customize a cost model parameterization." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "BASE_CONFIG = load_config(\"nrwal.yaml\")\n", - "\n", - "DEPTHS = [i for i in range(5, 60, 5)] # Ocean depth in meters\n", - "MEAN_WIND_SPEED = [i for i in range(2, 20, 2)] # Mean wind speed in m/s" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "orbit_to_nrwal_params = {\n", - " \"site.depth\": \"depth\",\n", - " \"site.mean_windspeed\": \"mean_windspeed\", # Not in NRWAL\n", - " \"site.distance_to_landfall\": \"dist_s_to_l\",\n", - " \"mooring_system_design.draft_depth\": \"draft_depth\", # Not in NRWAL\n", - " \"array_system_design.touchdown_distance\": \"touchdown_distance\", # Not in NRWAL\n", - " \"array_system_design.floating_cable_depth\": \"floating_cable_depth\", # Not in NRWAL\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Curve Fit Library" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class Curves():\n", - " \"\"\"\n", - " This class contains static methods for fitting data to various curve types.\n", - " Though they could exist outside of a class, consolidating them into a consistent\n", - " namespace allows for a simpler API throughout the script.\n", - " \"\"\"\n", - "\n", - " @staticmethod\n", - " def polynomial_eval(coeffs, data_points):\n", - " \"\"\"\n", - " This method evaluates a curve defined by a polynomial equation given a set of\n", - " coefficients and data points.\n", - "\n", - " Args:\n", - " coeffs (list): A list of coefficients for the curve. The order of the\n", - " coefficients should be from highest to lowest power.\n", - " data_points (list): A list of data points at which to evaluate the curve.\n", - "\n", - " Returns:\n", - " np.array: The curve evaluated at the given data points.\n", - " \"\"\"\n", - " curve = np.zeros_like(data_points)\n", - " for i, dp in enumerate(data_points):\n", - "\n", - " # This loop sums the terms of the polynomial\n", - " for j in range(len(coeffs)):\n", - " curve[i] += coeffs[j] * (dp ** (len(coeffs) - 1 - j))\n", - " return curve\n", - "\n", - " @staticmethod\n", - " def fit(func, x, y, fit_check=False):\n", - " if x is pd.Series:\n", - " x = x.to_numpy(dtype=np.float64)\n", - " elif x is np.array:\n", - " x = x.astype(np.float64)\n", - " if y is pd.Series:\n", - " y = y.to_numpy(dtype=np.float64)\n", - " elif y is np.array:\n", - " y = y.astype(np.float64)\n", - "\n", - " popt, pcov, nfodict, mesg, ier = optimize.curve_fit(func, x, y, full_output=True)\n", - "\n", - " if fit_check:\n", - " print(f\"mesg: {mesg}\")\n", - " print(f\"ier: {ier}\")\n", - " print(f\"Coefficients: {popt}\")\n", - " # print(f\"R-squared: {rvalue**2:.6f}\")\n", - " \n", - " return popt" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "class CostFunction():\n", - " \"\"\"\n", - " This class is used to create the ORBIT parameterization, fit a curve, plot the curve, and\n", - " export the function to NRWAL format. Parameterizations are limited to up to two independent\n", - " variables.\n", - " \"\"\"\n", - " def __init__(self, config: dict, parameters: dict, results: dict):\n", - " \"\"\"\n", - " On initialization, the config, parameters, and results dictionaries are prepared for\n", - " use in ORBIT.ParametricManager. Additionally, the independent variables are extracted\n", - " into x and y (for two-variable parameterizations) and z is extracted as the dependent\n", - " variable. Whether the cost function is 3D or 2D is determined by the length of the\n", - " parameters variable.\n", - "\n", - " Args:\n", - " config (str): Configuration settings to added to the BASE_CONFIG or overwrite\n", - " in the BASE_CONFIG. This must include the `design_phases` config.\n", - " parameters (dict): Parameters to use with ORBIT.ParametricManager; maximum of two\n", - " parameters are supported.\n", - " results (dict): Results to use with ORBIT.ParametricManager; this must include only\n", - " one variable.\n", - " \"\"\"\n", - " self.is_3d = False\n", - "\n", - " # Other attributes\n", - " # self.parametric\n", - " # self.x\n", - " # self.y\n", - " # self.z\n", - " # self.x_variable\n", - " # self.y_variable\n", - " self._linear_1d_curve = None\n", - " self._quadratic_1d_curve = None\n", - " self._poly3_1d_curve = None\n", - " self._linear_2d_curve = None\n", - " self._quadratic_2d_curve = None\n", - "\n", - " # Start with a copy of the global BASE_CONFIG and update it with the configuration\n", - " # given to this class\n", - " self.config = deepcopy(BASE_CONFIG)\n", - " self.config.update(config)\n", - "\n", - " self.parameters = deepcopy(parameters)\n", - " if len(self.parameters) > 2:\n", - " raise ValueError(\"This class is limited to parameterizations with two variables.\")\n", - "\n", - " # Puts the parameters and results settings into variables for use in parsing the ORBIT\n", - " # results and postprocessing the data\n", - " self.parameters = deepcopy(self.parameters)\n", - " _vars = list(self.parameters.keys())\n", - " self.x_variable = _vars.pop(0) # NOTE: This assumes the first parameter is site.depth; it's not critical to functionality but good to keep in mind\n", - " if len(_vars) == 1:\n", - " self.is_3d = True\n", - " self.y_variable = _vars.pop()\n", - " self.z_variable = list(results.keys())[0]\n", - "\n", - " self.results = deepcopy(results)\n", - " if len(results) != 1:\n", - " raise ValueError(\"This class is limited to results with one variable\")\n", - "\n", - " def run(self):\n", - " self.parametric = ParametricManager(self.config, self.parameters, self.results, product=True)\n", - " self.parametric.run()\n", - "\n", - " self.x = self.parametric.results[self.x_variable]\n", - " if self.is_3d:\n", - " self.y = self.parametric.results[self.y_variable]\n", - " self.z = self.parametric.results[self.z_variable]\n", - "\n", - "\n", - " ### --------- Curve fit functions --------- ###\n", - "\n", - " def linear_1d(self):\n", - " def f(x, a, b):\n", - " return a * x + b\n", - " self.coeffs = Curves.fit(f, self.x, self.z)\n", - " self._linear_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)\n", - "\n", - " def quadratic_1d(self):\n", - " def f(x, a, b, c):\n", - " return a * x**2 + b * x + c\n", - " self.coeffs = Curves.fit(f, self.x, self.z)\n", - " self._quadratic_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)\n", - "\n", - " def poly3_1d(self):\n", - " def f(x, a, b, c, d):\n", - " return a * x**3 + b * x**2 + c * x + d\n", - " self.coeffs = Curves.fit(f, self.x, self.z)\n", - " self._poly3_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)\n", - "\n", - " # def logarithmic_1d(self):\n", - " # pass\n", - "\n", - " # def exponential_1d(self):\n", - " # pass\n", - "\n", - " def linear_2d(self):\n", - " data_to_fit = np.array(list(zip(self.x, self.y, self.z)))\n", - "\n", - " # Best-fit linear plane\n", - " A = np.c_[\n", - " data_to_fit[:,0],\n", - " data_to_fit[:,1],\n", - " np.ones(data_to_fit.shape[0])\n", - " ]\n", - " self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2]) # coefficients\n", - "\n", - " # Evaluate it on the same points as the input data\n", - " self._linear_2d_curve = self.coeffs[0]*self.x + self.coeffs[1]*self.y + self.coeffs[2]\n", - "\n", - " def quadratic_2d(self):\n", - " data_to_fit = np.array(list(zip(self.x, self.y, self.z)))\n", - "\n", - " # best-fit quadratic curve\n", - " A = np.c_[\n", - " np.ones(data_to_fit.shape[0]),\n", - " data_to_fit[:,:2],\n", - " np.prod(data_to_fit[:,:2], axis=1),\n", - " data_to_fit[:,:2]**2\n", - " ]\n", - " self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2])\n", - "\n", - " # Evaluate it on the same points as the input data\n", - " # This dot product is equivalent to the sum of the terms of the polynomial;\n", - " # np.c_[] is used to concatenate the arrays into the correct form for the dot product\n", - " # and C is the coefficients of the polynomial\n", - " self._quadratic_2d_curve = np.dot(\n", - " np.c_[\n", - " np.ones(self.x.shape),\n", - " self.x,\n", - " self.y,\n", - " self.x*self.y,\n", - " self.x**2,\n", - " self.y**2\n", - " ],\n", - " self.coeffs\n", - " ).reshape(self.x.shape)\n", - "\n", - "\n", - " ### --------- Plotting functions --------- ###\n", - "\n", - " def plot(\n", - " self,\n", - " ax,\n", - " plot_data: bool = False,\n", - " plot_curves: list[str] = []\n", - " ):\n", - " if plot_data:\n", - " if self.is_3d:\n", - " ax.scatter(self.x, self.y, zs=self.z, zdir='z', label=\"Data\")\n", - " else:\n", - " ax.scatter(self.x, self.z, label=\"Data\")\n", - "\n", - " for curve in plot_curves:\n", - "\n", - " if curve == \"linear_1d\":\n", - " ax.plot(self.x, self._linear_1d_curve, label=\"Linear Fit\")\n", - "\n", - " if curve == \"quadratic_1d\":\n", - " ax.plot(self.x, self._quadratic_1d_curve, label=\"Quadratic Fit\")\n", - "\n", - " if curve == \"poly3_1d\":\n", - " ax.plot(self.x, self._poly3_1d_curve, label=\"Degree 3 Polynomial Fit\")\n", - "\n", - " if curve == \"linear_2d\":\n", - " ax.plot_surface(\n", - " np.reshape(self.x, (len(DEPTHS), -1)),\n", - " np.reshape(self.y, (len(DEPTHS), -1)),\n", - " np.reshape(self._linear_2d_curve, (len(DEPTHS), -1)),\n", - " alpha=0.3,\n", - " label=\"Linear Fit\"\n", - " )\n", - "\n", - " if curve == \"quadratic_2d\":\n", - " ax.plot_surface(\n", - " np.reshape(self.x, (len(DEPTHS), -1)),\n", - " np.reshape(self.y, (len(DEPTHS), -1)),\n", - " np.reshape(self._quadratic_2d_curve, (len(DEPTHS), -1)),\n", - " alpha=0.3,\n", - " label=\"Quadratic Fit\"\n", - " )\n", - "\n", - "\n", - " ### --------- Export functions --------- ###\n", - "\n", - " def export(self, filename: str, key: str, comments: str = \"\"):\n", - " \"\"\"\n", - " This function writes the curve equation to a file for use in NRWAL.\n", - "\n", - " Args:\n", - " filename (str): The file to write the curve equation to. If the file exists, the\n", - " equation is appended to the end of the file.\n", - " key (str): The key to use in the NRWAL file for the curve equation. In the key-value\n", - " pair, this argument is the key and the value is the equation string.\n", - " \"\"\"\n", - "\n", - " x_var = orbit_to_nrwal_params[self.x_variable]\n", - " if self.is_3d:\n", - " y_var = orbit_to_nrwal_params[self.y_variable]\n", - "\n", - " F = \"{:.1f}\"\n", - " S = \"{:s}\"\n", - " if self._linear_1d_curve is not None:\n", - " # y = ax + b\n", - " equation_string = f\"{F} * {S} + {F}\".format(self.coeffs[0], x_var, self.coeffs[1])\n", - "\n", - " if self._quadratic_1d_curve is not None:\n", - " # y = ax^2 + bx + c\n", - " equation_string = f\"{F} * {S}**2 + {F} * {S} + {F}\".format(self.coeffs[0], x_var, self.coeffs[1], x_var, self.coeffs[2])\n", - "\n", - " if self._poly3_1d_curve is not None:\n", - " # y = ax^3 + bx^2 + cx + d\n", - " equation_string = (\n", - " f\"{F} * {S}**3\"\n", - " f\" + {F} * {S}**2\"\n", - " f\" + {F} * {S}\"\n", - " f\" + {F}\".format(\n", - " self.coeffs[0], x_var,\n", - " self.coeffs[1], x_var,\n", - " self.coeffs[2], x_var,\n", - " self.coeffs[3]\n", - " )\n", - " )\n", - "\n", - " if self._linear_2d_curve is not None:\n", - " # z = ax + by + c\n", - " equation_string = f\"{F} * {S} + {F} * {S} + {F}\".format(self.coeffs[0], x_var, self.coeffs[1], y_var, self.coeffs[2])\n", - "\n", - " if self._quadratic_2d_curve is not None:\n", - " # z = ax^2 + bxy + cy^2 + dx + ey + f\n", - " equation_string = (\n", - " f\"{F} * {S}**2\"\n", - " f\" + {F} * {S} * {S}\"\n", - " f\" + {F} * {S}**2\"\n", - " f\" + {F} * {S}\"\n", - " f\" + {F} * {S}\"\n", - " f\" + {F}\".format(\n", - " self.coeffs[0], x_var,\n", - " self.coeffs[1], x_var, y_var,\n", - " self.coeffs[2], y_var,\n", - " self.coeffs[3], x_var,\n", - " self.coeffs[4], y_var,\n", - " self.coeffs[5]\n", - " )\n", - " )\n", - "\n", - " # nrwal_dict = {self.config[\"design_phases\"][0]: equation_string}\n", - " nrwal_dict = {key: equation_string}\n", - "\n", - " with open(filename, \"a\") as f:\n", - " f.write(\"\\n\")\n", - " if comments:\n", - " f.write(f\"# {comments}\\n\")\n", - " # f.write(f\"# {self.config['design_phases'][0]}\\n\")\n", - " yaml.dump(nrwal_dict, f)\n", - " f.write(f\"\\n\")\n", - " print(nrwal_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ORBIT Design Phase Cost Curves" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Monopile Substructure\n", - "\n", - "Independent variables:\n", - "- Water depth: impacts the mass of the monopile since it is fixed to the ocean floor\n", - "- Mean wind speed: impact the mass of the monopile by the load transferred from the turbine" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cost Curve Creator\n", + "\n", + "This notebook enables fitting curves to ORBIT models to create a cost function that can be\n", + "embedded in NRWAL.\n", + "A variety of curve and surface fitting options are available, and new ones can be added easily.\n", + "There are also tools for visualizing the ORBIT data and fitted curves.\n", + "\n", + "## Dependencies\n", + "\n", + "- ORBIT\n", + "- ipympl enables interactive matplotlib elements in jupyter notebooks via the `%matplotlib widget` magic command below\n", + "\n", + "## Instructions\n", + "\n", + "Follow the steps below to configure and run the notebook.\n", + "\n", + "1. Create a basic ORBIT configuration file including at least the following sections:\n", + "- site\n", + "- turbine\n", + "- plant\n", + "\n", + "2. Configure the notebook by setting the following variables in the \"Configuration\" section:\n", + "- `BASE_CONFIG`: the ORBIT config file at a given path\n", + "- `DEPTHS`: a list of water depths to use for cost curves\n", + "- `MEAN_WIND_SPEED`: a list of mean wind speed to use for cost curves\n", + "- Add any additional global parameter ranges\n", + "\n", + "3. Run the notebook to establish a first-pass fit for the ORBIT data. This will also plot the ORBIT\n", + "data and curves.\n", + "\n", + "4. Refine the curve fits by swapping the curve-fit function from the options available in\n", + "the \"Curve Fit Library\" section\n", + "\n", + "## Practical Guidance\n", + "\n", + "This notebook specifically models spatially varying costs typically related to water depth\n", + "but in some cases other variables are considered. The same methods could be used to model\n", + "the cost relationship for other variables. The general workflow is to first create a parameterized\n", + "ORBIT model and obtain the cost as a function of the variables of interest.\n", + "Then, fit a curve or surface to the data by starting with the linear options.\n", + "Plot the data and curve fits to evaluate whether the linear forms are sufficient.\n", + "If not, move to the quadratic or higher order curve fits.\n", + "\n", + "A class `CostFunction` is provided to simplify running the ORBIT parameterization, fit the\n", + "curves to the data, and visualize the results. An example is given below to instantiate the\n", + "class:\n", + "```python\n", + "cost_function = CostFunction(\n", + " config={\"design_phases\": [\"MonopileDesign\"]},\n", + " parameters={\n", + " \"site.depth\": DEPTHS,\n", + " \"site.mean_windspeed\": MEAN_WIND_SPEED,\n", + " },\n", + " results={\n", + " \"monopile_unit_cost\": lambda run: run.design_results[\"monopile\"][\"unit_cost\"],\n", + " }\n", + ")\n", + "```\n", + "\n", + "The config parameter is a dictionary containing additional configuration parameters to add to\n", + "the basic ORBIT configuration provided through the input file created in Step 1 in the instructions.\n", + "Any parameters given in the `CostFunction` config will be added to the base configuration or\n", + "overwritten if they already exist. The parameters dictionary contains the variables to be varied\n", + "in the cost function via `ORBIT.ParametricManager`, and the results dictionary sets the results\n", + "variables from ORBIT. Each of these dictionaries are passed directly to the\n", + "`ORBIT.ParametricManager` class.\n", + "\n", + "The fitted curves are saved on the `CostFunction` object and multiple types can exist at the\n", + "same time. Two versions of one type cannot be saved at the same time. To create a curve fit,\n", + "call one of the curve fit methods on the `CostFunction` instance. Then, an attribute is saved\n", + "on the instance with the curve fit type.\n", + "\n", + "Considerations:\n", + "- The `CostFunction` class supports parameterizations of at-most 2 variables.\n", + "- One instance of the `CostFunction` class can be used to fit multiple curves for a single\n", + " cost model.\n", + "- A new `CostFunction` instance should be created for each cost model.\n", + "\n", + "### Plotting API for 2D vs 3D plots\n", + "The `CostFunction` class handles 2D and 3D data seamlessly by using the x and z parameters for 2D\n", + "and adding y for 3D. The appropriate matplotlib API is used depending if the data is 2D or 3D.\n", + "From the calling script, be sure to configure the Axes that is given to `CostFunction.plot` with\n", + "the correct settings for 3D as listed in the table below.\n", + "\n", + "| Matplotlib setting | 2D | 3D |\n", + "|---------------------|----|----|\n", + "| Independent axis labels | `ax.set_xlabel()` | `ax.set_xlabel()`, `ax.set_zlabel()` |\n", + "| Dependent axis label | `ax.set_ylabel()` | `ax.set_zlabel()` |\n", + "\n", + "### Template workflow\n", + "\n", + "The following code block provides a template for creating a cost function for a model with\n", + "two independent parameters.\n", + "\n", + "```python\n", + "\n", + "# Create the CostFunction object with the ORBIT configuration for the parameterization\n", + "cost_function = CostFunction(\n", + " config={\n", + " \"design_phases\": [\"Design\"],\n", + " },\n", + " parameters={\n", + " \"site.depth\": DEPTHS,\n", + " \"site.mean_windspeed\": MEAN_WIND_SPEED,\n", + " },\n", + " results={\n", + " \"system_cost\": lambda run: run.design_results[\"system\"][\"system_cost\"],\n", + " }\n", + ")\n", + "\n", + "# Run ORBIT via ORBIT.ParametricManager\n", + "cost_function.run()\n", + "\n", + "# Fit two curves (surfaces since there are two independent parameters) to the data.\n", + "# After running the following two commands, the CostFunction object will have two related\n", + "# attributes that store the curve fits.\n", + "cost_function.fit_curve(\"linear_2d\")\n", + "cost_function.fit_curve(\"quadratic_2d\")\n", + "\n", + "# Plot the data and curves\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "ax.set_title(\"Depth vs mean wind speed\")\n", + "ax.set_xlabel(\"Depth (m)\")\n", + "ax.set_ylabel(\"Mean wind speed (m/s)\")\n", + "ax.set_zlabel(\"Cost ($)\")\n", + "cost_function.plot(ax, plot_data=True)\n", + "cost_function.plot(ax, plot_curves=[\"linear_1d\", \"quadratic_1d\"]) # These curves must have been generated first\n", + "# alternatively, the two lines above could be combined into a single line:\n", + "# cost_function.plot(ax, plot_data=True, plot_curves=[\"linear_1d\", \"quadratic_1d\"])\n", + "\n", + "# Export the curve function to a NRWAL-compatible file\n", + "cost_function.export(\"design.yaml\", \"design_system\")\n", + "```\n", + "\n", + "### Adding a new curve type\n", + "\n", + "There are a number of curve fit options in the `CostFunction` class, and more can be added by\n", + "creating a new method and connecting it in some key places in the class.\n", + "First, create a new method on the `CostFunction` class that follows the naming convention of\n", + "`{curve_type}_{dimension}` where `curve_type` is the name of the type of function like\n", + "\"exponential\" or \"linear\" and `dimension` is the number of independent variables the curve.\n", + "The function should return the fitted curve evaluated at the data points given to fit the curve.\n", + "A generic function signature is given below:\n", + "```python\n", + "class CostFunction:\n", + "\n", + " def curvetype_dimension(self):\n", + "\n", + " # Such as:\n", + " def linear_1d(self):\n", + "```\n", + "\n", + "To fit a curve to the data for one independent variable, it is recommended to use the\n", + "`scipy.optimize.curve_fit` function via the `Curves` class.\n", + "In general, a one-dimensional curve fit function will follow the form given below.\n", + "By setting the curve fit function `f`, you define the shape of the curve and set\n", + "the order of the coefficients in `self.coeffs` since they are returned in the order they are\n", + "given in the function signature.\n", + "The `Curves.polynomal_eval` function is available to easily evaluate polynomial curves, but other\n", + "curve-types can be evaluated by simply plugging in the data points (`self.x`) to the fitted\n", + "function.\n", + "\n", + "```python\n", + "# Define a function for a prototype curve; this is where you define the shape of the curve\n", + "def f(x, a, b):\n", + " return a * x + b\n", + "\n", + "# Call the scipy.optimize.curve_fit function and get the coefficients as a Numpy array\n", + "# Note that `self.x` and `self.z` are given since the CostFunction class adds a y\n", + "# only when there are more independent variables.\n", + "self.coeffs = Curves.fit(f, self.x, self.z)\n", + "\n", + "# Evaluate the curve at the data points (self.x)\n", + "self._linear_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)\n", + "```\n", + "\n", + "A two-dimensional curve (surface) fit will typically follow a similar process, as should below.\n", + "For these types, it is recommended to use the `numpy.linalg.lstsq` function.\n", + "First, reshape the data into a new array with each element containing the three-dimensional\n", + "data points.\n", + "Then, stack the data into a column matrix in the form of the equation that you're implementing.\n", + "See the comments in the code block for more information.\n", + "Evaluate the curve at the data points (`self.x`, `self.y`) by stating the form of the curve\n", + "with the coefficients from the curve fit.\n", + "\n", + "```python\n", + " # Reshape the data into a new array with each element containing the three-dimensional data points\n", + " data_to_fit = np.array(list(zip(self.x, self.y, self.z)))\n", + "\n", + " # Stack the data into a column matrix in the form of the equation that you're implementing.\n", + " # Here, the equation is z = ax + by + c and data_to_fit[:,0] are the x values,\n", + " # data_to_fit[:,1] are the y values. The third column is all ones to account for the constant\n", + " # term.\n", + " A = np.c_[\n", + " data_to_fit[:,0],\n", + " data_to_fit[:,1],\n", + " np.ones(data_to_fit.shape[0])\n", + " ]\n", + "\n", + " # Fit the curve to the data; the data is the cost and these are always `self.z` which is data_to_fit[:,2]\n", + " self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2])\n", + "\n", + " # Evaluate it on the same points as the input data\n", + " self._linear_2d_curve = self.coeffs[0]*self.x + self.coeffs[1]*self.y + self.coeffs[2]\n", + "```\n", + "\n", + "Finally, save the coefficients to `self.coeffs`, save the evaluated curve to\n", + "`self._{curve_type}_{dimension}_curve`, and add the corresponding if-statements\n", + "in `CostFunction.plot` and `CostFunction.export`." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "ORBIT library intialized at '/Users/rmudafor/Development/orbit/library'\n", - "{'substructure_17MW': '-569653.7 * depth**2 + 12505.1 * depth * mean_windspeed + 545620.3 * mean_windspeed**2 + 6917.7 * depth + 235.1 * mean_windspeed + -15478.7'}\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget\n", + "\n", + "from copy import deepcopy\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy import stats, optimize, linalg\n", + "import yaml\n", + "\n", + "from ORBIT import (\n", + " ParametricManager,\n", + " load_config,\n", + ")\n", + "\n", + "import matplotlib as mpl\n", + "mpl.rcParams[\"figure.autolayout\"] = True" + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d67df42313104cf1a234f208e058429d", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", - "text/html": [ - "\n", - "
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\n", - " Figure\n", - "
\n", - " \n", - "
\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration\n", + "\n", + "Replace any of these throughout the notebook to customize a cost model parameterization." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cost_function = CostFunction(\n", - " config={\"design_phases\": [\"MonopileDesign\"]},\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " \"site.mean_windspeed\": MEAN_WIND_SPEED,\n", - " },\n", - " results={\n", - " \"monopile_unit_cost\": lambda run: run.design_results[\"monopile\"][\"unit_cost\"],\n", - " # \"transition_piece_unit_cost\": lambda run: run.design_results[\"transition_piece\"][\"unit_cost\"],\n", - " }\n", - ")\n", - "cost_function.run()\n", - "\n", - "cost_function.linear_2d()\n", - "cost_function.quadratic_2d()\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(projection='3d')\n", - "ax.set_title(\"Monopile Substructure\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Mean wind speed (m/s)\")\n", - "ax.set_zlabel(\"Cost ($)\")\n", - "cost_function.plot(ax, plot_data=True)\n", - "cost_function.plot(ax, plot_curves=[\"linear_2d\", \"quadratic_2d\"])\n", - "ax.legend()\n", - "\n", - "cost_function.export(\"substructure.yaml\", \"substructure_17MW\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Semi-Submersible Substructure\n", - "\n", - "Since the semisubmersible is a floating structure, the water depth does not impact the mass\n", - "of the structure.\n", - "The mean wind speed does impact the mass of the structure by the load transferred from the\n", - "turbine, but this is not included in the design phase cost model directly.\n", - "The plot here shows that the cost is constant with respect to the water depth." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "BASE_CONFIG = load_config(\"nrwal.yaml\")\n", + "\n", + "DEPTHS = [i for i in range(5, 60, 5)] # Ocean depth in meters\n", + "MEAN_WIND_SPEED = [i for i in range(2, 20, 2)] # Mean wind speed in m/s" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4b13254c2f314312b9419d72d1c138c8", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "orbit_to_nrwal_params = {\n", + " \"site.depth\": \"depth\",\n", + " \"site.mean_windspeed\": \"mean_windspeed\", # Not in NRWAL\n", + " \"site.distance_to_landfall\": \"dist_s_to_l\",\n", + " \"mooring_system_design.draft_depth\": \"draft_depth\", # Not in NRWAL\n", + " \"array_system_design.touchdown_distance\": \"touchdown_distance\", # Not in NRWAL\n", + " \"array_system_design.floating_cable_depth\": \"floating_cable_depth\", # Not in NRWAL\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Curve Fit Library" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class Curves():\n", + " \"\"\"\n", + " This class contains static methods for fitting data to various curve types.\n", + " Though they could exist outside of a class, consolidating them into a consistent\n", + " namespace allows for a simpler API throughout the script.\n", + " \"\"\"\n", + "\n", + " @staticmethod\n", + " def polynomial_eval(coeffs, data_points):\n", + " \"\"\"\n", + " This method evaluates a curve defined by a polynomial equation given a set of\n", + " coefficients and data points.\n", + "\n", + " Args:\n", + " coeffs (list): A list of coefficients for the curve. The order of the\n", + " coefficients should be from highest to lowest power.\n", + " data_points (list): A list of data points at which to evaluate the curve.\n", + "\n", + " Returns:\n", + " np.array: The curve evaluated at the given data points.\n", + " \"\"\"\n", + " curve = np.zeros_like(data_points)\n", + " for i, dp in enumerate(data_points):\n", + "\n", + " # This loop sums the terms of the polynomial\n", + " for j in range(len(coeffs)):\n", + " curve[i] += coeffs[j] * (dp ** (len(coeffs) - 1 - j))\n", + " return curve\n", + "\n", + " @staticmethod\n", + " def fit(func, x, y, fit_check=False):\n", + " if x is pd.Series:\n", + " x = x.to_numpy(dtype=np.float64)\n", + " elif x is np.array:\n", + " x = x.astype(np.float64)\n", + " if y is pd.Series:\n", + " y = y.to_numpy(dtype=np.float64)\n", + " elif y is np.array:\n", + " y = y.astype(np.float64)\n", + "\n", + " popt, pcov, nfodict, mesg, ier = optimize.curve_fit(func, x, y, full_output=True)\n", + "\n", + " if fit_check:\n", + " print(f\"mesg: {mesg}\")\n", + " print(f\"ier: {ier}\")\n", + " print(f\"Coefficients: {popt}\")\n", + " # print(f\"R-squared: {rvalue**2:.6f}\")\n", + " \n", + " return popt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class CostFunction():\n", + " \"\"\"\n", + " This class is used to create the ORBIT parameterization, fit a curve, plot the curve, and\n", + " export the function to NRWAL format. Parameterizations are limited to up to two independent\n", + " variables.\n", + " \"\"\"\n", + " def __init__(self, config: dict, parameters: dict, results: dict):\n", + " \"\"\"\n", + " On initialization, the config, parameters, and results dictionaries are prepared for\n", + " use in ORBIT.ParametricManager. Additionally, the independent variables are extracted\n", + " into x and y (for two-variable parameterizations) and z is extracted as the dependent\n", + " variable. Whether the cost function is 3D or 2D is determined by the length of the\n", + " parameters variable.\n", + "\n", + " Args:\n", + " config (str): Configuration settings to added to the BASE_CONFIG or overwrite\n", + " in the BASE_CONFIG. This must include the `design_phases` config.\n", + " parameters (dict): Parameters to use with ORBIT.ParametricManager; maximum of two\n", + " parameters are supported.\n", + " results (dict): Results to use with ORBIT.ParametricManager; this must include only\n", + " one variable.\n", + " \"\"\"\n", + " self.is_3d = False\n", + "\n", + " # Other attributes\n", + " # self.parametric\n", + " # self.x\n", + " # self.y\n", + " # self.z\n", + " # self.x_variable\n", + " # self.y_variable\n", + " self._linear_1d_curve = None\n", + " self._quadratic_1d_curve = None\n", + " self._poly3_1d_curve = None\n", + " self._linear_2d_curve = None\n", + " self._quadratic_2d_curve = None\n", + "\n", + " # Start with a copy of the global BASE_CONFIG and update it with the configuration\n", + " # given to this class\n", + " self.config = deepcopy(BASE_CONFIG)\n", + " self.config.update(config)\n", + "\n", + " self.parameters = deepcopy(parameters)\n", + " if len(self.parameters) > 2:\n", + " raise ValueError(\"This class is limited to parameterizations with two variables.\")\n", + "\n", + " # Puts the parameters and results settings into variables for use in parsing the ORBIT\n", + " # results and postprocessing the data\n", + " self.parameters = deepcopy(self.parameters)\n", + " _vars = list(self.parameters.keys())\n", + " self.x_variable = _vars.pop(0) # NOTE: This assumes the first parameter is site.depth; it's not critical to functionality but good to keep in mind\n", + " if len(_vars) == 1:\n", + " self.is_3d = True\n", + " self.y_variable = _vars.pop()\n", + " self.z_variable = list(results.keys())[0]\n", + "\n", + " self.results = deepcopy(results)\n", + " if len(results) != 1:\n", + " raise ValueError(\"This class is limited to results with one variable\")\n", + "\n", + " def run(self):\n", + " self.parametric = ParametricManager(self.config, self.parameters, self.results, product=True)\n", + " self.parametric.run()\n", + "\n", + " self.x = self.parametric.results[self.x_variable]\n", + " if self.is_3d:\n", + " self.y = self.parametric.results[self.y_variable]\n", + " self.z = self.parametric.results[self.z_variable]\n", + "\n", + "\n", + " ### --------- Curve fit functions --------- ###\n", + "\n", + " def linear_1d(self):\n", + " def f(x, a, b):\n", + " return a * x + b\n", + " self.coeffs = Curves.fit(f, self.x, self.z)\n", + " self._linear_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)\n", + "\n", + " def quadratic_1d(self):\n", + " def f(x, a, b, c):\n", + " return a * x**2 + b * x + c\n", + " self.coeffs = Curves.fit(f, self.x, self.z)\n", + " self._quadratic_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)\n", + "\n", + " def poly3_1d(self):\n", + " def f(x, a, b, c, d):\n", + " return a * x**3 + b * x**2 + c * x + d\n", + " self.coeffs = Curves.fit(f, self.x, self.z)\n", + " self._poly3_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)\n", + "\n", + " # def logarithmic_1d(self):\n", + " # pass\n", + "\n", + " # def exponential_1d(self):\n", + " # pass\n", + "\n", + " def linear_2d(self):\n", + " data_to_fit = np.array(list(zip(self.x, self.y, self.z)))\n", + "\n", + " # Best-fit linear plane\n", + " A = np.c_[\n", + " data_to_fit[:,0],\n", + " data_to_fit[:,1],\n", + " np.ones(data_to_fit.shape[0])\n", + " ]\n", + " self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2]) # coefficients\n", + "\n", + " # Evaluate it on the same points as the input data\n", + " self._linear_2d_curve = self.coeffs[0]*self.x + self.coeffs[1]*self.y + self.coeffs[2]\n", + "\n", + " def quadratic_2d(self):\n", + " data_to_fit = np.array(list(zip(self.x, self.y, self.z)))\n", + "\n", + " # best-fit quadratic curve\n", + " A = np.c_[\n", + " np.ones(data_to_fit.shape[0]),\n", + " data_to_fit[:,:2],\n", + " np.prod(data_to_fit[:,:2], axis=1),\n", + " data_to_fit[:,:2]**2\n", + " ]\n", + " self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2])\n", + "\n", + " # Evaluate it on the same points as the input data\n", + " # This dot product is equivalent to the sum of the terms of the polynomial;\n", + " # np.c_[] is used to concatenate the arrays into the correct form for the dot product\n", + " # and C is the coefficients of the polynomial\n", + " self._quadratic_2d_curve = np.dot(\n", + " np.c_[\n", + " np.ones(self.x.shape),\n", + " self.x,\n", + " self.y,\n", + " self.x*self.y,\n", + " self.x**2,\n", + " self.y**2\n", + " ],\n", + " self.coeffs\n", + " ).reshape(self.x.shape)\n", + "\n", + "\n", + " ### --------- Plotting functions --------- ###\n", + "\n", + " def plot(\n", + " self,\n", + " ax,\n", + " plot_data: bool = False,\n", + " plot_curves: list[str] = []\n", + " ):\n", + " if plot_data:\n", + " if self.is_3d:\n", + " ax.scatter(self.x, self.y, zs=self.z, zdir='z', label=\"Data\")\n", + " else:\n", + " ax.scatter(self.x, self.z, label=\"Data\")\n", + "\n", + " for curve in plot_curves:\n", + "\n", + " if curve == \"linear_1d\":\n", + " ax.plot(self.x, self._linear_1d_curve, label=\"Linear Fit\")\n", + "\n", + " if curve == \"quadratic_1d\":\n", + " ax.plot(self.x, self._quadratic_1d_curve, label=\"Quadratic Fit\")\n", + "\n", + " if curve == \"poly3_1d\":\n", + " ax.plot(self.x, self._poly3_1d_curve, label=\"Degree 3 Polynomial Fit\")\n", + "\n", + " if curve == \"linear_2d\":\n", + " ax.plot_surface(\n", + " np.reshape(self.x, (len(DEPTHS), -1)),\n", + " np.reshape(self.y, (len(DEPTHS), -1)),\n", + " np.reshape(self._linear_2d_curve, (len(DEPTHS), -1)),\n", + " alpha=0.3,\n", + " label=\"Linear Fit\"\n", + " )\n", + "\n", + " if curve == \"quadratic_2d\":\n", + " ax.plot_surface(\n", + " np.reshape(self.x, (len(DEPTHS), -1)),\n", + " np.reshape(self.y, (len(DEPTHS), -1)),\n", + " np.reshape(self._quadratic_2d_curve, (len(DEPTHS), -1)),\n", + " alpha=0.3,\n", + " label=\"Quadratic Fit\"\n", + " )\n", + "\n", + "\n", + " ### --------- Export functions --------- ###\n", + "\n", + " def export(self, filename: str, key: str, comments: str = \"\"):\n", + " \"\"\"\n", + " This function writes the curve equation to a file for use in NRWAL.\n", + "\n", + " Args:\n", + " filename (str): The file to write the curve equation to. If the file exists, the\n", + " equation is appended to the end of the file.\n", + " key (str): The key to use in the NRWAL file for the curve equation. In the key-value\n", + " pair, this argument is the key and the value is the equation string.\n", + " \"\"\"\n", + "\n", + " x_var = orbit_to_nrwal_params[self.x_variable]\n", + " if self.is_3d:\n", + " y_var = orbit_to_nrwal_params[self.y_variable]\n", + "\n", + " F = \"{:.1f}\"\n", + " S = \"{:s}\"\n", + " if self._linear_1d_curve is not None:\n", + " # y = ax + b\n", + " equation_string = f\"{F} * {S} + {F}\".format(self.coeffs[0], x_var, self.coeffs[1])\n", + "\n", + " if self._quadratic_1d_curve is not None:\n", + " # y = ax^2 + bx + c\n", + " equation_string = f\"{F} * {S}**2 + {F} * {S} + {F}\".format(self.coeffs[0], x_var, self.coeffs[1], x_var, self.coeffs[2])\n", + "\n", + " if self._poly3_1d_curve is not None:\n", + " # y = ax^3 + bx^2 + cx + d\n", + " equation_string = (\n", + " f\"{F} * {S}**3\"\n", + " f\" + {F} * {S}**2\"\n", + " f\" + {F} * {S}\"\n", + " f\" + {F}\".format(\n", + " self.coeffs[0], x_var,\n", + " self.coeffs[1], x_var,\n", + " self.coeffs[2], x_var,\n", + " self.coeffs[3]\n", + " )\n", + " )\n", + "\n", + " if self._linear_2d_curve is not None:\n", + " # z = ax + by + c\n", + " equation_string = f\"{F} * {S} + {F} * {S} + {F}\".format(self.coeffs[0], x_var, self.coeffs[1], y_var, self.coeffs[2])\n", + "\n", + " if self._quadratic_2d_curve is not None:\n", + " # z = ax^2 + bxy + cy^2 + dx + ey + f\n", + " equation_string = (\n", + " f\"{F} * {S}**2\"\n", + " f\" + {F} * {S} * {S}\"\n", + " f\" + {F} * {S}**2\"\n", + " f\" + {F} * {S}\"\n", + " f\" + {F} * {S}\"\n", + " f\" + {F}\".format(\n", + " self.coeffs[0], x_var,\n", + " self.coeffs[1], x_var, y_var,\n", + " self.coeffs[2], y_var,\n", + " self.coeffs[3], x_var,\n", + " self.coeffs[4], y_var,\n", + " self.coeffs[5]\n", + " )\n", + " )\n", + "\n", + " # nrwal_dict = {self.config[\"design_phases\"][0]: equation_string}\n", + " nrwal_dict = {key: equation_string}\n", + "\n", + " with open(filename, \"a\") as f:\n", + " f.write(\"\\n\")\n", + " if comments:\n", + " f.write(f\"# {comments}\\n\")\n", + " # f.write(f\"# {self.config['design_phases'][0]}\\n\")\n", + " yaml.dump(nrwal_dict, f)\n", + " f.write(f\"\\n\")\n", + " print(nrwal_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ORBIT Design Phase Cost Curves" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Monopile Substructure\n", + "\n", + "Independent variables:\n", + "- Water depth: impacts the mass of the monopile since it is fixed to the ocean floor\n", + "- Mean wind speed: impact the mass of the monopile by the load transferred from the turbine" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ORBIT library intialized at '/Users/rmudafor/Development/orbit/library'\n", + "{'substructure_17MW': '-569653.7 * depth**2 + 12505.1 * depth * mean_windspeed + 545620.3 * mean_windspeed**2 + 6917.7 * depth + 235.1 * mean_windspeed + -15478.7'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d67df42313104cf1a234f208e058429d", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "text/html": [ + "\n", + "
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\n", + " Figure\n", + "
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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous \u2026" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + "source": [ + "cost_function = CostFunction(\n", + " config={\"design_phases\": [\"MonopileDesign\"]},\n", + " parameters={\n", + " \"site.depth\": DEPTHS,\n", + " \"site.mean_windspeed\": MEAN_WIND_SPEED,\n", + " },\n", + " results={\n", + " \"monopile_unit_cost\": lambda run: run.design_results[\"monopile\"][\"unit_cost\"],\n", + " # \"transition_piece_unit_cost\": lambda run: run.design_results[\"transition_piece\"][\"unit_cost\"],\n", + " }\n", + ")\n", + "cost_function.run()\n", + "\n", + "cost_function.linear_2d()\n", + "cost_function.quadratic_2d()\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(projection='3d')\n", + "ax.set_title(\"Monopile Substructure\")\n", + "ax.set_xlabel(\"Depth (m)\")\n", + "ax.set_ylabel(\"Mean wind speed (m/s)\")\n", + "ax.set_zlabel(\"Cost ($)\")\n", + "cost_function.plot(ax, plot_data=True)\n", + "cost_function.plot(ax, plot_curves=[\"linear_2d\", \"quadratic_2d\"])\n", + "ax.legend()\n", + "\n", + "cost_function.export(\"substructure.yaml\", \"substructure_17MW\")" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cost_function = CostFunction(\n", - " config={\"design_phases\": [\"SemiSubmersibleDesign\"]},\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " },\n", - " results={\n", - " \"substructure_unit_cost\": lambda run: run.design_results[\"substructure\"][\"unit_cost\"],\n", - " }\n", - ")\n", - "cost_function.run()\n", - "\n", - "cost_function.linear_1d()\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "ax.set_title(\"Semisubmersible Substructure\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Cost ($)\")\n", - "cost_function.plot(ax, plot_data=True)\n", - "cost_function.plot(ax, plot_curves=[\"linear_1d\"])\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mooring System\n", - "\n", - "This block creates a cost model for each type of mooring system.\n", - "For all types, the line length is a function of water depth.\n", - "For TLP systems, line length is the difference between the water depth and the draft.\n", - "For SemiTaut systems, line length is the sum of rope length and chain length.\n", - "Rope length is defined from a fixed relationship for depth and rope lengths.\n", - "Chain length is also defined from a fixed relationship for depth and chain diameter.\n", - "While the semi-taut system line length is dependent on rope length and chain length, the parameters\n", - "are fixed and depend on water depth so they are not included in this parameterization." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'catenary': '199913.5 * depth + 34961743.3'}\n", - "{'tlp': '156672.0 * depth + -156672.0 * draft_depth + 27496949.2'}\n", - "{'semitaut': '227446.8 * depth + 32803637.2'}\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Semi-Submersible Substructure\n", + "\n", + "Since the semisubmersible is a floating structure, the water depth does not impact the mass\n", + "of the structure.\n", + "The mean wind speed does impact the mass of the structure by the load transferred from the\n", + "turbine, but this is not included in the design phase cost model directly.\n", + "The plot here shows that the cost is constant with respect to the water depth." + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9527723938a24d85b577e15df912cbfb", - "version_major": 2, - "version_minor": 0 - }, - 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+ "text/html": [ + "\n", + "
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"cost_function.plot(ax, plot_curves=[\"linear_1d\"])\n", + "ax.legend()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "design_phase = \"MooringSystemDesign\"\n", - "results = {\n", - " \"mooring_system_system_cost\": lambda run: run.design_results[\"mooring_system\"][\"system_cost\"],\n", - "}\n", - "\n", - "# Catenary mooring system\n", - "cost_catenary = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " \"mooring_system_design\": {\"mooring_type\": \"Catenary\"}\n", - " },\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " },\n", - " results=results\n", - ")\n", - "cost_catenary.run()\n", - "\n", - "# Tension Leg Platform (TLP) mooring system\n", - "cost_tlp = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " \"mooring_system_design\": {\"mooring_type\": \"TLP\"}\n", - " },\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " \"mooring_system_design.draft_depth\": [i for i in range(5, 100, 5)] # Draft depth 5-100 meters\n", - " },\n", - " results=results\n", - ")\n", - "cost_tlp.run()\n", - "\n", - "# Semi-taut mooring system\n", - "cost_semitaut = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " \"mooring_system_design\": {\"mooring_type\": \"SemiTaut\"}\n", - " },\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " },\n", - " results=results\n", - ")\n", - "cost_semitaut.run()\n", - "\n", - "## Fit the data to a curve\n", - "cost_catenary.linear_1d()\n", - "cost_tlp.linear_2d()\n", - "cost_semitaut.linear_1d()\n", - "\n", - "## Plot the ORBIT data and curve fits\n", - "fig = plt.figure()\n", - "\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "ax.set_title(\"Catenary\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Cost ($)\")\n", - "cost_catenary.plot(ax, plot_data=True)\n", - "cost_catenary.plot(ax, plot_curves=[\"linear_1d\"])\n", - "\n", - "ax = fig.add_subplot(2, 2, 2, projection='3d')\n", - "ax.set_title(\"TLP\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Draft depth (m)\")\n", - "ax.set_zlabel(\"Cost ($)\")\n", - "cost_tlp.plot(ax, plot_data=True)\n", - "cost_tlp.plot(ax, plot_curves=[\"linear_2d\"])\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "ax.set_title(\"Semi-Taut\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Cost ($)\")\n", - "cost_semitaut.plot(ax, plot_data=True)\n", - "cost_semitaut.plot(ax, plot_curves=[\"linear_1d\"])\n", - "\n", - "cost_catenary.export(\"mooring_system.yaml\", \"catenary\")\n", - "cost_tlp.export(\"mooring_system.yaml\", \"tlp\")\n", - "cost_semitaut.export(\"mooring_system.yaml\", \"semitaut\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Array System\n", - "\n", - "The array system cost is entirely dependent on the cable length.\n", - "The cable length is a function of some fixed plant parameters and the following spatially\n", - "dependent parameters:\n", - "- water depth\n", - "- touchdown distance\n", - "- floating cable depth" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "daeda722d6574a2198151c690f55f0cc", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mooring System\n", + "\n", + "This block creates a cost model for each type of mooring system.\n", + "For all types, the line length is a function of water depth.\n", + "For TLP systems, line length is the difference between the water depth and the draft.\n", + "For SemiTaut systems, line length is the sum of rope length and chain length.\n", + "Rope length is defined from a fixed relationship for depth and rope lengths.\n", + "Chain length is also defined from a fixed relationship for depth and chain diameter.\n", + "While the semi-taut system line length is dependent on rope length and chain length, the parameters\n", + "are fixed and depend on water depth so they are not included in this parameterization." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'catenary': '199913.5 * depth + 34961743.3'}\n", + "{'tlp': '156672.0 * depth + -156672.0 * draft_depth + 27496949.2'}\n", + "{'semitaut': '227446.8 * depth + 32803637.2'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9527723938a24d85b577e15df912cbfb", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "text/html": [ + "\n", + "
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}, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# First plot cost as a function of depth, touchdown_distance, and floating_cable_depth to get a\n", - "# sense for the 1d relationships\n", - "\n", - "design_phase = \"ArraySystemDesign\"\n", - "results = {\n", - " \"array_system_system_cost\": lambda run: run.design_results[\"array_system\"][\"system_cost\"],\n", - "}\n", - "\n", - "# Water depth\n", - "cost_depth = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " },\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " },\n", - " results=results\n", - ")\n", - "cost_depth.run()\n", - "\n", - "# Touchdown distance\n", - "cost_touchdown_distance = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " },\n", - " parameters={\n", - " \"array_system_design.touchdown_distance\": [i for i in range(0, 100, 10)],\n", - " },\n", - " results=results\n", - ")\n", - "cost_touchdown_distance.run()\n", - "\n", - "# Floating cable depth\n", - "cost_cable_depth = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " },\n", - " parameters={\n", - " \"array_system_design.floating_cable_depth\": DEPTHS,\n", - " },\n", - " results=results\n", - ")\n", - "cost_cable_depth.run()\n", - "\n", - "cost_depth.linear_1d()\n", - "cost_touchdown_distance.quadratic_1d()\n", - "cost_cable_depth.linear_1d()\n", - "cost_cable_depth.quadratic_1d()\n", - "cost_cable_depth.poly3_1d()\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "ax.set_title(\"Depth\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Cost ($)\")\n", - "cost_depth.plot(ax, plot_data=True)\n", - "cost_depth.plot(ax, plot_curves=[\"linear_1d\"])\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "ax.set_title(\"Touchdown Distance\")\n", - "ax.set_xlabel(\"Touchdown Distance (m)\")\n", - "ax.set_ylabel(\"Cost ($)\")\n", - "cost_touchdown_distance.plot(ax, plot_data=True)\n", - "cost_touchdown_distance.plot(ax, plot_curves=[\"quadratic_1d\"])\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "ax.set_title(\"Floating Depth\")\n", - "ax.set_xlabel(\"Floating Depth (m)\")\n", - "ax.set_ylabel(\"Cost ($)\")\n", - "cost_cable_depth.plot(ax, plot_data=True)\n", - "cost_cable_depth.plot(ax, plot_curves=[\"linear_1d\", \"quadratic_1d\", \"poly3_1d\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "RuntimeWarning: /Users/rmudafor/Development/orbit/ORBIT/phases/design/_cables.py:386\n", - "The iteration is not making good progress, as measured by the \n", - " improvement from the last ten iterations.RuntimeWarning: /Users/rmudafor/Development/orbit/ORBIT/phases/design/_cables.py:386\n", - "The iteration is not making good progress, as measured by the \n", - " improvement from the last ten iterations.RuntimeWarning: /Users/rmudafor/Development/orbit/ORBIT/phases/design/_cables.py:372\n", - "overflow encountered in cosh" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Array System\n", + "\n", + "The array system cost is entirely dependent on the cable length.\n", + "The cable length is a function of some fixed plant parameters and the following spatially\n", + "dependent parameters:\n", + "- water depth\n", + "- touchdown distance\n", + "- floating cable depth" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Catenary calculation failed. Reverting to simple vertical profile.\n", - "Warning: Catenary calculation failed. Reverting to simple vertical profile.\n" - ] - } - ], - "source": [ - "# Then create functions of two variables.\n", - "# NOTE: The parameterization and plotting are split in two blocks since the ORBIT model takes\n", - "# some time to run.\n", - "\n", - "design_phase = \"ArraySystemDesign\"\n", - "results = {\n", - " \"array_system_system_cost\": lambda run: run.design_results[\"array_system\"][\"system_cost\"],\n", - "}\n", - "\n", - "# Water depth vs touchdown distance\n", - "cost_depth_touchdown_distance = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " },\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " \"array_system_design.touchdown_distance\": [i for i in range(0, 100, 10)],\n", - " },\n", - " results=results\n", - ")\n", - "cost_depth_touchdown_distance.run()\n", - "\n", - "# Water depth vs cable depth\n", - "cost_depth_cabledepth = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " },\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " \"array_system_design.floating_cable_depth\": DEPTHS,\n", - " },\n", - " results=results\n", - ")\n", - "cost_depth_cabledepth.run()\n", - "\n", - "# Touchdown distance vs cable depth\n", - "cost_touchdown_cabledepth = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " },\n", - " parameters={\n", - " \"array_system_design.floating_cable_depth\": DEPTHS,\n", - " \"array_system_design.touchdown_distance\": [i for i in range(0, 100, 10)],\n", - " },\n", - " results=results\n", - ")\n", - "cost_touchdown_cabledepth.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "daeda722d6574a2198151c690f55f0cc", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "text/html": [ + "\n", + "
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\"array_system_design.touchdown_distance\": [i for i in range(0, 100, 10)],\n", + " },\n", + " results=results\n", + ")\n", + "cost_touchdown_distance.run()\n", + "\n", + "# Floating cable depth\n", + "cost_cable_depth = CostFunction(\n", + " config={\n", + " \"design_phases\": [design_phase],\n", + " },\n", + " parameters={\n", + " \"array_system_design.floating_cable_depth\": DEPTHS,\n", + " },\n", + " results=results\n", + ")\n", + "cost_cable_depth.run()\n", + "\n", + "cost_depth.linear_1d()\n", + "cost_touchdown_distance.quadratic_1d()\n", + "cost_cable_depth.linear_1d()\n", + "cost_cable_depth.quadratic_1d()\n", + "cost_cable_depth.poly3_1d()\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(2, 2, 1)\n", + "ax.set_title(\"Depth\")\n", + "ax.set_xlabel(\"Depth (m)\")\n", + "ax.set_ylabel(\"Cost ($)\")\n", + "cost_depth.plot(ax, plot_data=True)\n", + "cost_depth.plot(ax, plot_curves=[\"linear_1d\"])\n", + "\n", + "ax = fig.add_subplot(2, 2, 2)\n", + "ax.set_title(\"Touchdown Distance\")\n", + "ax.set_xlabel(\"Touchdown Distance (m)\")\n", + "ax.set_ylabel(\"Cost ($)\")\n", + "cost_touchdown_distance.plot(ax, plot_data=True)\n", + "cost_touchdown_distance.plot(ax, plot_curves=[\"quadratic_1d\"])\n", + "\n", + "ax = fig.add_subplot(2, 2, 3)\n", + "ax.set_title(\"Floating Depth\")\n", + "ax.set_xlabel(\"Floating Depth (m)\")\n", + "ax.set_ylabel(\"Cost ($)\")\n", + "cost_cable_depth.plot(ax, plot_data=True)\n", + "cost_cable_depth.plot(ax, plot_curves=[\"linear_1d\", \"quadratic_1d\", \"poly3_1d\"])" + ] + }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a228ee4b8be142bdb82efb0c18561d66", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RuntimeWarning: /Users/rmudafor/Development/orbit/ORBIT/phases/design/_cables.py:386\n", + "The iteration is not making good progress, as measured by the \n", + " improvement from the last ten iterations.RuntimeWarning: /Users/rmudafor/Development/orbit/ORBIT/phases/design/_cables.py:386\n", + "The iteration is not making good progress, as measured by the \n", + " improvement from the last ten iterations.RuntimeWarning: /Users/rmudafor/Development/orbit/ORBIT/phases/design/_cables.py:372\n", + "overflow encountered in cosh" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Catenary calculation failed. Reverting to simple vertical profile.\n", + "Warning: Catenary calculation failed. Reverting to simple vertical profile.\n" + ] + } ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + "source": [ + "# Then create functions of two variables.\n", + "# NOTE: The parameterization and plotting are split in two blocks since the ORBIT model takes\n", + "# some time to run.\n", + "\n", + "design_phase = \"ArraySystemDesign\"\n", + "results = {\n", + " \"array_system_system_cost\": lambda run: run.design_results[\"array_system\"][\"system_cost\"],\n", + "}\n", + "\n", + "# Water depth vs touchdown distance\n", + "cost_depth_touchdown_distance = CostFunction(\n", + " config={\n", + " \"design_phases\": [design_phase],\n", + " },\n", + " parameters={\n", + " \"site.depth\": DEPTHS,\n", + " \"array_system_design.touchdown_distance\": [i for i in range(0, 100, 10)],\n", + " },\n", + " results=results\n", + ")\n", + "cost_depth_touchdown_distance.run()\n", + "\n", + "# Water depth vs cable depth\n", + "cost_depth_cabledepth = CostFunction(\n", + " config={\n", + " \"design_phases\": [design_phase],\n", + " },\n", + " parameters={\n", + " \"site.depth\": DEPTHS,\n", + " \"array_system_design.floating_cable_depth\": DEPTHS,\n", + " },\n", + " results=results\n", + ")\n", + "cost_depth_cabledepth.run()\n", + "\n", + "# Touchdown distance vs cable depth\n", + "cost_touchdown_cabledepth = CostFunction(\n", + " config={\n", + " \"design_phases\": [design_phase],\n", + " },\n", + " parameters={\n", + " \"array_system_design.floating_cable_depth\": DEPTHS,\n", + " \"array_system_design.touchdown_distance\": [i for i in range(0, 100, 10)],\n", + " },\n", + " results=results\n", + ")\n", + "cost_touchdown_cabledepth.run()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# NOTE: The parameterization and plotting are split in two blocks since the ORBIT model takes\n", - "# some time to run.\n", - "\n", - "cost_depth_touchdown_distance.quadratic_2d()\n", - "cost_depth_cabledepth.quadratic_2d()\n", - "cost_touchdown_cabledepth.quadratic_2d()\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(2, 2, 1, projection='3d')\n", - "ax.set_title(\"Depth vs Touchdown Distance\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Touchdown Distance (m)\")\n", - "ax.set_zlabel(\"Cost ($)\")\n", - "cost_depth_touchdown_distance.plot(ax, plot_data=True)\n", - "cost_depth_touchdown_distance.plot(ax, plot_curves=[\"quadratic_2d\"])\n", - "\n", - "ax = fig.add_subplot(2, 2, 2, projection='3d')\n", - "ax.set_title(\"Depth v Cable Depth\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Cable Depth (m)\")\n", - "ax.set_zlabel(\"Cost ($)\")\n", - "cost_depth_cabledepth.plot(ax, plot_data=True)\n", - "cost_depth_cabledepth.plot(ax, plot_curves=[\"quadratic_2d\"])\n", - "\n", - "ax = fig.add_subplot(2, 2, 3, projection='3d')\n", - "ax.set_title(\"Touchdown Distance v Floating Depth\")\n", - "ax.set_xlabel(\"Touchdown Distance (m)\")\n", - "ax.set_ylabel(\"Floating Depth (m)\")\n", - "ax.set_zlabel(\"Cost ($)\")\n", - "cost_touchdown_cabledepth.plot(ax, plot_data=True)\n", - "cost_touchdown_cabledepth.plot(ax, plot_curves=[\"quadratic_2d\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'floating': '36269853.8 * floating_cable_depth**2 + 28303.7 * floating_cable_depth * touchdown_distance + -9366.7 * touchdown_distance**2 + -117.4 * floating_cable_depth + -249.9 * touchdown_distance + 83.8'}\n" - ] - } - ], - "source": [ - "cost_touchdown_cabledepth.export(\"array_system.yaml\", \"floating\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Export System\n", - "\n", - "This block creates a cost model for systems with high voltage alternating current (HVAC) and\n", - "high voltage direct current (HVDC) cables.\n", - "\n", - "Independent variables:\n", - "- site.distance_to_landfall\n", - "- Depth" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a228ee4b8be142bdb82efb0c18561d66", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " Figure\n", + "
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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous \u2026" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NOTE: The parameterization and plotting are split in two blocks since the ORBIT model takes\n", + "# some time to run.\n", + "\n", + "cost_depth_touchdown_distance.quadratic_2d()\n", + "cost_depth_cabledepth.quadratic_2d()\n", + "cost_touchdown_cabledepth.quadratic_2d()\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(2, 2, 1, projection='3d')\n", + "ax.set_title(\"Depth vs Touchdown Distance\")\n", + "ax.set_xlabel(\"Depth (m)\")\n", + "ax.set_ylabel(\"Touchdown Distance (m)\")\n", + "ax.set_zlabel(\"Cost ($)\")\n", + "cost_depth_touchdown_distance.plot(ax, plot_data=True)\n", + "cost_depth_touchdown_distance.plot(ax, plot_curves=[\"quadratic_2d\"])\n", + "\n", + "ax = fig.add_subplot(2, 2, 2, projection='3d')\n", + "ax.set_title(\"Depth v Cable Depth\")\n", + "ax.set_xlabel(\"Depth (m)\")\n", + "ax.set_ylabel(\"Cable Depth (m)\")\n", + "ax.set_zlabel(\"Cost ($)\")\n", + "cost_depth_cabledepth.plot(ax, plot_data=True)\n", + "cost_depth_cabledepth.plot(ax, plot_curves=[\"quadratic_2d\"])\n", + "\n", + "ax = fig.add_subplot(2, 2, 3, projection='3d')\n", + "ax.set_title(\"Touchdown Distance v Floating Depth\")\n", + "ax.set_xlabel(\"Touchdown Distance (m)\")\n", + "ax.set_ylabel(\"Floating Depth (m)\")\n", + "ax.set_zlabel(\"Cost ($)\")\n", + "cost_touchdown_cabledepth.plot(ax, plot_data=True)\n", + "cost_touchdown_cabledepth.plot(ax, plot_curves=[\"quadratic_2d\"])" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'floating_hvac': '2840.0 * depth + 2840000.0 * dist_s_to_l + 8520000.0'}\n", - "{'floating_hvdc': '828.0 * depth + 828000.0 * dist_s_to_l + 2484000.0'}\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'floating': '36269853.8 * floating_cable_depth**2 + 28303.7 * floating_cable_depth * touchdown_distance + -9366.7 * touchdown_distance**2 + -117.4 * floating_cable_depth + -249.9 * touchdown_distance + 83.8'}\n" + ] + } + ], + "source": [ + "cost_touchdown_cabledepth.export(\"array_system.yaml\", \"floating\")" + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cf34930026454aa1a7d8511a87c5cce3", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " \n", - "
\n", - " " + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Export System\n", + "\n", + "This block creates a cost model for systems with high voltage alternating current (HVAC) and\n", + "high voltage direct current (HVDC) cables.\n", + "\n", + "Independent variables:\n", + "- site.distance_to_landfall\n", + "- Depth" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'floating_hvac': '2840.0 * depth + 2840000.0 * dist_s_to_l + 8520000.0'}\n", + "{'floating_hvdc': '828.0 * depth + 828000.0 * dist_s_to_l + 2484000.0'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf34930026454aa1a7d8511a87c5cce3", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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+ "text/html": [ + "\n", + "
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\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous \u2026" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + "source": [ + "design_phase = \"ExportSystemDesign\"\n", + "results = {\n", + " \"export_system_system_cost\": lambda run: run.design_results[\"export_system\"][\"system_cost\"],\n", + "}\n", + "\n", + "## Run ORBIT for each hvac and hvdc export system types\n", + "\n", + "cost_hvac = CostFunction(\n", + " config={\n", + " \"design_phases\": [design_phase],\n", + " \"export_system_design\": {\"cables\": \"XLPE_1000mm_220kV\"},\n", + " },\n", + " parameters={\n", + " \"site.depth\": DEPTHS,\n", + " \"site.distance_to_landfall\": [i for i in range(0, 400, 10)],\n", + " },\n", + " results=results\n", + ")\n", + "cost_hvac.run()\n", + "\n", + "cost_hvdc = CostFunction(\n", + " config={\n", + " \"design_phases\": [design_phase],\n", + " \"export_system_design\": {\"cables\": \"HVDC_2000mm_320kV\"},\n", + " },\n", + " parameters={\n", + " \"site.depth\": DEPTHS,\n", + " \"site.distance_to_landfall\": [i for i in range(0, 400, 10)],\n", + " },\n", + " results=results\n", + ")\n", + "cost_hvdc.run()\n", + "\n", + "cost_hvac.linear_2d()\n", + "cost_hvdc.linear_2d()\n", + "\n", + "## Plot the ORBIT data and curve fits\n", + "\n", + "fig = plt.figure()\n", + "\n", + "ax = fig.add_subplot(1, 2, 1, projection='3d')\n", + "ax.set_title(\"HVAC\")\n", + "ax.set_xlabel(\"Depth (m)\")\n", + "ax.set_ylabel(\"Distance to Landfall (m)\")\n", + "ax.set_zlabel(\"Cost ($)\")\n", + "cost_hvac.plot(ax, plot_data=True)\n", + "cost_hvac.plot(ax, plot_curves=[\"linear_2d\"])\n", + "\n", + "ax = fig.add_subplot(1, 2, 2, projection='3d')\n", + "ax.set_title(\"HVDC\")\n", + "ax.set_xlabel(\"Depth (m)\")\n", + "ax.set_ylabel(\"Distance to Landfall (m)\")\n", + "ax.set_zlabel(\"Cost ($)\")\n", + "cost_hvdc.plot(ax, plot_data=True)\n", + "cost_hvdc.plot(ax, plot_curves=[\"linear_2d\"])\n", + "\n", + "\n", + "multiline_comment = \"\\n# \".join([\n", + " \"The floating HVAC mooring system is \",\n", + " \"special because it's the only one that \",\n", + " \"is like it is.\"\n", + "])\n", + "cost_hvac.export(\"export_system.yaml\", \"floating_hvac\", comments=multiline_comment)\n", + "\n", + "singleline_comment = \"HVDC export system\"\n", + "cost_hvdc.export(\"export_system.yaml\", \"floating_hvdc\", comments=singleline_comment)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "design_phase = \"ExportSystemDesign\"\n", - "results = {\n", - " \"export_system_system_cost\": lambda run: run.design_results[\"export_system\"][\"system_cost\"],\n", - "}\n", - "\n", - "## Run ORBIT for each hvac and hvdc export system types\n", - "\n", - "cost_hvac = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " \"export_system_design\": {\"cables\": \"XLPE_1000mm_220kV\"},\n", - " },\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " \"site.distance_to_landfall\": [i for i in range(0, 400, 10)],\n", - " },\n", - " results=results\n", - ")\n", - "cost_hvac.run()\n", - "\n", - "cost_hvdc = CostFunction(\n", - " config={\n", - " \"design_phases\": [design_phase],\n", - " \"export_system_design\": {\"cables\": \"HVDC_2000mm_320kV\"},\n", - " },\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " \"site.distance_to_landfall\": [i for i in range(0, 400, 10)],\n", - " },\n", - " results=results\n", - ")\n", - "cost_hvdc.run()\n", - "\n", - "cost_hvac.linear_2d()\n", - "cost_hvdc.linear_2d()\n", - "\n", - "## Plot the ORBIT data and curve fits\n", - "\n", - "fig = plt.figure()\n", - "\n", - "ax = fig.add_subplot(1, 2, 1, projection='3d')\n", - "ax.set_title(\"HVAC\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Distance to Landfall (m)\")\n", - "ax.set_zlabel(\"Cost ($)\")\n", - "cost_hvac.plot(ax, plot_data=True)\n", - "cost_hvac.plot(ax, plot_curves=[\"linear_2d\"])\n", - "\n", - "ax = fig.add_subplot(1, 2, 2, projection='3d')\n", - "ax.set_title(\"HVDC\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Distance to Landfall (m)\")\n", - "ax.set_zlabel(\"Cost ($)\")\n", - "cost_hvdc.plot(ax, plot_data=True)\n", - "cost_hvdc.plot(ax, plot_curves=[\"linear_2d\"])\n", - "\n", - "\n", - "multiline_comment = \"\\n# \".join([\n", - " \"The floating HVAC mooring system is \",\n", - " \"special because it's the only one that \",\n", - " \"is like it is.\"\n", - "])\n", - "cost_hvac.export(\"export_system.yaml\", \"floating_hvac\", comments=multiline_comment)\n", - "\n", - "singleline_comment = \"HVDC export system\"\n", - "cost_hvdc.export(\"export_system.yaml\", \"floating_hvdc\", comments=singleline_comment)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Offshore Floating Substation\n", - "\n", - "This component is not a function of a spatially varying parameter, but it is included to complete\n", - "the export of the capex breakdown components." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'oss_substructure': '0.0 * depth + 2005200.0'}\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Offshore Floating Substation\n", + "\n", + "This component is not a function of a spatially varying parameter, but it is included to complete\n", + "the export of the capex breakdown components." + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4960b52c4ec14543a4d3f0cf4ba7b7aa", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'oss_substructure': '0.0 * depth + 2005200.0'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4960b52c4ec14543a4d3f0cf4ba7b7aa", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous \u2026" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + "source": [ + "cost_function = CostFunction(\n", + " config={\"design_phases\": [\"OffshoreFloatingSubstationDesign\"]},\n", + " parameters={\n", + " \"site.depth\": DEPTHS,\n", + " },\n", + " results={\n", + " \"offshore_substation_substructure\": lambda run: run.design_results[\"offshore_substation_substructure\"][\"unit_cost\"],\n", + " }\n", + ")\n", + "cost_function.run()\n", + "\n", + "cost_function.linear_1d()\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "ax.set_title(\"Offshore Floating Substation\")\n", + "ax.set_xlabel(\"Depth (m)\")\n", + "ax.set_ylabel(\"Cost ($)\")\n", + "cost_function.plot(ax, plot_data=True)\n", + "cost_function.plot(ax, plot_curves=[\"linear_1d\"])\n", + "ax.legend()\n", + "\n", + "cost_function.export(\"oss.yaml\", \"oss_substructure\")" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "cost_function = CostFunction(\n", - " config={\"design_phases\": [\"OffshoreFloatingSubstationDesign\"]},\n", - " parameters={\n", - " \"site.depth\": DEPTHS,\n", - " },\n", - " results={\n", - " \"offshore_substation_substructure\": lambda run: run.design_results[\"offshore_substation_substructure\"][\"unit_cost\"],\n", - " }\n", - ")\n", - "cost_function.run()\n", - "\n", - "cost_function.linear_1d()\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "ax.set_title(\"Offshore Floating Substation\")\n", - "ax.set_xlabel(\"Depth (m)\")\n", - "ax.set_ylabel(\"Cost ($)\")\n", - "cost_function.plot(ax, plot_data=True)\n", - "cost_function.plot(ax, plot_curves=[\"linear_1d\"])\n", - "ax.legend()\n", - "\n", - "cost_function.export(\"oss.yaml\", \"oss_substructure\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "bos", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "bos", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/Example - Custom Array Layout.ipynb b/examples/custom_array.ipynb similarity index 92% rename from examples/Example - Custom Array Layout.ipynb rename to examples/custom_array.ipynb index 2aa16af0..78c56982 100644 --- a/examples/Example - Custom Array Layout.ipynb +++ b/examples/custom_array.ipynb @@ -2,29 +2,29 @@ "cells": [ { "cell_type": "markdown", + "id": "bb87ebba", "metadata": {}, "source": [ - "# Custom Array Cabling Layout Example\n", - "## Dudgeon Windfarm\n", - "\n", + "(custom-array-layou)=\n", + "# Custom Array Cabling Guide\n", "\n", - "#### Author: Rob Hammond\n", - "#### Date: 4 May 2020\n", - "#### Update: 27 October 2025\n", - "\n", - "##### Data source: Dudgeon Wind Farm turbine locations from their publicly available [Call to Mariners](http://dudgeonoffshorewind.co.uk/news/notices/Dudgeon%20-%20Notice%20to%20Mariners%20wk25.pdf)\n", + "## Dudgeon Windfarm\n", "\n", + "This guide will walk through four of the main use cases for using the custom array cable layout\n", + "functionality of `ORBIT` for when custom turbine locations, cable lengths or burial speeds are needed.\n", "\n", - "This notebook will guide you through four of the main use cases on using the custom array cable layout functionality of `ORBIT` for when custom turbine locations, cable lengths or burial speeds are needed.\n", + "This example uses the Dudgeon Wind Farm turbine locations derived from their publicly available\n", + "[Call to Mariners](http://dudgeonoffshorewind.co.uk/news/notices/Dudgeon%20-%20Notice%20to%20Mariners%20wk25.pdf) documents.\n", "\n", - "**Note:** All array cable layout files are CSVs, which can be edited in Microsoft Excel.\n", + "## Setup\n", "\n", - "**Update:** This example was updated to work with ORBIT >= 1.0.0" + "First, we'll import the necessary libraries and functionality, and setup our library reference." ] }, { "cell_type": "code", "execution_count": 1, + "id": "93015a5f", "metadata": {}, "outputs": [ { @@ -36,9 +36,9 @@ } ], "source": [ - "import os\n", "from copy import deepcopy\n", "from pprint import pprint\n", + "from pathlib import Path\n", "\n", "import numpy as np\n", "import pandas as pd\n", @@ -49,37 +49,40 @@ "from ORBIT.phases.design import CustomArraySystemDesign\n", "from ORBIT.phases.install import ArrayCableInstallation\n", "\n", - "# initialize the library location\n", - "library.initialize_library(\"../library\")" + "\n", + "# Set the library path for later use and initialize the ORBIT library\n", + "here = Path(\".\").resolve()\n", + "library_path = here.parents[1] / \"library\" if here.stem == \"topical_guides\" else here\n", + "library.initialize_library(library_path)" ] }, { "cell_type": "markdown", + "id": "19e8efdf", "metadata": {}, "source": [ "## Contents\n", - " - [Overview](#overview): How to use the inputs\n", - " - [Case 1](#case_1): Needing to know what to collect\n", - " - [Case 2](#case_2): Coordinates with a straight-line distance for cable length\n", - " - [Case 3](#case_3): Using distance from a reference point\n", - " - [Case 4](#case_4): Adjusting for exclusions in the cable paths\n", - " - [Case 5](#case_5): Fully customizing the cabling parameters\n", - " - [Applying the cases to `ArrayCableInstallation`](#running)\n", - " - [Using `ProjectManager` to model the entire process](#project_manager)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", + "\n", + "- [Overview](#overview): How to use the inputs\n", + "- [Case 1](#case_1): Needing to know what to collect\n", + "- [Case 2](#case_2): Coordinates with a straight-line distance for cable length\n", + "- [Case 3](#case_3): Using distance from a reference point\n", + "- [Case 4](#case_4): Adjusting for exclusions in the cable paths\n", + "- [Case 5](#case_5): Fully customizing the cabling parameters\n", + "- [Applying the cases to `ArrayCableInstallation`](#running)\n", + "- [Using `ProjectManager` to model the entire process](#project_manager)\n", + "\n", "## Overview\n", "\n", - "#### Before starting it is important to demonstrate how to create a configuration file or how to set up a customized layout file.\n", + "### Working with the ORBIT Library\n", "\n", - "In the highest level of this repository there is a folder called `library` where all of the example data for this notebook is going to be stored. While any folder could be used, the folder structure must be strictly adhered to. More details on this structure can be found [here](https://github.com/WISDEM/ORBIT/blob/master/ORBIT/library.py#L9-L23).\n", + "In the highest level of this repository there is a folder called `library` where all of the example\n", + "data for this notebook is going to be stored. While any folder could be used, the folder structure\n", + "must be strictly adhered to. More details on this structure can be found in the\n", + "[library section of the ORBIT introduction tutorial](#library-tutorial).\n", "\n", - "For this example of how to setup a configuration, I will be using the file `/ORBIT/library/project/config/example_custom_array_simple.yaml`. YAML' files are used for configuration throughout this codebase due their ease of encoding and loading `Python` data types.\n", + "For this example of how to setup a configuration, we will be using the file\n", + "[`library/project/config/example_custom_array_simple.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/project/config/example_custom_array_simple.yaml).\n", "\n", "Now, we will load the configuration file and display it below." ] @@ -87,6 +90,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "5a7ea0ee", "metadata": {}, "outputs": [ { @@ -110,88 +114,158 @@ }, { "cell_type": "markdown", + "id": "a448ecf8", "metadata": {}, "source": [ - "#### A couple of things to notice in the configuration file for a custom array layout:\n", - "```python\n", - "{\n", - " # Array cabling system specific data configuration\n", - " 'array_system_design': {\n", - " \n", - " # A list of array cable YAML files that can be found in library/project/cables/ as\n", - " # XLPE_400mm_33kV.yaml, XLPE_630mm_33kV.yaml, and XLPE_630mm_220kV.yaml\n", - " 'cables': ['XLPE_400mm_33kV', 'XLPE_630mm_33kV', 'XLPE_630mm_220kV'],\n", - " \n", - " # A YAML file named dudgeon_array.csv found in the same location\n", - " 'location_data': 'dudgeon_array'},\n", - " \n", - " # We are using a custom layout and the Dudgeon contains 67 turbines\n", - " 'plant': {'layout': 'custom', 'num_turbines': 67},\n", - " \n", - " # The average water depth at the site\n", - " 'site': {'depth': 20},\n", - " \n", - " # Turbine details (optional for custom)\n", - " 'turbine': 'SWT_6MW_154m_110m'\n", - "}\n", - "```\n", + "### Key Differences In A Custom Layout Configuration\n", "\n", - "#### Now, let's see what is contained within the additional files from the configuration dictionary\n", + "There are 2 important differences in the custom array design that are work calling out:\n", "\n", - "It should be noted that running the design class extracts the data from the files automatically to produce the below output." + "1) The `array_system_design` dictionary contains the `location_data` key, which contains the base\n", + " file name for the layout file, which is assumed to be CSV file located at\n", + " [`library/cables/dudgeon_array.csv`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/dudgeon_array.csv)\n", + "2) The `plant` dictionary uses the \"custom\" for `layout` to indicate that the custom array design\n", + " workflow will be used.\n", + "\n", + "Now, let's see what is contained within the additional files from the configuration dictionary. It\n", + "should be noted that running the design class extracts the data from the files automatically to\n", + "produce the below output." ] }, { "cell_type": "code", "execution_count": 3, + "id": "2c28f55d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'array_system_design': {'cables': {'XLPE_400mm_33kV': {'ac_resistance': 0.06, 'capacitance': 225, 'conductor_size': 400, 'cost_per_km': 364352, 'current_capacity': 600, 'inductance': 0.375, 'linear_density': 35, 'name': 'XLPE_400mm_33kV', 'rated_voltage': 33}, 'XLPE_630mm_33kV': {'ac_resistance': 0.04, 'capacitance': 300, 'conductor_size': 630, 'cost_per_km': 546528, 'current_capacity': 700, 'inductance': 0.35, 'linear_density': 42.5, 'cable_type': 'HVAC', 'name': 'XLPE_630mm_33kV', 'rated_voltage': 33}, 'XLPE_630mm_220kV': {'ac_resistance': 0.25, 'capacitance': 160, 'conductor_size': 630, 'cost_per_km': 853557, 'current_capacity': 715, 'inductance': 0.41, 'linear_density': 96, 'rated_voltage': 220, 'cable_type': 'HVAC', 'name': 'XLPE_630mm_220kV'}}, 'location_data': 'dudgeon_array'}, 'plant': {'layout': 'custom', 'num_turbines': 67}, 'site': {'depth': 20}, 'turbine': {'blade': {'deck_space': 100, 'length': 75, 'mass': 100}, 'hub_height': 110, 'nacelle': {'deck_space': 200, 'mass': 360}, 'name': 'SWT-6MW-154', 'rotor_diameter': 154, 'tower': {'deck_space': 36, 'sections': 2, 'length': 110, 'mass': 150}, 'turbine_rating': 6, 'rated_windspeed': 13}}\n" + "{\n", + " \"array_system_design\": {\n", + " \"cables\": {\n", + " \"XLPE_400mm_33kV\": {\n", + " \"ac_resistance\": 0.06,\n", + " \"capacitance\": 225,\n", + " \"conductor_size\": 400,\n", + " \"cost_per_km\": 364352,\n", + " \"current_capacity\": 600,\n", + " \"inductance\": 0.375,\n", + " \"linear_density\": 35,\n", + " \"name\": \"XLPE_400mm_33kV\",\n", + " \"rated_voltage\": 33\n", + " },\n", + " \"XLPE_630mm_220kV\": {\n", + " \"ac_resistance\": 0.25,\n", + " \"cable_type\": \"HVAC\",\n", + " \"capacitance\": 160,\n", + " \"conductor_size\": 630,\n", + " \"cost_per_km\": 853557,\n", + " \"current_capacity\": 715,\n", + " \"inductance\": 0.41,\n", + " \"linear_density\": 96,\n", + " \"name\": \"XLPE_630mm_220kV\",\n", + " \"rated_voltage\": 220\n", + " },\n", + " \"XLPE_630mm_33kV\": {\n", + " \"ac_resistance\": 0.04,\n", + " \"cable_type\": \"HVAC\",\n", + " \"capacitance\": 300,\n", + " \"conductor_size\": 630,\n", + " \"cost_per_km\": 546528,\n", + " \"current_capacity\": 700,\n", + " \"inductance\": 0.35,\n", + " \"linear_density\": 42.5,\n", + " \"name\": \"XLPE_630mm_33kV\",\n", + " \"rated_voltage\": 33\n", + " }\n", + " },\n", + " \"location_data\": \"dudgeon_array\"\n", + " },\n", + " \"plant\": {\n", + " \"layout\": \"custom\",\n", + " \"num_turbines\": 67\n", + " },\n", + " \"site\": {\n", + " \"depth\": 20\n", + " },\n", + " \"turbine\": {\n", + " \"blade\": {\n", + " \"deck_space\": 100,\n", + " \"length\": 75,\n", + " \"mass\": 100\n", + " },\n", + " \"hub_height\": 110,\n", + " \"nacelle\": {\n", + " \"deck_space\": 200,\n", + " \"mass\": 360\n", + " },\n", + " \"name\": \"SWT-6MW-154\",\n", + " \"rated_windspeed\": 13,\n", + " \"rotor_diameter\": 154,\n", + " \"tower\": {\n", + " \"deck_space\": 36,\n", + " \"length\": 110,\n", + " \"mass\": 150,\n", + " \"sections\": 2\n", + " },\n", + " \"turbine_rating\": 6\n", + " }\n", + "}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "UserWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/array_system_design.py:1088\n", - "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated." + "UserWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/array_system_design.py:1103\n", + "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated.\n" ] } ], "source": [ "array = CustomArraySystemDesign(config)\n", "array.run()\n", - "pprint(array.config)" + "print(array.config.dump())" ] }, { "cell_type": "markdown", + "id": "a8c60a14", "metadata": {}, "source": [ - "#### When the `dudgeon_array.csv` file is loaded, it is not passed back into the configuration dictionary, so let's dissect this file:\n", + "### Custom Array Layout CSV Explanation\n", + "\n", + "When the `dudgeon_array.csv` file is loaded, it is not passed back into the configuration\n", + "dictionary, so let's dissect this file:\n", "\n", "1. The file must have all of the columns shown below (not case-sensitive).\n", - " - All columns must be completely filled out for turbines (note on substation(s) following).\n", - " - `cable_length` and `bury_speed` are optional and if these are not known, simply fill with a 0.\n", - "2. A latitude and longitude must be provided for all turbines and substation(s). This can either be a WGS-84 decimal coordinate or a distance-based \"coordinate\" where latitude and longitude are the distances from some reference point, in kilometers; see [Case 3](#case_3) for more details.\n", - "2. Define the offshore substation(s)\n", - " - For each substation, the values in columns `id` and `substation_id` _must_ be the same.\n", - " - There is no need to fill in any data for the columns `String`, `Order`, `cable_length` and `bury_speed`.\n", - "3. Define the turbines\n", - " - Each turbine should have a reference to its substation in the `substation_id` column.\n", + " - All columns must be completely filled out for turbines (note on substation(s) following).\n", + " - `cable_length` and `bury_speed` are optional and if these are not known, simply fill with a 0.\n", + "2. A latitude and longitude must be provided for all turbines and substation(s). This can either be\n", + " a WGS-84 decimal coordinate or a distance-based \"coordinate\" where latitude and longitude are the\n", + " distances from some reference point, in kilometers; see [Case 3](#case_3) for more details.\n", + "3. Define the offshore substation(s)\n", + " - For each substation, the values in columns `id` and `substation_id` _must_ be the same.\n", + " - There is no need to fill in any data for the columns `String`, `Order`, `cable_length` and\n", + " `bury_speed`.\n", + "4. Define the turbines\n", + " - Each turbine should have a reference to its substation in the `substation_id` column.\n", " - In this example, there is one substaion, so all of the values are \"DOW_OSS\".\n", - " - `string` and `order` should be 0-indexed for their ordering and not skip any numbers.\n", - " - In this example, the strings are ordered in clock-wise order starting from the string with turbines labeled with an \"A\" in the [Call to Mariners](http://dudgeonoffshorewind.co.uk/news/notices/Dudgeon%20-%20Notice%20to%20Mariners%20wk25.pdf)\n", - " - The ordering on a string should travel from substation to the farthest end of the cable" + " - `string` and `order` should be 0-indexed for their ordering and not skip any numbers.\n", + " - In this example, the strings are ordered in clock-wise order starting from the string with\n", + " turbines labeled with an \"A\" in the\n", + " [Call to Mariners](http://dudgeonoffshorewind.co.uk/news/notices/Dudgeon%20-%20Notice%20to%20Mariners%20wk25.pdf)\n", + " - The ordering on a string should travel from substation to the farthest end of the cable\n", + "\n", + "Below is the how the Dudgeon layout has been configured." ] }, { "cell_type": "code", "execution_count": 4, + "id": "86611b6b", "metadata": {}, "outputs": [ { @@ -400,33 +474,31 @@ } ], "source": [ - "df = pd.read_csv(\"../library/cables/dudgeon_array.csv\").fillna(\"\")\n", + "df = pd.read_csv(library_path / \"cables/dudgeon_array.csv\").fillna(\"\")\n", "df.sort_values(by=[\"String\", \"Order\"])" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", + "id": "7a04891a", "metadata": {}, "source": [ - "\n", + "(case_1)=\n", "## Case 1: Needing to know what to collect\n", "\n", - "Here we know that we need to have a csv created to input all the data but need to see what data is necessary to collect.\n", - "\n", + "In this first case, we assume little knowledge of what data are required for the CSV, and walk\n", + "through generating a sample CSV. We will use the\n", + "[`library/cables/example_custom_array_no_data.csv`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_no_data.csv)\n", + "configuration for this example.\n", "\n", - "First, we need to load in the configuration dictionary. Then, we will create a \"starter\" file that can be filled in for a new project, which will be saved in the \"library\"." + "First, we need to load in the configuration dictionary. Then, we will create a starter file in\n", + "the `/project/config/plant` folder that can be filled in for a new project, which will be saved in the initialized library folder." ] }, { "cell_type": "code", "execution_count": 5, + "id": "b9c9bb8b", "metadata": {}, "outputs": [ { @@ -438,7 +510,6 @@ " 'plant': {'layout': 'custom', 'num_turbines': 67},\n", " 'site': {'depth': 20},\n", " 'turbine': 'SWT_6MW_154m_110m'}\n", - "\n", "+--------------------------------+\n", "| PROJECT SPECIFICATIONS |\n", "+---------------------------+----+\n", @@ -447,20 +518,7 @@ "| N turbines partial string | 2 |\n", "| N partial strings | 1 |\n", "+---------------------------+----+\n", - "Saving custom array to: /cables/dudgeon_array_no_data.csv\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "/Users/rhammond/GitHub_Public/ORBIT/library/cables/dudgeon_array_no_data.csv already exists, overwrite [y/n]? y\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Saving custom array to: /project/plant/dudgeon_array_no_data.csv\n", "Save complete!\n" ] } @@ -468,269 +526,62 @@ "source": [ "config = library.extract_library_specs(\"config\", \"example_custom_array_no_data\")\n", "pprint(config)\n", - "print()\n", "\n", "array = CustomArraySystemDesign(config)\n", - "save_path = array.config[\"array_system_design\"][\"location_data\"]\n", - "array.create_project_csv(save_path)" + "save_name = array.config[\"array_system_design\"][\"location_data\"]\n", + "array.create_project_csv(save_name, folder=\"plant\")" ] }, { "cell_type": "markdown", + "id": "7dfa3b0e", "metadata": {}, "source": [ - "#### Let's take a look at the data to see what it output\n", + "There are a few items worth noting in the layout:\n", "\n", - "**NOTE**:\n", - " 1. The offshore substation (row 0) is indicated via the `id` and `substation_id` columns being equal\n", - " 2. For substaions only the `id`, `substation_id`, `name`, `latitued`, and `longitude` are required\n", - " 3. `cable_length` and `bury_speed` are optional columns for turbines\n", - " 4. `string` and `order` are filled out to maximize the length of a string given the cable(s) provided so in this case we can have up to 6 turbines in a string. **These are also, very importantly, starting their numbering with 0.**" + "1. The offshore substation (row 0) is indicated via the `id` and `substation_id` columns being equal\n", + "2. For substaions only the `id`, `substation_id`, `name`, `latitude`, and `longitude` are required\n", + "3. `cable_length` and `bury_speed` are optional columns for turbines\n", + "4. `string` and `order` are filled out to maximize the length of a string given the cable(s)\n", + " provided, which translates to a maximum of 5 turbines in a string.\n", + "5. The string and cable numbering are 0-indexed, so the numbering system starts with 0." ] }, { "cell_type": "code", "execution_count": 6, + "id": "e12e739a", "metadata": {}, "outputs": [], "source": [ - "dudgeon_array_no_data = pd.read_csv(\"../library/cables/dudgeon_array_no_data.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idsubstation_idnamelatitudelongitudestringordercable_lengthbury_speed
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..............................
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68 rows \u00d7 9 columns

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" - ], - "text/plain": [ - " id substation_id name latitude longitude string \\\n", - "0 oss1 oss1 offshore_substation 0.0 0.0 NaN \n", - "1 t0 oss1 turbine-0 0.0 0.0 0.0 \n", - "2 t1 oss1 turbine-1 0.0 0.0 0.0 \n", - "3 t2 oss1 turbine-2 0.0 0.0 0.0 \n", - "4 t3 oss1 turbine-3 0.0 0.0 0.0 \n", - ".. ... ... ... ... ... ... \n", - "63 t62 oss1 turbine-62 0.0 0.0 12.0 \n", - "64 t63 oss1 turbine-63 0.0 0.0 12.0 \n", - "65 t64 oss1 turbine-64 0.0 0.0 12.0 \n", - "66 t65 oss1 turbine-65 0.0 0.0 13.0 \n", - "67 t66 oss1 turbine-66 0.0 0.0 13.0 \n", - "\n", - " order cable_length bury_speed \n", - "0 NaN NaN NaN \n", - "1 0.0 0.0 0.0 \n", - "2 1.0 0.0 0.0 \n", - "3 2.0 0.0 0.0 \n", - "4 3.0 0.0 0.0 \n", - ".. ... ... ... \n", - "63 2.0 0.0 0.0 \n", - "64 3.0 0.0 0.0 \n", - "65 4.0 0.0 0.0 \n", - "66 0.0 0.0 0.0 \n", - "67 1.0 0.0 0.0 \n", - "\n", - "[68 rows x 9 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dudgeon_array_no_data" + "dudgeon_array_no_data = pd.read_csv(library_path / f\"project/plant/{save_name}.csv\")\n", + "dudgeon_array_no_data\n", + "\n", + "# NOTE: remove this line if you would like to keep this data\n", + "Path(library_path / \"project/plant/dudgeon_array_no_data.csv\").unlink()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", + "id": "3d878364", "metadata": {}, "source": [ - "\n", - "## Case 2: Standard straight-line distance for cable lengths\n", + "(case_2)=\n", + "## Case 2: Straight-Line Distance for Cable Lengths\n", + "\n", + "We have the turbine and offshore substation locations that were extracted from the Call to Mariners\n", + "referenced in the [Dudgeon Wind Farm Overview](#dudgeon-windfarm). However there is not any\n", + "information regarding the actual cable lengths or the cable burial speeds for each section. As such,\n", + "we will demonstrate using the standard straight-line distance and default cable burying rates.\n", "\n", - "Here we have the turbine and offshore substation locations that were extracted from the data source in the header but nothing specific regarding the actual cable lengths or the cable burial speeds for each section." + "This case will rely on the\n", + "[`library/cables/example_custom_array_simple.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_simple.yaml) configuration." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, + "id": "c1ca2920", "metadata": {}, "outputs": [ { @@ -752,39 +603,28 @@ "pprint(config)" ] }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/array_system_design.py:1088\n", - "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated." - ] - } - ], - "source": [ - "array = CustomArraySystemDesign(config)\n", - "array.run()" - ] - }, { "cell_type": "markdown", + "id": "a29c491a", "metadata": {}, "source": [ - "#### Let's take a look at the data to see what it output\n", - "\n", - "**NOTE**: Here the cable length and bury speed are still set to 0 to indicate that they are unknown" + "The below figure demonstrates the meaning of the straight-line distance between two points." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, + "id": "785f1989", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "UserWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/array_system_design.py:1103\n", + "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated.\n" + ] + }, { "data": { "image/png": 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", @@ -797,19 +637,25 @@ } ], "source": [ + "array = CustomArraySystemDesign(config)\n", + "array.run()\n", "array.plot_array_system(show=True)" ] }, { "cell_type": "markdown", + "id": "465c4c59", "metadata": {}, "source": [ - "#### It should be noted here that the the latitude and longitude here are WGS-84 decimal coordinates" + "Here the cable length and bury speed are still set to 0 to indicate that they are unknown, which\n", + "will tell the installation phase to use either ORBIT's defaults or the vessel's settings. Notice\n", + "that the latitude and longitude here are WGS-84 decimal coordinates." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, + "id": "35c29773", "metadata": {}, "outputs": [ { @@ -1061,7 +907,7 @@ "[67 rows x 12 columns]" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1072,14 +918,16 @@ }, { "cell_type": "markdown", + "id": "b44d138a", "metadata": {}, "source": [ - "#### Now let's look at the cost for this cabling setup by each type of cable as well as the total cost" + "For later comparison, we'll show the cabling costs for the straight-line cabling assumption." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, + "id": "13a3b94d", "metadata": {}, "outputs": [ { @@ -1098,25 +946,38 @@ "print(f\"{'Cable Type':<16}| {'Cost in USD':>15}\")\n", "for cable, cost in array.cost_by_type.items():\n", " print(f\"{cable:<16}| ${cost:>15,.2f}\")\n", - " \n", + "\n", "print(f\"{'Total':<16}| ${array.total_cable_cost:>15,.2f}\")" ] }, { "cell_type": "markdown", + "id": "6a674d26", "metadata": {}, "source": [ - "\n", - "## Case 3: Distance-based \"coordinate\" system\n", + "(case_3)=\n", + "## Case 3: Distance-based coordinate system\n", + "\n", + "In this case, we will consider each turbine and substation on a distance-based coordinate system\n", + "where the longitude and latitude are the longitudinal (x direction) and latitudinal (y direction)\n", + "**distances**, in kilometers, from a common reference point. We are still using the Dudgeon data,\n", + "but the distances were computed outside of this example and the details are not be included.\n", "\n", - "In this case, we will consider each turbine and substation on a distance-based \"coordinate\" system where the longitude and latitude are the longitudinal (x direction) and latitudinal (y direction) **distances**, in kilometers, from a common reference point. We are still using the Dudgeon data, but the distances were computed outside of this example and the details are not be included.\n", + ":::{important}\n", + "For distance-based coordinate systems, all points should be be positive, meaning the reference point\n", + "should either be both west and south of the farm itself, or at the west-most and south-most point.\n", + ":::\n", "\n", - "Below, we can see that the input file is still encoded in the exact same manner as [Case 2](#case_2), but latitude and longitude are relative distances and not proper coordinates." + "Below, we can see that the input file\n", + "[`library/cables/dudgeon_distance_based.csv`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/dudgeon_distance_based.csv)\n", + "is still encoded in the exact same manner as [Case 2](#case_2), but latitude and longitude are\n", + "relative distances and not proper coordinates." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, + "id": "0209f5b5", "metadata": {}, "outputs": [ { @@ -1319,26 +1180,30 @@ "[68 rows x 9 columns]" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df = pd.read_csv(\"../library/cables/dudgeon_distance_based.csv\", index_col=False).fillna(\"\")\n", + "df = pd.read_csv(library_path / \"cables/dudgeon_distance_based.csv\", index_col=False).fillna(\"\")\n", "df" ] }, { "cell_type": "markdown", + "id": "68f8ed8f", "metadata": {}, "source": [ - "#### For this case we also add the `distance` argument to the `array_system_design` and set it to `True` to indicate we are dealing with distances." + "Using the distance-based location data requires us to set `distance` to True in the\n", + "`array_system_design` section of the configuration. This change is shown below in the\n", + "[`library/cables/example_custom_array_simple_distance_based.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_simple_distance_based.yaml) configuration." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, + "id": "def4f2ab", "metadata": {}, "outputs": [ { @@ -1363,46 +1228,30 @@ }, { "cell_type": "markdown", + "id": "f4fb4080", "metadata": {}, "source": [ - "#### OR we can create the flag in the function call.\n", - "\n", - "**Note:** the configuration dictionary will always override this setting." + "Alternatively, we can set the `distance=True` when calling the `CustomArraySystemDesign`, however\n", + "the configuration dictionary's setting will override this input to allow for project-level\n", + "configurations to run as expected. Below, we can see some of the cable lengths differ slightly due\n", + "to the methodology of converting the WGS-84coordinates to relative points, however the spacing is\n", + "maintained, and we can see that this is still the Dudgeon windfarm." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, + "id": "80963443", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "UserWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/array_system_design.py:1088\n", - "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated." + "UserWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/array_system_design.py:1103\n", + "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated.\n" ] - } - ], - "source": [ - "array_distance = CustomArraySystemDesign(config, distance=True)\n", - "array_distance.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's take a look at the data to see what it output\n", - "\n", - "While some of the cable lengths may be slightly different, the spacing is still maintained, and we can see that this is the Dudgeon windfarm." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + }, { "data": { "image/png": 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", @@ -1415,21 +1264,24 @@ } ], "source": [ + "array_distance = CustomArraySystemDesign(config, distance=True)\n", + "array_distance.run()\n", "array_distance.plot_array_system(show=True)" ] }, { "cell_type": "markdown", + "id": "3443d9e5", "metadata": {}, "source": [ - "#### Now let's look at the cost for this cabling setup by each type of cable as well as the total cost and compare it to the previous case\n", - "\n", - "While there is a minor difference, this difference is small in comparison to the total project cost and errs in a more conservative direction." + "Overall, the cabling cost is highly similar, with the difference being attributed to the method\n", + "to convert the WGS-84 coordiantes to relative coordinates." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, + "id": "c44de973", "metadata": {}, "outputs": [ { @@ -1448,81 +1300,72 @@ "print(f\"{'Cable Type':<16} | {'Cost in USD (lat,lon)':>20} | {'Cost in USD (dist_lat,dist_lon)':>15}\")\n", "for (cable1, cost1), (cable2, cost2) in zip(array.cost_by_type.items(), array_distance.cost_by_type.items()):\n", " print(f\"{cable1:<16} | ${cost1:>20,.2f} | ${cost2:>15,.2f}\")\n", - " \n", + "\n", "print(f\"{'Total':<16} | ${array.total_cable_cost:>20,.2f} | ${array_distance.total_cable_cost:>15,.2f}\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", + "id": "b5f6feac", "metadata": {}, "source": [ - "\n", - "## Case 4: We want to account for some additions to the cable lengths due to exclusion zones\n", + "(case_4)=\n", + "## Case 4: Site-Wide Cable Length Modifications\n", "\n", - "This can be done with the `\"average_exclusion_percent\"` keyword in the configuration that can be seen below.\n", + "To account for exclusion zones from rocky soil or other seabed conditions, we use the\n", + "`average_exclusion_percent` input in the `array_system_design` configuration section. This exclusion\n", + "will be applied to all cable sections, so it's important to account for this when modeling\n", + "additional cable lengths.\n", "\n", - "**Note:**\n", - " 1. There is an average exclusion and is applied to each of the cable sections\n", - " 2. The plot won't change because it will not have details on the new paths so we'll only demonstrate the cost changes (a 4.8% increase, which is in line with the exclusion and the accounting for the site depth." + "In the\n", + "[`library/cables/example_custom_array_exclusions.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_exclusions.yaml)\n", + "configuration, a 4.8% exclusion is applied to the entire farm. When plotting farms with exclusion\n", + "zones, they will not be shown since we are not mapping the true cable path, simply the connections\n", + "between turbines. In this case, we can also a modest increase in cabling costs resulting from the\n", + "additional cable required to account for the exclusion zones." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, + "id": "59b5e904", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'array_system_design': {'cables': ['XLPE_400mm_33kV',\n", - " 'XLPE_630mm_33kV',\n", - " 'XLPE_630mm_220kV'],\n", - " 'location_data': 'dudgeon_array',\n", - " 'average_exclusion_percent': 0.05},\n", - " 'plant': {'layout': 'custom', 'num_turbines': 67},\n", - " 'site': {'depth': 20},\n", - " 'turbine': 'SWT_6MW_154m_110m'}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config = library.extract_library_specs(\"config\", \"example_custom_array_exclusions\")\n", - "config" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ + "name": "stdout", + "output_type": "stream", + "text": [ + "{'array_system_design': {'average_exclusion_percent': 0.05,\n", + " 'cables': ['XLPE_400mm_33kV',\n", + " 'XLPE_630mm_33kV',\n", + " 'XLPE_630mm_220kV'],\n", + " 'location_data': 'dudgeon_array'},\n", + " 'plant': {'layout': 'custom', 'num_turbines': 67},\n", + " 'site': {'depth': 20},\n", + " 'turbine': 'SWT_6MW_154m_110m'}\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "UserWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/array_system_design.py:1088\n", - "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated." + "UserWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/array_system_design.py:1103\n", + "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated.\n" ] } ], "source": [ + "config = library.extract_library_specs(\"config\", \"example_custom_array_exclusions\")\n", + "pprint(config)\n", + "\n", "array_exclusion = CustomArraySystemDesign(config)\n", "array_exclusion.run()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, + "id": "024c8036", "metadata": {}, "outputs": [ { @@ -1541,80 +1384,73 @@ "print(f\"{'Cable Type':<16}| {'Cost in USD':>15}\")\n", "for cable, cost in array_exclusion.cost_by_type.items():\n", " print(f\"{cable:<16}| ${cost:>15,.2f}\")\n", - " \n", + "\n", "print(f\"{'Total':<16}| ${array_exclusion.total_cable_cost:>15,.2f}\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", + "id": "b0d0a841", "metadata": {}, "source": [ - "\n", - "## Case 5: Customize the distances \n", - "\n", - "If we look at the map in the [Call to Mariners](http://dudgeonoffshorewind.co.uk/news/notices/Dudgeon%20-%20Notice%20to%20Mariners%20wk25.pdf) there are different sized exclusions in the cables, so for this example we'll change the distances from [Case 4](#case_4) where we used an average exclusion to be a bit different in each case by using the `cable_length` column. In addition, we will utilize the `bury_speed` column to demonstrate how these columns will be used.\n", + "(case_5)=\n", + "## Case 5: Custom Cable Lengths\n", "\n", - "**Note:** this work was done outside the notebook, but can be uploaded as show in the example below.\n", + "If we look at the map in the\n", + "[Call to Mariners](http://dudgeonoffshorewind.co.uk/news/notices/Dudgeon%20-%20Notice%20to%20Mariners%20wk25.pdf)\n", + "there are different sized exclusions in the cables, so for this example we'll change the distances\n", + "from [Case 4](#case_4) to have more variation by using the `cable_length` column of the\n", + "`location_data` CSV. In addition, we will utilize the `bury_speed` column to demonstrate how these\n", + "columns will be used. Please note this work was performed outside the example, and we will only\n", + "show the resulting configurations.\n", "\n", - "For this example, half of the windfarm will have different soil condition, so we will use our proxy: `bury_speed` by modifying the burial speed to be fast (0.5 km/h) and slow (0.05 km/hr), respectively, to account for sandy soil and rocky soil. The purpose of this is for passing through customized parameters in the design phase to be utilized in the installation phase as will be seen in the final two examples." + "For this example, half of the windfarm will have different soil condition, so we will use our proxy:\n", + "`bury_speed` by modifying the burial speed to be fast (0.5 km/h) and slow (0.05 km/hr),\n", + "respectively, to account for sandy soil and rocky soil. The purpose of this is for passing through\n", + "customized parameters in the design phase to be utilized in the installation phase as will be seen\n", + "in the final two examples." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, + "id": "3833eb5d", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'array_system_design': {'cables': ['XLPE_400mm_33kV',\n", - " 'XLPE_630mm_33kV',\n", - " 'XLPE_630mm_220kV'],\n", - " 'location_data': 'dudgeon_custom'},\n", - " 'plant': {'layout': 'custom', 'num_turbines': 67},\n", - " 'site': {'depth': 20},\n", - " 'turbine': 'SWT_6MW_154m_110m'}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "{'array_system_design': {'cables': ['XLPE_400mm_33kV',\n", + " 'XLPE_630mm_33kV',\n", + " 'XLPE_630mm_220kV'],\n", + " 'location_data': 'dudgeon_custom'},\n", + " 'plant': {'layout': 'custom', 'num_turbines': 67},\n", + " 'site': {'depth': 20},\n", + " 'turbine': 'SWT_6MW_154m_110m'}\n" + ] } ], "source": [ "config = library.extract_library_specs(\"config\", \"example_custom_array_custom\")\n", + "pprint(config)\n", "\n", - "# Note location_data the same one that was saved because I updated it!\n", - "config" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ "array_custom = CustomArraySystemDesign(config)\n", "array_custom.run()" ] }, { "cell_type": "markdown", + "id": "46b1fda0", "metadata": {}, "source": [ - "#### Note that there are now cable lengths defined as well as burial speeds for installation" + "Note that there are now cable lengths defined as well as burial speeds for the installation phase." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, + "id": "60c694ae", "metadata": {}, "outputs": [ { @@ -1866,7 +1702,7 @@ "[67 rows x 12 columns]" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1877,14 +1713,16 @@ }, { "cell_type": "markdown", + "id": "9656bad6", "metadata": {}, "source": [ - "#### See also that the costs have increased again!" + "Once again, the cabling costs have increased." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, + "id": "64fb59cd", "metadata": {}, "outputs": [ { @@ -1903,71 +1741,49 @@ "print(f\"{'Cable Type':<16}| {'Cost in USD':>15}\")\n", "for cable, cost in array_custom.cost_by_type.items():\n", " print(f\"{cable:<16}| ${cost:>15,.2f}\")\n", - " \n", + "\n", "print(f\"{'Total':<16}| ${array_custom.total_cable_cost:>15,.2f}\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Let's run some simulations!\n", - "We can now compare cases 2-4 to see how the installation cost will vary." - ] - }, { "cell_type": "markdown", + "id": "deed3cbb", "metadata": {}, "source": [ - "#### First, we have to create a configuration dictionary for each of the 3 main cases we'll be simulating for installations, corresponding to the configuration file from the tests library. Then, we'll update eeach with the `design_result` of each of the 3 cases that we defined above." + "(running)=\n", + "## Incorporating Custom Array Designs Into `ProjectManager`\n", + "\n", + "Using cases 2, 3, 4, and 5 we will demonstrate the project-wide effects from differing cabling layouts.\n", + "\n", + "### Setting Up The Cases\n", + "\n", + "Using the\n", + "[`library/cables/example_array_cable_install.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_array_cable_install.yaml)\n", + "configuration as a base configuration, we'll create a new configuration for each of the cases\n", + "using each case's `design_result` as the `array_system` values." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, + "id": "cacd4552", "metadata": {}, "outputs": [], "source": [ "base_config = library.extract_library_specs(\"config\", \"example_array_cable_install\")\n", "\n", - "#Case 2\n", "array_case2 = deepcopy(base_config)\n", "array_case2[\"array_system\"] = array.design_result[\"array_system\"]\n", "\n", - "# Case 3\n", "array_case3 = deepcopy(base_config)\n", "array_case3[\"array_system\"] = array_distance.design_result[\"array_system\"]\n", "\n", - "# Case 4\n", "array_case4 = deepcopy(base_config)\n", "array_case4[\"array_system\"] = array_exclusion.design_result[\"array_system\"]\n", "\n", - "# Case 5\n", "array_case5 = deepcopy(base_config)\n", - "array_case5[\"array_system\"] = array_custom.design_result[\"array_system\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Instantiate the simulations" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ + "array_case5[\"array_system\"] = array_custom.design_result[\"array_system\"]\n", + "\n", "sim2 = ArrayCableInstallation(array_case2)\n", "sim3 = ArrayCableInstallation(array_case3)\n", "sim4 = ArrayCableInstallation(array_case4)\n", @@ -1976,16 +1792,20 @@ }, { "cell_type": "markdown", + "id": "fc5029fe", "metadata": {}, "source": [ - "#### Run the simulations\n", + "### Run And Inspect The Simulation Results\n", "\n", - "We can see that both the installation cost and the time required to complete the simulation have all increased here, which corresponds to the increased cable lengths and changes to the burial speeds defined above." + "We can see that both the installation cost and the time required to complete the installations have\n", + "all increased here, corresponding to the increased cable lengths and changes to the burial speeds\n", + "defined above." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 21, + "id": "d6996132", "metadata": {}, "outputs": [ { @@ -1998,6 +1818,20 @@ "with exclusions | $24,784,480.17 | 2,391\n", "custom | $31,792,520.58 | 3,088\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "custom | $31,792,520.58 | 3,088" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] } ], "source": [ @@ -2012,28 +1846,24 @@ " print(f\"{name:<26} | ${cost:>13,.2f} | {time:>16,.0f}\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", + "id": "5b768d6c", "metadata": {}, "source": [ - "\n", - "## Let's put this all together and load with a data frame\n", + "(project_manager)=\n", + "### Incorporating Case 5 Into `ProjectManager`\n", "\n", - "### Using `ProjectManager` we will run Case 4 from design to installation.\n", - "\n", - "We'll see here at the end that we end up with the same results running a custom array cabling project piecemeal and as a whole." + "We will now incorporate the desgin settings from [Case 5](#case_5) to demonstrate incorporation\n", + "of the custom array design tooling into `ProjectManager`. This example will use the\n", + "[`library/cables/example_custom_array_project_manager.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/library/cables/example_custom_array_project_manager.yaml)\n", + "configuration." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 22, + "id": "a3a1658c", "metadata": {}, "outputs": [ { @@ -2080,7 +1910,7 @@ " 'array_cable_bury_vessel': 'example_cable_lay_vessel'}" ] }, - "execution_count": 28, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2094,18 +1924,18 @@ ] }, { - "cell_type": "code", - "execution_count": 29, + "cell_type": "markdown", + "id": "0ebe43bc", "metadata": {}, - "outputs": [], "source": [ - "project = ProjectManager(config)\n", - "project.run()" + "Below, we can see that the results coming from the `ProjectManager` are the same as the additive\n", + "results of running each phase separately." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 23, + "id": "64b8a2c2", "metadata": {}, "outputs": [ { @@ -2114,36 +1944,32 @@ "text": [ "Custom Design | $40,307,731.86\n", "Custom Installation | $31,792,520.58\n", - "Total Cost. | $72,100,252.44\n", + "Total Custom Cost | $72,100,252.44\n", "Project Manager Cost | $72,100,252.44\n" ] } ], "source": [ - "total = array_custom.total_cable_cost + sim5.installation_capex\n", + "project = ProjectManager(config)\n", + "project.run()\n", "\n", + "total = array_custom.total_cable_cost + sim5.installation_capex\n", "print(f\"Custom Design | ${array_custom.total_cable_cost:>13,.2f}\")\n", "print(f\"Custom Installation | ${sim5.installation_capex:>13,.2f}\")\n", - "print(f\"Total Cost. | ${total:>13,.2f}\")\n", + "print(f\"Total Custom Cost | ${total:>13,.2f}\")\n", "print(f\"Project Manager Cost | ${project.bos_capex:>13,.2f}\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { + "jupytext": { + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.19.1" + } + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", @@ -2160,8 +1986,55 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11" - } + }, + "source_map": [ + 12, + 29, + 48, + 75, + 78, + 94, + 98, + 126, + 129, + 142, + 149, + 160, + 166, + 179, + 182, + 186, + 190, + 196, + 198, + 202, + 208, + 228, + 231, + 237, + 240, + 248, + 252, + 257, + 263, + 280, + 288, + 294, + 313, + 319, + 323, + 325, + 329, + 335, + 349, + 368, + 376, + 386, + 396, + 402, + 407 + ] }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } diff --git a/examples/export_cable_system.ipynb b/examples/export_cable_system.ipynb new file mode 100644 index 00000000..893f33e1 --- /dev/null +++ b/examples/export_cable_system.ipynb @@ -0,0 +1,414 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ef2ec428", + "metadata": {}, + "source": [ + "# HVAC vs HVDC Systems\n", + "\n", + "Technology decisions have an impact on a wind project's CapEx. This example will provide a basic\n", + "setup to compare how different project sizes, export cable types (HVDC or HVDC), distances to shore,\n", + "and etc. effect project costs. Instead of other guides' approach of using the `ExportSystemDesign`,\n", + "this will highlight the use of the `ElectricalDesign` model that co-designs the substation\n", + "and export cabling system." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1bdcfa15", + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from ORBIT.phases.design import ElectricalDesign\n", + "from ORBIT import ProjectManager, ParametricManager\n", + "\n", + "# Apply thousands separators and no decimals to floats\n", + "pd.options.display.float_format = '{:,.0f}'.format" + ] + }, + { + "cell_type": "markdown", + "id": "27712633", + "metadata": {}, + "source": [ + "## Setup The Models\n", + "\n", + "Here we will setup a base configuration for use with `ProjectManager` plus additional configurations\n", + "for running in the `ParametericManager` to look at the cost tradeoffs in export cable types\n", + "depending on project size.\n", + "Config must include all required variables except those you plan to vary. In this example, we will be manually vary the cable type and then use the `ParametricManager` to vary cable type and plant capacity." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "25a42ce3", + "metadata": {}, + "outputs": [], + "source": [ + "base_config = {\n", + " \"export_cable_install_vessel\": \"example_cable_lay_vessel\",\n", + " \"site\": {\n", + " \"distance\": 100,\n", + " \"depth\": 20,\n", + " \"distance_to_landfall\": 50,\n", + " },\n", + " \"plant\": {\n", + " \"capacity\": 1000,\n", + " },\n", + " \"turbine\": \"12MW_generic\",\n", + " \"oss_install_vessel\": \"example_heavy_lift_vessel\",\n", + " \"feeder\": \"future_feeder\",\n", + " \"design_phases\": [\n", + " \"ElectricalDesign\",\n", + " ],\n", + " \"install_phases\": [\n", + " \"ExportCableInstallation\",\n", + " \"OffshoreSubstationInstallation\",\n", + " ],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "65677c91", + "metadata": {}, + "source": [ + "Now we can create an HVAC and HVDC variation of the `base_config`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b69c62c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ORBIT library intialized at '/Users/rhammond/GitHub_Public/ORBIT/library'\n", + "OffshoreSubstationInstallation:\n", + "\t Warning: 'Feeder 0' Cargo Mass Capacity Exceeded\n", + "OffshoreSubstationInstallation:\n", + "\t Warning: 'Feeder 0' Cargo Mass Capacity Exceeded\n" + ] + } + ], + "source": [ + "hvac_config = deepcopy(base_config)\n", + "hvac_config[\"export_system_design\"] = {\"cables\": \"XLPE_1000mm_220kV\"}\n", + "\n", + "hvdc_config = deepcopy(base_config)\n", + "hvdc_config[\"export_system_design\"] = {\"cables\": \"HVDC_2000mm_320kV\"}\n", + "\n", + "\n", + "hvac_project = ProjectManager(hvac_config)\n", + "hvac_project.run()\n", + "\n", + "hvdc_project = ProjectManager(hvdc_config)\n", + "hvdc_project.run()" + ] + }, + { + "cell_type": "markdown", + "id": "d3c8be73", + "metadata": {}, + "source": [ + "## Compare the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9cbdb025", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HVAC CapEx per kW: $2,974.90 \n", + "HVDC CapEx per kW: $3,377.08\n" + ] + } + ], + "source": [ + "print(f\"HVAC CapEx per kW: ${hvac_project.total_capex_per_kw:,.2f} \")\n", + "print(f\"HVDC CapEx per kW: ${hvdc_project.total_capex_per_kw:,.2f} \")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4b30149b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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HVAC CapExHVDC CapEx
Category
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Export System Installation49,953,78814,008,968
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Project355,000,000355,000,000
\n", + "
" + ], + "text/plain": [ + " HVAC CapEx HVDC CapEx\n", + "Category \n", + "Export System 318,311,296 95,436,000\n", + "Offshore Substation 245,496,817 795,031,033\n", + "Export System Installation 49,953,788 14,008,968\n", + "Offshore Substation Installation 3,861,208 3,861,208\n", + "Onshore Substation 195,273,630 254,763,736\n", + "Turbine 1,310,400,000 1,310,400,000\n", + "Soft 496,606,631 548,577,239\n", + "Project 355,000,000 355,000,000" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "capex_df = (\n", + " pd.DataFrame(\n", + " [*hvac_project.capex_breakdown.items()],\n", + " columns=[\"Category\", \"HVAC CapEx\"]\n", + " ).set_index(\"Category\")\n", + " .join(\n", + " pd.DataFrame(\n", + " [*hvdc_project.capex_breakdown.items()],\n", + " columns=[\"Category\", \"HVDC CapEx\"]\n", + " ).set_index(\"Category\")\n", + " )\n", + ")\n", + "capex_df" + ] + }, + { + "cell_type": "markdown", + "id": "8274721a", + "metadata": {}, + "source": [ + "## Setup The Parametric Runs\n", + "\n", + "From the two base cases above, we see that HVDC cables are more cost effective than HVAC by\n", + "roughly 30%. However, the offshore substation (OSS) is over three times the cost of an HVAC OSS.\n", + "To compare this sensitivity and see if there is an point that these technologies cross we'll use\n", + "the `ParametricManager` and sweep each cable for a range of plant capacities." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "41ef8149", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = {\n", + " \"export_system_design.cables\": [\"XLPE_1000mm_220kV\", \"HVDC_2000mm_320kV\"],\n", + " \"plant.capacity\": np.arange(100, 2100, 100),\n", + "}\n", + "\n", + "results = {\n", + " \"cable_cost\": lambda run: run.total_cable_cost,\n", + " \"oss_cost\": lambda run: run.substation_cost,\n", + " \"num_cables\": lambda run: run.num_cables,\n", + " \"num_substations\": lambda run: run.num_substations,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f2842c29", + "metadata": {}, + "outputs": [], + "source": [ + "parametric = ParametricManager(\n", + " base_config, parameters, results, module = ElectricalDesign, product=True\n", + ")\n", + "parametric.run()" + ] + }, + { + "cell_type": "markdown", + "id": "5eb93e3f", + "metadata": {}, + "source": [ + "## Compare the Cost vs Capacity Trade Off\n", + "\n", + "The inflection point of the below graph shows that for a project that has less than 700 MW of\n", + "capacity, HVAC is more cost effective, and for projects greater than 700 MW should, HVDC is more\n", + "cost effective." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3d3ec2cf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(parametric.results)\n", + "\n", + "fig = plt.figure(figsize=(6,4), dpi=200)\n", + "ax = fig.subplots(1)\n", + "\n", + "hvac_df = df[df[\"export_system_design.cables.XLPE_1000mm_220kV.name\"] == \"XLPE_1000mm_220kV\"]\n", + "hvdc_df = df[df[\"export_system_design.cables.HVDC_2000mm_320kV.name\"] == \"HVDC_2000mm_320kV\"]\n", + "\n", + "ax.plot(\n", + " hvac_df[\"plant.capacity\"],\n", + " (hvac_df[\"cable_cost\"] + hvac_df[\"oss_cost\"]) / 1e6,\n", + " label=\"HVAC\"\n", + ")\n", + "\n", + "ax.plot(\n", + " hvdc_df[\"plant.capacity\"],\n", + " (hvdc_df[\"cable_cost\"] + hvdc_df[\"oss_cost\"]) / 1e6,\n", + " label=\"HVDC\",\n", + ")\n", + "\n", + "ax.set_ylabel(\"CapEx [$M]\")\n", + "ax.set_xlabel(\"Capacity [MW]\")\n", + "ax.legend()\n", + "ax.grid()\n", + "\n", + "fig.tight_layout()" + ] + } + ], + "metadata": { + "jupytext": { + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.19.1" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "source_map": [ + 12, + 22, + 34, + 43, + 65, + 69, + 82, + 86, + 91, + 105, + 114, + 128, + 133, + 141 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/fixed_bottom_installations.ipynb b/examples/fixed_bottom_installations.ipynb new file mode 100644 index 00000000..9ec029b1 --- /dev/null +++ b/examples/fixed_bottom_installations.ipynb @@ -0,0 +1,734 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4a8b3042", + "metadata": {}, + "source": [ + "# Fixed-Bottom Substructure Installation Models in ORBIT\n", + "\n", + "This guide walks through the use of three separate substructure and turbine installation methods\n", + "listed below. All configuration files for this guide can be found in the\n", + "[`examples/configs/`](https://github.com/NLRWindSystems/ORBIT/tree/main/examples/configs)\n", + "folder.\n", + "\n", + "1. Separate monopile and turbine installation using a heavy lift vessel (HLV) for the monopiles\n", + " and wind turbine installation vessel (WTIV) for the turbines.\n", + "2. Onshore assembly of the gravity-based foundation (GBF) and turbine, tow out and joint\n", + " installation at site.\n", + "3. Tow-out of the GBF and turbine installation using the WTIV.\n", + "\n", + "First, we'll import the required libraries and functionality, and initialize any common variables." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7426cfa6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "UserWarning: /var/folders/q5/tfpytqxn0r396dfg7rk5sj8rwq9tvv/T/ipykernel_41437/4204870401.py:19\n", + "Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n" + ] + } + ], + "source": [ + "import copy\n", + "from pprint import pprint\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as ticker\n", + "\n", + "from ORBIT import ProjectManager, load_config\n", + "\n", + "# Apply thousands separators and no decimals to floats\n", + "pd.options.display.float_format = '{:,.0f}'.format\n", + "\n", + "# Set the example path for use in the docs and standalone examples usage\n", + "here = Path(\".\").resolve()\n", + "example_path = here.parents[1] / \"examples\" if here.stem == \"topical_guides\" else here\n", + "\n", + "weather = pd.read_csv(\n", + " example_path / \"data/example_weather.csv\", parse_dates=[\"datetime\"]\n", + ").set_index(\"datetime\")" + ] + }, + { + "cell_type": "markdown", + "id": "86377ae3", + "metadata": {}, + "source": [ + "## Load The Configurations\n", + "\n", + "Each of cases 1 and 2 have their own configuration file, however the third case is highly similar\n", + "to Case 2, so we will make a distinct copy of it, and add the turbine installation phase to\n", + "indicate the separate substructure and turbine installations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ccd8e5e5", + "metadata": {}, + "outputs": [], + "source": [ + "case1_config = load_config(example_path / \"configs/example_separate_monopile_turbine_vessel.yaml\")\n", + "case2_config = load_config(example_path / \"configs/example_gravity_based_project.yaml\")\n", + "\n", + "case3_config = copy.deepcopy(case2_config)\n", + "case3_config[\"wtiv\"] = \"example_wtiv\"\n", + "case3_config[\"install_phases\"][\"TurbineInstallation\"] = 0" + ] + }, + { + "cell_type": "markdown", + "id": "93501b73", + "metadata": {}, + "source": [ + "The primary differences between these projects deal with the installation strategies, and\n", + "required design stages to support them. Below, we note these differences, but highlighting\n", + "the installation phases that will be modeled.\n", + "\n", + "Note that the `TurbineInstallation` model is only required for projects involving a WTIV (Case 1\n", + "and 2). The `GravityBasedInstallation` model offers flexibility so that if a WTIV is not specified\n", + "in the configuration file, then it models the tow-out of a fully assembled substructure and turbine;\n", + "and, if a WTIV is present, it models the tow-out of the substructure alone with discrete GBF\n", + "and turbine installation phases." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b7b464e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Monopile and Turbine Installation (Heavy Lift Vessel for Monopile Installation, WTIV for Turbine Installation)\n", + "Install phases: ['ArrayCableInstallation', 'ExportCableInstallation', 'TurbineInstallation', 'OffshoreSubstationInstallation', 'ScourProtectionInstallation', 'MonopileInstallation']\n", + "\n", + "Gravity-Based Foundation Intallation (Substructure-Turbine Assembly Tow-out, no WTIV)\n", + "Install phases: ['ArrayCableInstallation', 'ExportCableInstallation', 'GravityBasedInstallation', 'OffshoreSubstationInstallation']\n", + "\n", + "Gravity-Based Foundation and Turbine Intallation (Substructure Tow-out, WTIV for Turbine Installation)\n", + "Install phases: ['ArrayCableInstallation', 'ExportCableInstallation', 'GravityBasedInstallation', 'OffshoreSubstationInstallation', 'TurbineInstallation']\n" + ] + } + ], + "source": [ + "print(f\"Monopile and Turbine Installation (Heavy Lift Vessel for Monopile Installation, WTIV for Turbine Installation)\")\n", + "print(f\"Install phases: {list(case1_config['install_phases'].keys())}\\n\")\n", + "print(f\"Gravity-Based Foundation Intallation (Substructure-Turbine Assembly Tow-out, no WTIV)\")\n", + "print(f\"Install phases: {list(case2_config['install_phases'].keys())}\\n\")\n", + "print(f\"Gravity-Based Foundation and Turbine Intallation (Substructure Tow-out, WTIV for Turbine Installation)\")\n", + "print(f\"Install phases: {list(case3_config['install_phases'].keys())}\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "a1f1c79f", + "metadata": {}, + "source": [ + "## Run The Three Cases\n", + "\n", + "This project is always being modeled with the example weather project supplied that is representative of US East Coast wind farm locations." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c71efe98", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ORBIT library intialized at '/Users/rhammond/GitHub_Public/ORBIT/library'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DeprecationWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/install/quayside_assembly_tow/gravity_base.py:91\n", + "support_vessel will be deprecated and replaced with towing_vessels and ahts_vessel in the towing groups.\n" + ] + } + ], + "source": [ + "case1_project = ProjectManager(case1_config, weather=weather)\n", + "case1_project.run()\n", + "\n", + "case2_project = ProjectManager(case2_config, weather=weather)\n", + "case2_project.run()\n", + "\n", + "case3_project = ProjectManager(case3_config, weather=weather)\n", + "case3_project.run()" + ] + }, + { + "cell_type": "markdown", + "id": "bf7ef3e8", + "metadata": {}, + "source": [ + "## Results Comparison\n", + "\n", + "### CapEx Breakdown" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2aaaec09", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Monopiles + WTIVGBF-Turbine Assembly Tow-outGBF Tow-out + WTIV
CapEx Component
Array System61,594,99361,594,99361,594,993
Array System Installation58,448,33558,448,33558,448,335
Export System358,235,289358,235,289358,235,289
Export System Installation24,499,85924,499,85924,499,859
Offshore Substation307,307,330307,307,330307,307,330
Offshore Substation Installation5,095,6005,095,6005,095,600
Onshore Substation000
Project355,000,000355,000,000355,000,000
Scour Protection6,618,00000
Scour Protection Installation14,748,98900
Soft743,889,072605,889,505664,148,782
Substructure554,716,392285,000,000285,000,000
Substructure Installation53,356,40353,289,84736,496,243
Turbine1,275,000,0001,275,000,0001,275,000,000
Turbine Installation95,293,403095,293,403
Total3,913,803,6673,389,360,7593,526,119,835
\n", + "
" + ], + "text/plain": [ + " Monopiles + WTIV \\\n", + "CapEx Component \n", + "Array System 61,594,993 \n", + "Array System Installation 58,448,335 \n", + "Export System 358,235,289 \n", + "Export System Installation 24,499,859 \n", + "Offshore Substation 307,307,330 \n", + "Offshore Substation Installation 5,095,600 \n", + "Onshore Substation 0 \n", + "Project 355,000,000 \n", + "Scour Protection 6,618,000 \n", + "Scour Protection Installation 14,748,989 \n", + "Soft 743,889,072 \n", + "Substructure 554,716,392 \n", + "Substructure Installation 53,356,403 \n", + "Turbine 1,275,000,000 \n", + "Turbine Installation 95,293,403 \n", + "Total 3,913,803,667 \n", + "\n", + " GBF-Turbine Assembly Tow-out \\\n", + "CapEx Component \n", + "Array System 61,594,993 \n", + "Array System Installation 58,448,335 \n", + "Export System 358,235,289 \n", + "Export System Installation 24,499,859 \n", + "Offshore Substation 307,307,330 \n", + "Offshore Substation Installation 5,095,600 \n", + "Onshore Substation 0 \n", + "Project 355,000,000 \n", + "Scour Protection 0 \n", + "Scour Protection Installation 0 \n", + "Soft 605,889,505 \n", + "Substructure 285,000,000 \n", + "Substructure Installation 53,289,847 \n", + "Turbine 1,275,000,000 \n", + "Turbine Installation 0 \n", + "Total 3,389,360,759 \n", + "\n", + " GBF Tow-out + WTIV \n", + "CapEx Component \n", + "Array System 61,594,993 \n", + "Array System Installation 58,448,335 \n", + "Export System 358,235,289 \n", + "Export System Installation 24,499,859 \n", + "Offshore Substation 307,307,330 \n", + "Offshore Substation Installation 5,095,600 \n", + "Onshore Substation 0 \n", + "Project 355,000,000 \n", + "Scour Protection 0 \n", + "Scour Protection Installation 0 \n", + "Soft 664,148,782 \n", + "Substructure 285,000,000 \n", + "Substructure Installation 36,496,243 \n", + "Turbine 1,275,000,000 \n", + "Turbine Installation 95,293,403 \n", + "Total 3,526,119,835 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The breakdown of project costs by module is available at 'capex_breakdown'\n", + "\n", + "df = pd.DataFrame({\n", + " 'Monopiles + WTIV': pd.Series(case1_project.capex_breakdown),\n", + " 'GBF-Turbine Assembly Tow-out': case2_project.capex_breakdown,\n", + " 'GBF Tow-out + WTIV': pd.Series(case3_project.capex_breakdown)\n", + "}).fillna(0)\n", + "df.loc['Total'] = df.sum()\n", + "df.index.name = \"CapEx Component\"\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "34caffe4", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_capex_comparison(df, num_turbines, project_capacity_mw, top_limit=4000):\n", + " # Reformat the data for easier plotting\n", + " ix_order = [\"Monopiles + WTIV\", \"GBF-Turbine Assembly Tow-out\", \"GBF Tow-out + WTIV\"]\n", + " df = df.copy()\n", + " df.columns = df.columns.str.strip()\n", + " df /= 1e6\n", + " df = df.drop(\"Total\").T.loc[ix_order]\n", + "\n", + " capacity_kw = project_capacity_mw * 1000\n", + "\n", + " # Colors for components\n", + " colors = plt.get_cmap(\"tab20\").colors\n", + " component_order = df.columns.tolist()\n", + " color_map = {component: colors[i % len(colors)] for i, component in enumerate(component_order)}\n", + "\n", + " fig = plt.figure(figsize=(14, 14))\n", + " ax = fig.add_subplot(111)\n", + "\n", + " bar_width = 0.7 # Slightly thinner bars\n", + " bottoms = np.zeros(len(df))\n", + " for component in component_order:\n", + " vals = df[component].values\n", + " bars = ax.bar(\n", + " df.index,\n", + " vals,\n", + " bottom=bottoms,\n", + " width=bar_width,\n", + " color=color_map[component],\n", + " edgecolor=\"black\",\n", + " linewidth=0.8,\n", + " label=component\n", + " )\n", + " bottoms += vals\n", + "\n", + " # Add text inside each stacked segment if $/kW >= 40\n", + " for i, val_musd in enumerate(vals):\n", + " if val_musd == 0:\n", + " continue\n", + " val_usd = val_musd * 1e6\n", + " val_per_kw = val_usd / capacity_kw\n", + " val_per_wtg_musd = val_musd / num_turbines\n", + "\n", + " if val_per_kw >= 40:\n", + " y_pos = bars[i].get_y() + bars[i].get_height() / 2\n", + " text = (\n", + " f\"${val_musd:,.1f}M | \"\n", + " f\"${val_per_kw:,.0f}/kW | \"\n", + " f\"${val_per_wtg_musd:,.2f}M/WTG\"\n", + " )\n", + " ax.text(\n", + " i,\n", + " y_pos,\n", + " text,\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=8,\n", + " color=\"black\"\n", + " )\n", + "\n", + " # Add total values text on top of bars\n", + " total_musd = df.sum(axis=1)\n", + " for i, total in enumerate(total_musd):\n", + " total_usd = total * 1e6\n", + " total_per_kw = total_usd / capacity_kw\n", + " total_per_wtg_musd = total / num_turbines\n", + " text = (\n", + " f\"Total:\\n\"\n", + " f\"${total:,.1f}M | \"\n", + " f\"${total_per_kw:,.0f}/kW | \"\n", + " f\"${total_per_wtg_musd:,.2f}M/WTG\"\n", + " )\n", + " ax.text(\n", + " i,\n", + " total * 1.005,\n", + " text,\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=9,\n", + " color=\"black\",\n", + " fontweight=\"bold\"\n", + " )\n", + "\n", + " # Format y-axis ticks with commas\n", + " ax.yaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:,.0f}\"))\n", + "\n", + " ax.set_ylabel(\"CapEx ($ Millions)\", fontweight=\"bold\")\n", + " ax.set_xlabel(\"Installation Strategy\", fontweight=\"bold\")\n", + " ax.set_title(\"CapEx Breakdown by Component\")\n", + " ax.set_ylim(0, top_limit)\n", + " ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n", + "\n", + " ax.legend(title=\"CapEx Component\", ncols=5, loc=\"upper center\")\n", + " fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "02f55af5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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RiPCwLEkzEeUuxqBkjEGMQUSUsxh/kjH+MP6Qa2OhJRVoGC8DM8aBu4eHBt6E+HhJSEjQsV7Ms6ttXL1MqlRL7iqAoHPt6kU5e/KIvr9w5oRcPHtKyla4I8U+XpjwqXzxw275bNlOfcHUb36zdjswVKvdUB4b8II82u+520qTcSPg5u6ux4sxZlA7ZQ4siYmJUr9pW5vvoTvAn1t/07+RdgSVClWSZ4/bsfEXadK6k8367Tr3kB4DX5Rxwx/TQGdA7RpqAY0gBbixWTR7arrjl6SXJihfuZoEBhWV6KgbktXW/rhAx7/xCwhy+HnR4qWk99BXpO+wsSk+2/7HSpvBo41zgXQbN3a4eUEXknU/L9TP0oPz90CvIdq15uqlC9blN2OiM5E6InI1jEG3MAYxBhFRzmH8uYXxh/GHXBtnD6cCLTEhXmZNe02ibkToGCcYz2TwfydpRhl67pRMGTNIkpIStWauZNmK8vzr0621cENHvyfvvfqMBkaMnfL0f9+y1kp+9/lkKVqsVIYHCu7Wc/Btp2nx19Pk2uWLcvXyBdm0ZoV0fWyQzXggaPp/z/09UwzajPFovnj/Ffnfwi/1pqVJ6/ukVceH5ezJo+IXEOxw8GaMaYIatzdG9JIxU+bqfmo3bCkXzhyX5u2GW9dDwFo6d7o8MXhUptKErgifTRmtN0m4RnUbt9bBnuG3nxbIt59Nlqgb12XbH6t00OZXpszVG6Jfln4tYVdCrTPTTfxvbzl59KD+PeLJtlp7+eany/R9UlKSrPv5e+s1Ts293VJ2m8DvY8+236Xf8HE2y9ENZPUP32gXEZtz8c1HTt+89H52jPy08Et584UntEsOuuj4FvKTPkNfcer7ROS6GINuYQxiDCKinMP4cwvjD+MPuTY3C35plGERERESFBQk4eHhEhgYKPk1ffPWHL2tZvp5yYIvp0ivp/9P8gKMK9P+/p5p1lRlVXqWzv1Ia2Af7j1MshOOt0Tp8mkOwO2q1wjjtiyePVVefX9ebh9KvoQa5T4d7sy3+S3ZKojxx5XzN0cYg1wLY1D2YfwpeApiDHLVvM0Rxh/XwviTfQpC/IlwskyNLS2J/lWn4a2aoPwgq9Jzu101CsI1ql6nEYM1EeXL/C2zGINyDmMQEeXHvC2zGH9yDuMP5QQWWhL9y5nxNVxF0zZdtEYuv6THCMZ+/kH5Kk1ERM7KS/kbYxARUf6Rl/I2xh+igoeFlpSm7Bjol25f3cat8t31qVK9Xr5LE2UN/iYKJl5318UYRAUFfw8FF6+9a2L8oYKCv4dbWGhJDmFAXi9vH3nmoYa5fShERJofIV+i/I/xh4hcCeNPwcIYRESugvEnGQstySHMqhYfFyulB80Qd+9CuX04RFSAJcXFyIWvhqaY7ZHyJ8YfInIVjD8FD2MQEbkCxp9bWGhJafIMCBF3n8K5fRhEVIAlxUbn9iFQLmD8IaLcxvhTcDEGEVFuYvy5hcW2RERERERERERE5FJYaElEREREREREREQuhd3DiUyub5wvwa162yyzJMTLtXVfSsyJ3eLm4S3eJSpLsQde0s/C1nwm0Ue3SWLEJSk9YLp4l6yS6rbjLp+UsNUzJTHqur4v0qafFK7eUm6e3isXv3tFAho9IEU7DLGuf+WnDyRq/2+pbvfsjIFSbuisDKcnct9aidi+FIP2iIibFGnTVwrd0UQsCXFyecW7En/ltLh5eotH4WAp2mmYeBUpY/1u7IWjcn3DPAnpNEzOz35eKoz83mbbCeGX5PxXQ6X8iAXi5uGly859Nlh8ytWSYve/kLyNc4fl8g+Tpdyw2SmON/Tbl3U9z6CSGUqTs9ch5tRfcn39HLHE3dS0F7qjsQS3GyBubu4Sfz1Urix/WyyWJJGkRPEKKS9FOz8nHr7+khB+Uc7NHCSFqjaTEt1fu3UsG+ZL+ObvpPgjr0rhai10mSUxQc59/oweY9SB36Rkr0nJ5ybiipybMUBCuo4U/7oddFn41kV6vhNjIiQxMkyXxV86IV7FKur1wVhKpXq/q2OaXP/jG4n5Z4deG3FzE+/ilSSodR/xCi6V6rkioryD8Yfxh/GHiHID4w/jD+MPuTIWWhIhkB1YJzd2LJeEyKsSc2y7+DfoLAENuuhn19bP0Qy+zODPxc3NTRIjr1m/V7j63RLYtLuEzh+V5vaT4m/K5SVvSki3F8W3XG2xJCVK0s1I6+eeRcrofi3tB2qwwxgWsWcPiYd/SKbSg6CMNOFYY/7ZKUEtHtegkhhzQ8LWzJSygz8XD/8icvPsAbm87C0p/9x8/V5A/U7iW6WxpjNi149ydeV0KfXkO9btRh/dIoWrNU91v55BJTTYx144oulMiLgsbt6FJPb839Z1cJPiW7Full4jZ68DAnCxB0droMNNysUFY/XGCEHU0z9ESvZ+V9y9fKw3AuEb51tvpNx8/CQ+7JwkRl0TD78iGtyjDq0Xr+KVbPZx8/Q+8SlbQ9MYtvpTsSTG6zVFur1LV9fPjaB989Re8avZRvzrdbR+/9TkblKq92Rx9/XX9xaLRS4tGq83EaUHfqzHh31HH94kCddDGbSJ8jjGH8Yfxh8iyg2MP4w/jD+UF7B7OBV4SXE3NTgVe+hlDQLIuH3L1bF+Frn3Vwlu008DGSDYGXzL1xHPwGLp7iPq4HrxLlNDAxm4uXuIR+Eg6+duXj7iW7G+1pbp+of+0FpIcffIcHrir56V8K2LpWTPiRJwVxcp2WvircCCWjRNV/LAvkk3o8QzIPn4UYOFGkcjnT5lamjNoVnM0W1SuKpt0EZQuvLje3J11cd6M+JToZ4GJsC/hSo31LSits5Y5luhXpZdo4xcB++Sd1iDHNLrXbKy9bjcPL2sARvpsMTH6s2amX/t9hK5/7fkdJzco9szgqsh+uhWKXxnC/EMLKHBHTcwRrqDmneX2POH/z1vCRJ77pD4Vkz7XNw89Zdeh6Idn7UeH2pG/Wq2lkKVGjhx9ojIVTH+MP4kv2f8IaKcxfjD+JP8nvGHXB8LLYnckoMoapAAGaNXsfL6d8L1C+LuGyDhWxbKha9Hak1WzMk9Gd6Fdjnw8JJLiyfI+dnPyZX/vS+J0eE26/jV7SiRe1fr35H7Vot/3Vu1Txni4anB2di+u4+feBUpnfxR4SApet8wTcvZGU/J1ZUfanN9R27sWiGF72x2Kw1h53QWRfNNC2pLLy4cJ17FKkhI5+F6HhGEYk/vvVWrWKGuBlXUqjkbqDJyjTILtbBRf2+SQnc0tbkBwfU5O/1Jib92XoJb23bD8Ktzr0TtX6t/41oZNYbW71sscvPELilUpZG+x80J0g2x5w6Kb6W79BqgKwa6muDvtLqCQNzFY9rdww3XlYjyF8Yfh5th/GH8IaJsxvjjcDOMP4w/5HpYaEkFnruXrxR/dKyEb/xWbuz6US4tnWhtzo8aJ4wTggBRuv80bSp/ZcW71uDhNEuS3Dy1R4p2+o+OOeIRECJhv3xis4pvuZqSGHFZYo7vEjc3D/EKKZep9KAmrWjHoRK26mO5sfsnufzjFK19hKTYKA3Gpft+IOWGzpaQLs/L5WWTNFiZ4SZFg1bb/jZdIwrdaaplTEyQ0HmjxL/uvdr9wpqOCnUl9tzfus3Yswd1PBefCnXl5pl9yYHKL1hr4bLqGmUGup9cWvKGBDXtLj6l77Qux41Vmac+knLPzROvouXkxp6VNt9DbaZHQDGJPrZdg6lv5btsPo+7cES/h5sbQLpjz+yThIhLWiOJMVp8yteV2NP7dDlqZTPq5pn9emOBsXIwvg4R5V2MP4w/BsYfIspJjD+MPwbGH3J1LLQkEtFm5uhG4H/X/eJf5165uPB1HYDaM7A4qrjEr1Y7XQ/N4VEzFHf5VIa27xFQXIMZuiKg+4FfrfYOg45fnXu0FtLPrgYro/xqtZVSfd4V/3r3iW/ZmnJp6Zu6PObEn+Lu42+tpStctZkGMHM3iPBtSyX6yGYp2WOCBktDzBE0+zcFbXcPDcg6Fk1ignUx0oibkqhDG8S9UEByoCpbQ8eoQQ1kRrtGpHeNMhWwF74uhe5sJoFNH3G4DoI3ahGj9q9L8RmWX/15mvjVaKPdFMzsb2yQVtzAxBzfLb7lk8ex0VrX03u1BtKZGlfvEndI3MXj1nOM7+PGwq92O00LEeVtjD+MP2aMP0SUUxh/GH/MGH/IVbHQkgo8zEyGpv/g5u4u3qWraS2aJSlBm69jrJWbJ3br52jWjjFAMloL6FezldayGZlszPGdOgufo4CAQILxOjILtaAJN64mp8fTS9ODGkbwDC4lcZeOWwfTRlcFzBSnNyc6gPUyiT60Xkr0nGgzVklCZJgOpu1VtOytHbm5aZcI1LxdXjpRknQMlFvBKnzzAmugQvDHuYzcvzbjXSPSuUYZ3c6lRePEt0ojCW7Zy+Yz3LggjaCDTP+9UbxK2A4yDRivJbDpo+J/V/Ig2GYxR7fb3Nh4BoSIR2AxHRgcN23gU7am3Dx7UMd2ceYGxrdSA63hDFv7uc05Th5zhojyMsYfxh9NI+MPEeUwxh/GH00j4w/lARwkgAo8dIG4tvYLDWyY7Q2zkhXtOERryABdGjD2ybXf52igwntj8GYMvhzzzw4NlKj5wnfKDvki+bOV06VQ1WY6Lgq6A6ALQei8l7TmErPiIeDZQ9eBoOY9bis9GLQZXS8sCbEaiKKPbJGQzs/rZz6lqupxXFzwitYUYpwUDO6MQZkTIq7ItXVfaWC/+N0Ya41b6X4fSMzRrZoWR4reO1iu//GNXFo8Xkp0Tz4HCFCRf62yBirdd4U6ErF1caa6BKR3jZy9Djd2rtCBoRGcUZsKftVbSVDLnhJ3+YSm498daq2yMXOeGW6Egpo/lmJ5/NUzeqODa2im52Lvaq2VBXdvX10H5xZBPT2omS7RY4Jc3/CNXJj1H3Hz8tX04ToF3uZvhYhyF+MP4w/jDxHlBsYfxh/GH8or3CwYOZUyLCIiQoKCgiQ8PFwCAwMlv6av/MiF1vEpCoLrG+dLcCvbwYdd1dkZA6Xc0Fk5kh4EwuDWfW3GP8kOod++LMXufyHNwZld9RqFb12EuxwJavZobh9KvoMa+jPTHs+3+S3ZYvxxfYw/roXxJ/sw/hQ8BTEGuWre5gjjj2th/Mk+BSH+RDhZplagW1p+8sknMmXKFAkNDZX69evLRx99JE2b3ppJiwoec81YfpBV6Sn5+BviKlz1Gt1uDTERFWyumrdlFuNPzmH8IaL8mLdlFuNPzmH8oZxQYMe0/P777+XFF1+UcePGye7du7XQslOnTnLp0q0BeangyewgybkhsPFD+So9xpg27j5++SpNRETOyEt5G+MPEVH+kZfyNsYfooKnwBZafvDBBzJ48GB56qmnpFatWjJz5kwpXLiwzJqVdnNzIlcR2CT9oJ3XaNA2DYBNRESuh/GHiIhyA+MPUcFTILuHx8XFya5du2TMmOTBdsHd3V06dOggW7Zscfid2NhYfRnQ7x7CwsIkISHBug28kpKS9GXeNl6JiYliHkI0teUeHh46+KyxXfNywPrOLPf09NTtmpdju1jf/hjtl9+4cUO8vLzEEnlVEmKjxcNNxN3t1rYTLSJJFhFPdxHT4lSXJySJIIVedsXkqS2PT0r+vqcTyy3/bgfHh+NMbzmOD8fJNDFNTFPeSFNiXIzmR9euXbPmi9mR7xnyU16eF9N0/fp1vd5uUVfFLT6a/z8xTUwT05RraXKLi9G8CeNuMS8vGGmKjo7W7RgxCPj/E9PENDFNOZ4m0/NPUlJSvszLIyMjk89TOtPsFMiJeM6fPy9ly5aVzZs3S4sWLazLR40aJevXr5dt27al+M748eNlwoQJOXykRERERERERERE+c+ZM2ekXLlyqX5eIFtaZgZaZWIMTANKj9HKMiQkREubiSgltEwoX768ZkT5ddYzIiJyPYw/RESUWxiDiNKH9pPo4VumTJk01yuQhZbFihXTJq0XL160WY73pUqVcvgdHx8ffZkFBwdn63ES5RcI1gzYRESU0xh/iIgotzAGEaUtKCgonTUK6EQ83t7e0qhRI1m7dq1Ny0m8N3cXJyIiIiIiIiIiopxXIFtaArp69+/fXxo3bixNmzaVadOmSVRUlM4mTkRERERERERERLmnwBZa9uzZUy5fviyvv/66hIaGSoMGDWTVqlVSsmTJ3D40onwDQyqMGzcuxdAKRERE2Ynxh4iIcgtjEFHWKZCzhxMREREREREREZHrKpBjWhIREREREREREZHrYqElERERERERERERuRQWWhIREREREREREZFLYaElEaXp5MmT4ubmJnv27NH3v//+u76/fv16bh8aERFloUqVKsm0adPSXAf5//Lly3PsmPKSdu3ayciRI2/7HBMRERFRMhZaEuWQAQMG6MPes88+m+Kz//znP/oZ1nE15cuXlwsXLkidOnXE1UVGRoqXl5csWLDAZnmvXr30/KIA1v7h8bXXXtN/8XlqL+O6GA/rFy9edLgfw6BBg6Rhw4bZmFIicgWhoaEyYsQIqVq1qvj6+krJkiXl7rvvlhkzZkh0dLR1PXMe4+HhIWXKlNF84tq1a9Z1jAoh+9fYsWNT7Det/Aqv8ePHZ1uaEQ+6dOkiOSEmJkaKFi0qxYoVk9jYWCnojPuI1F74neUFLPgmyl9yKxamtq75hXXyQt7+8MMPZ+s+Zs6cKQEBAZKQkJDiuQkVbo7O61dffeXU+Z0zZ44EBwfrd99//30pUqSI3Lx5M8Ux4LcQGBgo06dPz9a0Uv7DQkuiHC4AREEXHsQMyNS//fZbqVChgrgi3FSUKlVKPD09c2X/CIT2wTQ1/v7+0rhx4xQ3KHiPc29efuLECTl16pTcc889smPHDn0Qx2vJkiX6+d9//21d9uGHH9psDzdj999/v8yaNSvFMURFRcnChQv1JoyI8q/jx4/LXXfdJb/++qu89dZb8ueff8qWLVtk1KhR8r///U/WrFljs/4bb7yh+cnp06dl/vz58scff8jzzz+fYrvmvAevl19+OcU65s/Rag8PAeZlL730UobSEhcX5/S6iAc+Pj6SE5Af165dW2rUqMFCLhGNRebrDLNnz7a+RywjIioosbBly5Y26zz++OPSuXNnm2VYJ7/C8xGek5zRvn17LaTcuXOnddmGDRs0pm/bts2mkHHdunX6XNq3b98Mn198B89CS5cuTXEMixcv1vuNPn363Fa6qeBhoSVRDkLrOxSemTNy/I3AgIBvhlYlCOIlSpTQWstWrVrZPJAYtWBr167VgrrChQtr4ECQN0Mt5x133CHe3t5SvXp1+eabb2w+xzawDlrOFCpUSKpUqaJBJbXu4Y5s3LhRWrdurd9H+nDcCFiGTz/9VO68805r7etjjz0m2QVB2Vw4eejQIQ3EQ4cOtVmOv/Hg3aJFCylevLgGbbzQqgdw3o1lQUFBKfaDQkmce9x0mS1atEhrMXv37p1taSSi3Dds2DCtzMEDAG7ka9asqfnnQw89JD/99JM88MADNuujhQPyk7Jly2o+1b9/f9m9e3eK7ZrzHrxQGWPP/DnyJ+TRxnu0pkC8MEPBprkVntGqY9KkSdrSBbHBcOPGDXniiSfEz89Pj/WTTz5JtZWcER8Qx5AmxKH69evrA2tGYkRq0MoDDzd44W8zi8WiLUoRP5GXIx3mB9+04k5SUpK8/fbbUrlyZT0mHLM57hnx9ZdfftHYjHVQwXXp0iVZuXKlXmsUFD/55JM2rYgA+f/w4cP1uqCFKFrz41gdGThwoHTr1s1mWXx8vP4G7NML2Kb52gNatxjvDx48KE2bNtXzUbp0aX3IN1rVoPAA6yYmJup7xHSk0VwQ8PTTT6f7MLl+/fpU95Fa9/cGDRpYW/8av8NHHnkkT7UOJSLXi4V4tjGvg7waeZPxHn8jX0PLP8QnPOscPXpUv4t8Gff/5rwfeRXyNXPswjbs83kztBLt16+fw30A8j5sN7WYjM+//vpr+eGHH7K1dSjiPNJm/yyE64RYuHXrVpvluDbpnV+8sI79dcM1d9SwA8tw72E8axE5i4WWRDkMDyloGWHOwJ966qkU66GGEq1MEMgQzNHlolOnThIWFmaz3quvvqpN8XGzgJsGbN+wbNky7a7x3//+V/bv3y9DhgzRfaEGzQwPVd27d5e//vpLC9vQnRqFfc74559/tNYN39+7d698//33GuTx0AY4LjxIomYVBaqrVq2SNm3aSHZBkDVqZwFpxQM8HjjNgRrLUWCJB9rM6Nq1qz4I29dw4to++uij1m4SRJT/XL16VVuVYGgPFO45ggeP1Jw7d05+/PFHadasmeQWVLogr1y9erUWaBmmTJmihXhoLYMCKcQQrJMWxCG07kRBWLVq1bTQ0yjISi9GpAbfQ+EnHoLxQosQtI43ID5OnTpVPvvsM31AREFq3bp1nYo7KLCcO3euFvAeOHBAXnjhBS2sQ4GcGR4mP/74Y9m8ebOcOXNGjwMPm+gdgYdx/AY++ugjm+8gZiMWb9++XVtGfvDBB/Lll186TCMepnFsRrwCXAs8IPfs2VMyAr8pxKUmTZpoLEdlJAo+J06cqJ+j0BgF0riugLSiUNUcF7EsrZ4N6e3DGUblq9FClK1DifIuV4+FqKBDPFixYoXGExRUIg9D5RCOC3HByANR+IhnH/SGO3z4sDVPRH6HwsjM7MMZiJ32LRizq3UonpHMz4D4G3l+27ZtrcuRfrS8xLqZhYYdv/32m03MRotctKplTzTKFAsR5Yj+/ftbHnroIculS5csPj4+lpMnT+rL19fXcvnyZf0M60BkZKTFy8vLMn/+fOv34+LiLGXKlLG8++67+n7dunVoumFZs2aNdZ2ffvpJl8XExOj7li1bWgYPHmxzHD169LB07drV+h7rP/vsszbrNGvWzDJ06FD9+8SJE7rOn3/+abPfa9eu6ftBgwZZnnnmGZvvb9iwweLu7q7HsWTJEktgYKAlIiIiU+dt9uzZlrZt2zq9flRUlMXb29vy7bffWtOLcxYfH2/x8/OzHD9+XJdXqFDBMmHChBTft0+fGZYvW7bM+v7ll1+2VK5c2ZKUlKTvjx07ZnFzc7O5JkSU/2zdulXzg6VLl9osDwkJ0XwGr1GjRlmXV6xYUfMlLEeej+8inzXnM0beY3zfeF25ciXdPDIoKMj6fty4cZb69evbrDN16lQ9BgNiTcmSJS2xsbE262Gdzp072yzr2bOnpUuXLg7zQSM+fPnll9bPDxw4oMsOHTrkVIxIzSuvvGJ5+OGHre8RI5E2w/vvv2+pVq2axkZ7acWdmzdvWgoXLmzZvHmzzXIc5xNPPJFqfH377bd12T///GNdNmTIEEunTp2s7xGratasaY0JMHr0aF1mPse4HoZatWpZJk+ebH3/wAMPWAYMGGBxhvla4HxVr17dZt+ffPKJxd/f35KYmKjvGzZsaJkyZYr+jXM7adIk/V3euHHDcvbsWd3ekSNHUt2fM/uwTx/g92i+dvaxlIjyJleKheZnLUBehu1s2rTJ+jm2UahQIcvChQv1/fTp0y21a9fWv5cvX67Hgu/PmDFDl3Xo0EHzvdQ4sw9nY7Jx3BmBmIN7AGd98cUXei7xTIT46Onpqc+leGZq06aNrrN27VpN06lTp1J8P7XjtL8PSUhIsJQtW9Ym33/ttdf02cuIFUQZwZaWRDkMXREwHiJa6KGlAf5Gawf7FiaoocMg1gYMlIwuWfYtIOvVq2f92+jSgC5sgHXN2wC8t98GWhzav3e2pSVaWyAt6LZhvNAiFN3vMG5kx44dpWLFitpVBOOcYPyatLpZoLu1eVuYuAgtbMzLMGZOalAbilpRo+bUaDmCli+oucRy1PZhP7dTiwho1Yo0GrWTuJ7o7oFWnURU8KB1HVobYhxG+4lj/u///k8/Q2tDtHIE5P9Gd10D8jusZ7zQ5Qzs88XbhVZIcFI1AAASpUlEQVSJ9t26MhsP0opD6cUIR3BO0GLR3FUZf2M7+B706NFDW4QgtgwePFh7FhitO9OKO8eOHdO/sY75mNDyErE3tXShZT3iC7ZpXmak09C8eXOblkU4f2gJan+dza0tjd4XmOQN3c/NPSachWuEfZn3jXiPMczOnj2r79GaBjEQ5Yb4naFXALpyouUrYiW62KNLfWq/N2f2QUSUnbHQWcivcO9vbsUZEhKi3aSNmIY8EcNqXL582fq8gBfySTyHoZW90foc+aA5X3R2H1kJzz/mY8A5sj8u+2GrzJAWDM2CFu74LnpG4LkU58EY1xJpR5y7nbkWMB8Cuv0jZiPeIG4jpqO3n7s7i58o43JnZg2iAg4PJEbXOPvxwjIKhZkG40HCeKjLCXhYQbdzR4NoI+DhoRjd2xEE0YXk9ddf1y53CJiOulDjock8fibGSkM3QDx0GtIbCwWFkeiCiG5/eKg1ZvI2uj/g/ODh83a7o+DhDl3u8MCJGwE89OLhOa2uMESU92G4Dvx/bj+GsFGghXGf7KFyCt8z8g50M0YBEPKkDh06WNfD2FKO8kZzvojxFFODBwL7MRQddVNLrStfVseh9GKEIxhLEt0G7btI46EWD7kocMTYmDj/mOQB3dcxrhq6tuPBE2OmpRZ3cDyA7t0YU83MfoIh+3SZ3xvLbjfeYiw0dMNHt0I8IOP6I65kB8QpDEmDgmSkBRMcGQ/o6BqJGJnR31tmf39ElPflRizMSqi8wzMF4gZeGOcZ4zROnjxZ4wXyLqOrNoYbyegkd1mdJ6KAEl3JDRjSC0OvoALK/ByVGpz3cuXK6bk25/n4DmIqYhA+y4rGF3jWxVAs6CaOOIkhVhwNh0bkDBZaEuUCjFuC2dMQ6NHixJ4xcc6mTZu0tYgR4BBAR44c6fR+0IIC20BtlwHva9WqZbMeBl/Gg5P5vf3EQKlBgSBqKY0bEEdQC4kbEbzGjRunNyEIYuYga17XvC0M6IybnrS276jQEmNsYdwxjGeJGj/A2DWff/653jygdYijVkYZhbFZMMnPgw8+qA/ZGNuGiPI3tKRAwRnGO3zuuecyVQBo5EuoWHGGs3kgWk2EhoZqPmcUIKY1kZo982D8xnvEksxyJkbYwziJGFsZY2Wa4YESn+HcA2IDBvzHC2OqoRBu3759us/U4g6+i8JJtEYxF9JlFbRWsT9/eDA3rrej3xImJkDlFwouM/tQh2uECj7zdUe8RwEuHlLN41piLFAj7Si0fOedd/QBFuNfGxxdL2f2gd+feYzOiIiIFC1qUWCaWstTIso7ciMWOgv5FVrfI082Ch4xBicKWI3nIORjyBcxCQ4aOuCZAY0a0DoU4yVjolMjTXgewSuj+3AmJuN5xJk8EQWs5oYbiIE4pow+IxkVVWj1asAzElr6o5UsnmtuF55lEWdQUYa0IxYbz7REGcVCS6JcgABtdBtw9CCDAImAgWCC4ITWKO+++652acvIAMb4PmrkUACJYIHBrtFyES1T7Ge8RmBGsEaLRgQsRzOXOjJ69GjtDoeWo+jmhmPHAypavuAmBpMKoDs2giG6dvz8889a42aerTar4cYBD6WYIMH80Ivu9ejKh5uTMWPGZMm+0EURLYjQkui+++7Tmkoiyv8wOzUqP5B3ohUfuhKjRQUqlzCIf6NGjWzWR2GR8eCCFgeYbA0PM1k94D4KodDVDTEDM2Zjohc8iDjbWg6FUPguCtKQjyM+oFViZqUXI+zh2BGrMKlBnTp1bD5D5RpmncaEdPgcD3loMY+HzHnz5ukDHB6K0oo7KGBDaxlMvoNliHvh4eGabpwjcyVfZqAw9MUXX9SYgNaeiEOYLC8tOC+YRRzpyez+0dIULZZQcIBzjYdmFNbiWIzueDgX+J0izhvnHucI9wmoGE2vENeZfaCFDroEoiAZBcVo5Wp/n4NhVNBiFv//IFZntNsnEbkOV42FqCzCzNjoAYUCSOT9aNWOFvZYbo6ZqLDB8RvdvpEvIp80F+pldh/OxGTkiehhgDwVBcFBQUEpWvZnFRRaopLPPs/H38jX0ajmdofPMuCZFecG7CcuJcqQDI2ASUSZlt4gy+aJeAATFDz33HOWYsWK6cQ9d999t2X79u1pThiDyXKwDJMjGD799FNLlSpVdGIfTFowd+5cm/1ifQyk37FjR91PpUqVLN9//7318/Qm4gEcF76PwfgxwHO9evV0gH9jwgUMFF2kSBEdmBqfmbef1RPxGPAdHCcGCTdr166dLt+yZYvD72VkIh4DJpnAZ8ag20RUMJw/f94yfPhwnZALeSzywKZNm+pkJ5gUzIAB95FHGK/ixYvrhGhGvppe3pMW+wHwAZMIlC9fXvPjfv36aX7szKD/WAcTlGECM0xWU6pUKcuHH35os46jiXjM6cDxYxnS40yMsPfee+9ZgoODHU6wg4mD8BmOCceASRMw4Q622bx5c+vEOenFHUwkM23aNJ1UBtcN1wMT6qxfvz7Va+HoPNtPsIB9Dhs2TCe3w3Fh/5jEwTxxjaOJavA5lpsnyXOGfUz6/fffLU2aNNGJLnDtMAkQJlwwGzFihM1ESYA0YH1npLeP8PBwnbwJ6cdvcM6cOSkm4lmxYoWlatWqOgmE+XdJRHmTK8RCR7EtLCzM0rdvX827EQuQz9tPNmY8OyEvMyCPxrJVq1alu09n9pFeTMZkOEaMtI+fWTkRjzlu16hRw2Y5JofFcsTF1Dg7EY8hOjpalxctWlQnwSPKLDf8J2PFnESUn6CrAiYwQKsaIiIiylkYZxMtc9BF3NGwKUREREQFFbuHExERERHlMHRPv3LlinYfR1dqjI1MRERERLew0JKIiIiIKIdh/EvMkItJbDDeFyYPIiIiIqJb2D2ciIiIiIiIiIiIXEryVH9ERERERERERERELoKFlkRERERERERERORSWGhJRERERERERERELoWFlkRERERERERERORSWGhJRERERERERERELoWFlkRERESUIePHjxc3NzepVKmSdRn+xjJ8lhVOnjyp28Pr999/z5JtEhEREVHewUJLIiIiIhfUrl27FAWDWWXOnDnWAkFXLAAFHx8fadasmb4CAwNz7HguXLggQ4YMkcqVK+sxhISESL169aRv374SHR2dbYW0Ztm5bSIiIqK8wjO3D4CIiIiIyF7p0qVl69atObrPpKQkuf/+++XPP/8UT09PqVWrlsTExMiRI0dk3759MnXqVClcuHCGt5uYmKj/enh4ZMNRExEREeVPbGlJRERElMdaX/br10/GjRunBXtFihSRPn36yI0bN6zr/fzzz9KiRQsJDg7WQraqVatKz5495dq1azJgwAB56qmnrOsaLS6NVn0vv/yy1K5dW7/r5eUlZcqUkf79+2sLxIxAYd/DDz+sLRb9/Py01eKdd94pr7/+usTFxVnTM2HCBP371KlT1mNBS9DUuodv3LhROnXqJEFBQbrNmjVrypQpU6wFg+aWiqNHj5bhw4dra8kSJUrIiBEjJCEhIdVjRuEkCizh119/lb/++kuXXb9+XZYtW6bpMI4Lxws4fnOrVXPL0blz58odd9wh3t7ecubMGVm9erW0bt1ajwXL0IIU71euXKnfTW/bsG3bNunatateH19fX2nYsKEsXrzYJh2nT5/Wc4TPq1WrJkuXLrWeE1x/FM4a78eOHWv9XlhYmF5zLF+wYEGGrjcRERFRVmOhJREREVEegwIltPorVKiQFqjNnz9f3nnnHf3s8uXL8sgjj2grRRTsoaDw6tWrsnDhQgkPD9dCtCpVqli3ZXTBLleunL5ftWqVnDt3TsqXL6+FnaGhoVr49tBDD2XoGGNjY+WHH37QwksUnKGg7tixY/Lmm2/Kq6++quugJWPZsmX1bxTiGcdSvHhxh9tE4WX79u21QBGtFitWrCiHDx+WUaNGybPPPptifZyj7777Ts8Tzsv06dNl9uzZqR4zCvMMX3/9tWzYsEEiIyO18A8FsNiO0W0dxws4fuO4zc6fP68FhGixWbJkSV124MABLXQMCAiQOnXqiMVi0ULYBx98UAtI09v2pk2brIWcOBYUPKKQtUePHnqNANt89NFH9RzFx8fr/tG1HdfR4O7uLs8884w1nUa6V6xYoYW6+N0gvURERES5ykJERERELqdt27YW3KpVrFgxxbKAgADL2bNnLYmJiZZGjRrpsmbNmuk6O3futK4THR2ty5KSkizbt2+3REZG6vvZs2frOo5uBffu3avbNXzxxRfWdY8dO6bLxo0bl+LY8DeW4TOIi4uzHDhwwGbbffr00XXKlStnXeZoW3DixAnrftetW6fL2rRpY1332rVrumzEiBG6zM3NzfLPP//YHEvlypUt169ft8TExFjKlCmjy3r27JnqOcd5at++vXW/eHl4eFhatGhhmT9/vs269um1Tw9eM2bMsG4X5/TkyZPW44awsDC9Tlh37Nix6W67Xbt2urxjx46W+Ph4XTZy5Eibc7pmzRrr/j/++OMUy/r376/LQkNDLV5eXrps5cqVuqxbt276/umnn071HBERERHlFLa0JCIiIspj7rnnHm2FhxZzNWrU0GUXL17Uf9G1Gy0p0V0crRvRfRgt/tC9G92b07Nnzx5p0qSJ+Pv7azfhwYMH27QedBaObd68edrKEi0IsS28z+h2zHbs2KH/Gt2j4cknn7S2MNy1a5fN+mjBiFaDaCmJburm8+QIjhEtTdFC8+6779au0uh2vmXLFundu7d2W3cWWkIarRmxXZwPtD7FtcB1QUvRokWLWrv1O3NOtm/frv+im7nRjXvatGm67OzZs9pCFq05DY8//rj+e++99+q+zND6Ey0yYdasWXoc2C5gOAAiIiKi3MZCSyIiIqI8xiiwA3T/NQrtAAV0KLxDV2ijS/c333yjfy9atCjN7aKrMgqsdu/erdtB4SXGjDSYx41MD7qrv/3223L06FEdexNdnI2u4OZu2Ll1nlKDrtkjR47UcxEREaFdplGAC8uXL3d63+jijoJKM0zygy7zGFu0bt2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Monopiles + WTIVGBF-Turbine Assembly Tow-outGBF Tow-out + WTIV
CapEx Component
Substructure Installation53,356,40353,289,84736,496,243
Turbine Installation95,293,403095,293,403
Total148,649,80753,289,847131,789,647
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" + ], + "text/plain": [ + " Monopiles + WTIV GBF-Turbine Assembly Tow-out \\\n", + "CapEx Component \n", + "Substructure Installation 53,356,403 53,289,847 \n", + "Turbine Installation 95,293,403 0 \n", + "Total 148,649,807 53,289,847 \n", + "\n", + " GBF Tow-out + WTIV \n", + "CapEx Component \n", + "Substructure Installation 36,496,243 \n", + "Turbine Installation 95,293,403 \n", + "Total 131,789,647 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sub_turb_install_df = df.loc[[\"Substructure Installation\", \"Turbine Installation\"]]\n", + "sub_turb_install_df.loc[\"Total\"] = sub_turb_install_df.sum(axis=0)\n", + "sub_turb_install_df" + ] + }, + { + "cell_type": "markdown", + "id": "c1aa5a26", + "metadata": {}, + "source": [ + "### Comparing Installation Timing\n", + "\n", + "Now we can compare the actual installation phase timing to demonstrate the effects of using\n", + "differing numbers of vessels and installation strategies. Notice that for both Case 1 and 3\n", + "the installations all start at the same date, which is likely unrealistic in practice as each\n", + "stage cannot happen at a single turbine at the same time, nor can the installation of scouring\n", + "protection be installed prior to the monopiles being installed." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "454a1a05", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 4))\n", + "ax = fig.add_subplot(111)\n", + "\n", + "cases = [\"Monopiles + WTIV\", \"GBF-Turbine Assembly Tow-out\", \"GBF Tow-out + WTIV\"]\n", + "case1_df = pd.DataFrame.from_dict(case1_project.phase_dates).T\n", + "case1_df = case1_df.loc[[\"MonopileInstallation\", \"ScourProtectionInstallation\", \"TurbineInstallation\"]]\n", + "case1_df.start = pd.to_datetime(case1_df.start)\n", + "case1_df.end = pd.to_datetime(case1_df.end)\n", + "case1_df = case1_df.rename(index={ix: f\"{cases[0]}\\n{ix}\" for ix in case1_df.index})\n", + "\n", + "case2_df = pd.DataFrame.from_dict(case2_project.phase_dates).T\n", + "case2_df = case2_df.loc[[\"GravityBasedInstallation\"]]\n", + "case2_df.start = pd.to_datetime(case2_df.start)\n", + "case2_df.end = pd.to_datetime(case2_df.end)\n", + "case2_df = case2_df.rename(index={ix: f\"{cases[1]}\\n{ix}\" for ix in case2_df.index})\n", + "\n", + "case3_df = pd.DataFrame.from_dict(case3_project.phase_dates).T\n", + "case3_df = case3_df.loc[[\"GravityBasedInstallation\", \"TurbineInstallation\"]]\n", + "case3_df.start = pd.to_datetime(case3_df.start)\n", + "case3_df.end = pd.to_datetime(case3_df.end)\n", + "case3_df = case3_df.rename(index={ix: f\"{cases[2]}\\n{ix}\" for ix in case3_df.index})\n", + "\n", + "ax.barh(y=case3_df.index, width=case3_df.end - case3_df.start, left=case3_df.start);\n", + "ax.barh(y=case2_df.index, width=case2_df.end - case2_df.start, left=case2_df.start);\n", + "ax.barh(y=case1_df.index, width=case1_df.end - case1_df.start, left=case1_df.start);\n", + "\n", + "ax.grid(axis=\"x\")\n", + "ax.set_axisbelow(True)\n", + "ax.set_xlim(pd.to_datetime(\"2009-10-21\"), pd.to_datetime(\"2010-07\"))\n", + "fig.tight_layout()" + ] + } + ], + "metadata": { + "jupytext": { + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.19.1" + } + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "source_map": [ + 12, + 29, + 51, + 59, + 66, + 78, + 85, + 91, + 100, + 106, + 119, + 215, + 217, + 221, + 225, + 235 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/introduction.ipynb b/examples/introduction.ipynb new file mode 100644 index 00000000..046da2aa --- /dev/null +++ b/examples/introduction.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "df44d4ab", + "metadata": {}, + "source": [ + "(intro-tutorial)=\n", + "# ORBIT Introduction\n", + "\n", + "ORBIT's CapEx modeling is comprised of the both design and installation models for a variety of\n", + "offshore wind turbine subsystems. As such, the model's core functionality are split into the\n", + "`design` and `install` model classes. The design classes are intended to model the sizing and cost\n", + "of offshore wind components and the installation modules simulate the installation of these\n", + "subcomponents in a discrete event simulation framework. This tutorial will walk through the basics\n", + "of modeling the design and then installation of the monopile, leading to the introduction of the\n", + "`ProjectManger` to orchestrate the design and installation of multiple turbine subsystems.\n", + "\n", + "To get started, we first import the required imports that will be used in this demonstration." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "19082169", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "from copy import deepcopy\n", + "from pprint import pprint\n", + "\n", + "from ORBIT import ProjectManager, load_config, save_config\n", + "from ORBIT.phases.design import MonopileDesign, design_phases\n", + "from ORBIT.phases.install import MonopileInstallation, install_phases" + ] + }, + { + "cell_type": "markdown", + "id": "e3b4d3bf", + "metadata": {}, + "source": [ + "While this introduction will focus on the monopile design and installation to highlight working with\n", + "ORBIT, it should be noted that there are both fixed and floating substructure models. Below is an\n", + "easy way to check what models are available in ORBIT." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "65b015d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['MonopileDesign',\n", + " 'ScourProtectionDesign',\n", + " 'SparDesign',\n", + " 'SemiSubmersibleDesign',\n", + " 'MooringSystemDesign',\n", + " 'ArraySystemDesign',\n", + " 'CustomArraySystemDesign',\n", + " 'ElectricalDesign',\n", + " 'ExportSystemDesign',\n", + " 'OffshoreSubstationDesign',\n", + " 'OffshoreFloatingSubstationDesign']\n" + ] + } + ], + "source": [ + "pprint(design_phases)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6a818483", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['MonopileInstallation',\n", + " 'JacketInstallation',\n", + " 'ScourProtectionInstallation',\n", + " 'GravityBasedInstallation',\n", + " 'MooredSubInstallation',\n", + " 'MooringSystemInstallation',\n", + " 'TurbineInstallation',\n", + " 'ArrayCableInstallation',\n", + " 'ExportCableInstallation',\n", + " 'OffshoreSubstationInstallation',\n", + " 'FloatingSubstationInstallation']\n" + ] + } + ], + "source": [ + "pprint(install_phases)" + ] + }, + { + "cell_type": "markdown", + "id": "232ad20c", + "metadata": {}, + "source": [ + "## Configuration Basics\n", + "\n", + "Each model has a property `expected_config` that provides basic information about the required and\n", + "optional inputs for the model. Notice that for each input there is a provided data type, an\n", + "indication if the parameter is optional, and any nested dictionary configurations are fully mapped\n", + "in the same way as individal parameters. Below, we can see the expected configurations for both\n", + "the monopile design and installation classes. It should be noted that when combining complimentary\n", + "design and installation phases for a component, that the design model will provide most of the\n", + "installation inputs as a `design_result` (more details in the `ProjectManager` introduction)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "31de88c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'monopile_design': {'air_density': 'kg/m3 (optional)',\n", + " 'load_factor': 'float (optional)',\n", + " 'material_factor': 'float (optional)',\n", + " 'monopile_density': 'kg/m3 (optional)',\n", + " 'monopile_modulus': 'Pa (optional)',\n", + " 'monopile_steel_cost': 'USD/t (optional)',\n", + " 'monopile_tp_connection_thickness': 'm (optional)',\n", + " 'soil_coefficient': 'N/m3 (optional)',\n", + " 'tp_steel_cost': 'USD/t (optional)',\n", + " 'transition_piece_density': 'kg/m3 (optional)',\n", + " 'transition_piece_length': 'm (optional)',\n", + " 'transition_piece_thickness': 'm (optional)',\n", + " 'turb_length_scale': 'm (optional)',\n", + " 'weibull_scale_factor': 'float (optional)',\n", + " 'weibull_shape_factor': 'float (optional)',\n", + " 'yield_stress': 'Pa (optional)'},\n", + " 'plant': {'num_turbines': 'int'},\n", + " 'site': {'depth': 'm', 'mean_windspeed': 'm/s'},\n", + " 'turbine': {'hub_height': 'm',\n", + " 'rated_windspeed': 'm/s',\n", + " 'rotor_diameter': 'm'}}\n" + ] + } + ], + "source": [ + "pprint(MonopileDesign.expected_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4b139d6a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'feeder': 'dict | str (optional)',\n", + " 'monopile': {'deck_space': 'm2',\n", + " 'diameter': 'm',\n", + " 'length': 'm',\n", + " 'mass': 't',\n", + " 'unit_cost': 'USD'},\n", + " 'monopile_supply_chain': {'enabled': '(optional, default: False)',\n", + " 'num_substructures_delivered': 'int (optional: '\n", + " 'default: 1)',\n", + " 'substructure_delivery_time': 'h (optional, '\n", + " 'default: 168)',\n", + " 'substructure_storage': 'int (optional, default: '\n", + " 'inf)'},\n", + " 'num_feeders': 'int (optional)',\n", + " 'plant': {'num_turbines': 'int'},\n", + " 'port': {'monthly_rate': 'USD/mo (optional)',\n", + " 'name': 'str (optional)',\n", + " 'num_cranes': 'int (optional, default: 1)'},\n", + " 'site': {'depth': 'm', 'distance': 'km'},\n", + " 'transition_piece': {'deck_space': 'm2', 'mass': 't', 'unit_cost': 'USD'},\n", + " 'turbine': {'hub_height': 'm'},\n", + " 'wtiv': 'dict | str'}\n" + ] + } + ], + "source": [ + "pprint(MonopileInstallation.expected_config)" + ] + }, + { + "cell_type": "markdown", + "id": "04a0f4cd", + "metadata": {}, + "source": [ + "### Design Models\n", + "\n", + "Design phase modules in ORBIT are intended to capture broad scaling trends for offshore wind\n", + "components and do not represent the required fidelity of a full engineering design. Please see NLR's\n", + "[WISDEM](https://github.com/NLRWindSystems/WISDEM/) if a higher fidelity turbine design model\n", + "is required.\n", + "\n", + "For the sake of illustration we will provide only the required inputs, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "91279471", + "metadata": {}, + "outputs": [], + "source": [ + "# Filling out the config for a simple fixed bottom project:\n", + "design_config = {\n", + " \"site\": {\n", + " \"depth\": 25,\n", + " \"mean_windspeed\": 9.5,\n", + " },\n", + " \"plant\": {\n", + " \"num_turbines\": 50,\n", + " },\n", + " \"turbine\": {\n", + " \"rotor_diameter\": 220,\n", + " \"hub_height\": 120,\n", + " \"rated_windspeed\": 13,\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "a0ce740f", + "metadata": {}, + "source": [ + "Similar to `expected_config`, every design and installation model contains a `run` method that runs\n", + "the design or installation simulation logic." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "24a67f3e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ORBIT library intialized at '/Users/rhammond/GitHub_Public/ORBIT/library'\n", + "Total Substructure Cost: $386.6 M\n", + "{'monopile': {'deck_space': np.float64(54.48035485737655),\n", + " 'diameter': np.float64(7.381080873244551),\n", + " 'embedment_length': np.float64(28.73789461200757),\n", + " 'length': np.float64(63.737894612007565),\n", + " 'mass': np.float64(1015.3457502626202),\n", + " 'moment': np.float64(12.250526562877912),\n", + " 'thickness': np.float64(0.08016080873244551),\n", + " 'unit_cost': np.float64(3691797.147954887)},\n", + " 'transition_piece': {'deck_space': np.float64(56.87275152687858),\n", + " 'diameter': np.float64(7.541402490709443),\n", + " 'length': 25,\n", + " 'mass': np.float64(406.9956472415284),\n", + " 'thickness': np.float64(0.08016080873244551),\n", + " 'unit_cost': np.float64(4039838.794519411)}}\n" + ] + } + ], + "source": [ + "monopile_design = MonopileDesign(design_config)\n", + "monopile_design.run()\n", + "print(f\"Total Substructure Cost: ${monopile_design.total_cost / 1e6:,.1f} M\")\n", + "pprint(monopile_design.design_result)" + ] + }, + { + "cell_type": "markdown", + "id": "8e6fa89f", + "metadata": {}, + "source": [ + "### Incomplete or Incorrect Configurations\n", + "\n", + "If a required input is missing, an error message will be raised with the input and it's location\n", + "within the configuration. This error message used dot-notation to show the structure of the\n", + "dictionary. Each \".\" represents a lower level in the dictionary such that `site.depth` means the \"site\" subdictionary is missing the \"depth\" key, value pair.\n", + "\n", + "In the example below, the `site` inputs have been removed. The following inputs will be missing:\n", + "`[\"site.depth\", \"site.mean_windspeed\"]`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6dc31de4", + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [ + { + "ename": "MissingInputs", + "evalue": "Input(s) '['site.depth', 'site.mean_windspeed']' missing in config.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mMissingInputs\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m config_error = deepcopy(design_config)\n\u001b[32m 2\u001b[39m _ = config_error.pop(\u001b[33m\"\u001b[39m\u001b[33msite\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m failed_monopile_design = \u001b[43mMonopileDesign\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_error\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/GitHub_Public/ORBIT/ORBIT/phases/design/monopile_design.py:76\u001b[39m, in \u001b[36mMonopileDesign.__init__\u001b[39m\u001b[34m(self, config, **kwargs)\u001b[39m\n\u001b[32m 67\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 68\u001b[39m \u001b[33;03mCreates an instance of MonopileDesign.\u001b[39;00m\n\u001b[32m 69\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 72\u001b[39m \u001b[33;03mconfig : dict\u001b[39;00m\n\u001b[32m 73\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 75\u001b[39m config = \u001b[38;5;28mself\u001b[39m.initialize_library(config, **kwargs)\n\u001b[32m---> \u001b[39m\u001b[32m76\u001b[39m \u001b[38;5;28mself\u001b[39m.config = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvalidate_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 77\u001b[39m \u001b[38;5;28mself\u001b[39m._design = \u001b[38;5;28mself\u001b[39m.config.get(\u001b[33m\"\u001b[39m\u001b[33mmonopile_design\u001b[39m\u001b[33m\"\u001b[39m, {})\n\u001b[32m 79\u001b[39m \u001b[38;5;28mself\u001b[39m._outputs = {}\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/GitHub_Public/ORBIT/ORBIT/phases/base.py:115\u001b[39m, in \u001b[36mBasePhase.validate_config\u001b[39m\u001b[34m(self, config)\u001b[39m\n\u001b[32m 112\u001b[39m missing = \u001b[38;5;28mself\u001b[39m._check_keys(expected, config)\n\u001b[32m 114\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m missing:\n\u001b[32m--> \u001b[39m\u001b[32m115\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m MissingInputs(missing)\n\u001b[32m 117\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 118\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m benedict(config)\n", + "\u001b[31mMissingInputs\u001b[39m: Input(s) '['site.depth', 'site.mean_windspeed']' missing in config." + ] + } + ], + "source": [ + "config_error = deepcopy(design_config)\n", + "_ = config_error.pop(\"site\")\n", + "\n", + "failed_monopile_design = MonopileDesign(config_error)" + ] + }, + { + "cell_type": "markdown", + "id": "1fb32df5", + "metadata": {}, + "source": [ + "### Optional Inputs\n", + "\n", + "Optional inputs can be provided as they are available or desired in place of ORBIT's\n", + "defaults. In general ORBIT's default values are updated on annual basis to align with\n", + "the last complete year of inflationary data and commodity price indices. These values\n", + "also align with the annual [NLR Cost of Wind Energy Review](https://github.com/NatLabRockies/AnnualReportingWind/)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b1a77dd3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Substructure Cost: $269.26 M\n", + "{'monopile': {'deck_space': np.float64(54.48035485737655),\n", + " 'diameter': np.float64(7.381080873244551),\n", + " 'embedment_length': np.float64(28.73789461200757),\n", + " 'length': np.float64(63.737894612007565),\n", + " 'mass': np.float64(1015.3457502626202),\n", + " 'moment': np.float64(12.250526562877912),\n", + " 'thickness': np.float64(0.08016080873244551),\n", + " 'unit_cost': np.float64(3553710.1259191707)},\n", + " 'transition_piece': {'deck_space': np.float64(56.87275152687858),\n", + " 'diameter': np.float64(7.541402490709443),\n", + " 'length': 25,\n", + " 'mass': np.float64(406.9956472415284),\n", + " 'thickness': np.float64(0.08016080873244551),\n", + " 'unit_cost': np.float64(1831480.4125868778)}}\n" + ] + } + ], + "source": [ + "design_config = {\n", + " \"site\": {\n", + " \"depth\": 25,\n", + " \"mean_windspeed\": 9.5,\n", + " },\n", + " \"plant\": {\n", + " \"num_turbines\": 50,\n", + " },\n", + " \"turbine\": {\n", + " \"rotor_diameter\": 220,\n", + " \"hub_height\": 120,\n", + " \"rated_windspeed\": 13,\n", + " },\n", + "\n", + " # Overriding of the design cost defaults, both in $USD/tonne\n", + " \"monopile_design\": {\n", + " \"monopile_steel_cost\": 3500,\n", + " \"tp_steel_cost\": 4500,\n", + " }\n", + "}\n", + "\n", + "monopile_design = MonopileDesign(design_config)\n", + "monopile_design.run()\n", + "print(f\"Total Substructure Cost: ${monopile_design.total_cost / 1e6:,.2f} M\")\n", + "pprint(monopile_design.design_result)" + ] + }, + { + "cell_type": "markdown", + "id": "32ed5f7d", + "metadata": {}, + "source": [ + "### Overriding Values from the Design Phase\n", + "\n", + "In the example above, the `MonopileDesign` phase will produce the input parameters \"monopile and\n", + "\"transition_piece\". It is also possible to supply some of the values for these designs if they are\n", + "known, and let `MonopileDesign` fill in the rest. For example, if the user knows the dimensions of\n", + "the monopile but not the transition piece, the \"monopile\" dictionary can be added to the project config above:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d3e137b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'monopile': {'deck_space': np.float64(54.48035485737655),\n", + " 'diameter': np.float64(7.381080873244551),\n", + " 'embedment_length': np.float64(28.73789461200757),\n", + " 'length': np.float64(63.737894612007565),\n", + " 'mass': np.float64(1015.3457502626202),\n", + " 'moment': np.float64(12.250526562877912),\n", + " 'thickness': np.float64(0.08016080873244551),\n", + " 'unit_cost': np.float64(3691797.147954887)},\n", + " 'transition_piece': {'deck_space': np.float64(56.87275152687858),\n", + " 'diameter': np.float64(7.541402490709443),\n", + " 'length': 25,\n", + " 'mass': np.float64(406.9956472415284),\n", + " 'thickness': np.float64(0.08016080873244551),\n", + " 'unit_cost': np.float64(4039838.794519411)}}\n" + ] + } + ], + "source": [ + "design_config_custom = {\n", + " \"site\": {\n", + " \"depth\": 25,\n", + " \"mean_windspeed\": 9.5,\n", + " },\n", + " \"plant\": {\n", + " \"num_turbines\": 50,\n", + " },\n", + " \"turbine\": {\n", + " \"rotor_diameter\": 220,\n", + " \"hub_height\": 120,\n", + " \"rated_windspeed\": 13,\n", + " },\n", + " \"monopile\": {\n", + " \"type\": \"Monopile\",\n", + " \"mass\": 800,\n", + " \"length\": 100,\n", + " },\n", + "}\n", + "\n", + "monopile_design = MonopileDesign(design_config_custom)\n", + "monopile_design.run()\n", + "monopile_design_result = monopile_design.design_result\n", + "pprint(monopile_design_result)" + ] + }, + { + "cell_type": "markdown", + "id": "c72b6d4c", + "metadata": {}, + "source": [ + "### Installation Phases\n", + "\n", + "ORBIT's installation phases tend to require more inputs and provide implicit pathways to model\n", + "installation strategies. For instance, in the monopile installation, we can provide a \"wtiv\" vessel\n", + "for a single WTIV installation strategy or provide a \"feeder\" configuration with \"num_feeders\"\n", + "to model barges ferrying components to and from the site while a WTIV installs the turbines.\n", + "Additionally, supply chains and ports can be configured to model component availability and port\n", + "logistics.\n", + "\n", + "Using the output from the above example, we can add further configurations. Note that ORBIT provides\n", + "a series of default vessls in `library/vessels/` to support all possible installation strategies.\n", + "For more details on vessel configurations, please see the [vessels section](#vessels)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "648a6896", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Installation Cost: $33.97 M\n", + "{\n", + " \"feeder\": {\n", + " \"crane_specs\": {\n", + " \"max_lift\": 500\n", + " },\n", + " \"jacksys_specs\": {\n", + " \"leg_length\": 85,\n", + " \"max_depth\": 40,\n", + " \"max_extension\": 60,\n", + " \"speed_above_depth\": 0.5,\n", + " \"speed_below_depth\": 0.5\n", + " },\n", + " \"storage_specs\": {\n", + " \"max_cargo\": 12000,\n", + " \"max_deck_load\": 8,\n", + " \"max_deck_space\": 1000\n", + " },\n", + " \"transport_specs\": {\n", + " \"max_waveheight\": 2.5,\n", + " \"max_windspeed\": 20,\n", + " \"transit_speed\": 6\n", + " },\n", + " \"vessel_specs\": {\n", + " \"day_rate\": 93692\n", + " }\n", + " },\n", + " \"monopile\": {\n", + " \"deck_space\": 54.48035485737655,\n", + " \"diameter\": 7.381080873244551,\n", + " \"embedment_length\": 28.73789461200757,\n", + " \"length\": 63.737894612007565,\n", + " \"mass\": 1015.3457502626202,\n", + " \"moment\": 12.250526562877912,\n", + " \"thickness\": 0.08016080873244551,\n", + " \"unit_cost\": 3691797.147954887\n", + " },\n", + " \"num_feeders\": 2,\n", + " \"plant\": {\n", + " \"num_turbines\": 50\n", + " },\n", + " \"site\": {\n", + " \"depth\": 25,\n", + " \"distance\": 70,\n", + " \"mean_windspeed\": 9.5\n", + " },\n", + " \"transition_piece\": {\n", + " \"deck_space\": 56.87275152687858,\n", + " \"diameter\": 7.541402490709443,\n", + " \"length\": 25,\n", + " \"mass\": 406.9956472415284,\n", + " \"thickness\": 0.08016080873244551,\n", + " \"unit_cost\": 4039838.794519411\n", + " },\n", + " \"turbine\": {\n", + " \"hub_height\": 120,\n", + " \"rated_windspeed\": 13,\n", + " \"rotor_diameter\": 220\n", + " },\n", + " \"wtiv\": {\n", + " \"crane_specs\": {\n", + " \"max_hook_height\": 100,\n", + " \"max_lift\": 1200,\n", + " \"max_windspeed\": 15\n", + " },\n", + " \"jacksys_specs\": {\n", + " \"leg_length\": 110,\n", + " \"max_depth\": 75,\n", + " \"max_extension\": 85,\n", + " \"speed_above_depth\": 1,\n", + " \"speed_below_depth\": 2.5\n", + " },\n", + " \"storage_specs\": {\n", + " \"max_cargo\": 8000,\n", + " \"max_deck_load\": 15,\n", + " \"max_deck_space\": 4000\n", + " },\n", + " \"transport_specs\": {\n", + " \"max_waveheight\": 3,\n", + " \"max_windspeed\": 20,\n", + " \"transit_speed\": 10\n", + " },\n", + " \"vessel_specs\": {\n", + " \"day_rate\": 400000,\n", + " \"mobilization_days\": 7,\n", + " \"mobilization_mult\": 1\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "install_config = deepcopy(monopile_design_result)\n", + "install_config[\"wtiv\"] = \"example_wtiv\"\n", + "install_config[\"feeder\"] = \"example_feeder\"\n", + "install_config[\"num_feeders\"] = 2\n", + "install_config[\"site\"] = design_config[\"site\"] | {\"distance\": 70}\n", + "install_config[\"plant\"] = design_config[\"plant\"]\n", + "install_config[\"turbine\"] = design_config[\"turbine\"]\n", + "\n", + "monopile_install = MonopileInstallation(install_config)\n", + "monopile_install.run()\n", + "print(f\"Total Installation Cost: ${monopile_install.installation_capex / 1e6:,.2f} M\")\n", + "print(monopile_install.config.dump())" + ] + }, + { + "cell_type": "markdown", + "id": "d93a518e", + "metadata": {}, + "source": [ + "### Loading and Saving Configurations\n", + "\n", + "In addition to writing dictionaries in a script or Notebook file, ORBIT also provides the\n", + "`load_config` and `save_config` functions to load and save configurations for easier scenario\n", + "management. In the following example, we demonstrate a hypothetical workflow loading, updating, and\n", + "saving a new monopile design configuration.\n", + "\n", + "```python\n", + "design_config = load_config(\"path/to/monopile_design.yaml\")\n", + "\n", + "... # calculate additional properties of the monopile and update the configuration\n", + "\n", + "save_config(design_config, \"path/to/new_monopile_design.yaml\")\n", + "```\n", + "\n", + "Other use cases could be for creating input templates for project configurations, such\n", + "as those used by `ProjectManager` in the next section.\n", + "\n", + "(library-tutorial)=\n", + "## Using A Data Library\n", + "\n", + "ORBIT makes use of its own\n", + "[internal library](https://github.com/NLRWindSystems/ORBIT/tree/main/library) when a user-provided\n", + "library path is not provided (i.e. a value isn't provide so the default `None` is used in\n", + "`ProjectManager(config, library_path=None)`). When a value is provided, user library files will be\n", + "searched for first, and the default library will be checked for any unfound files.\n", + "\n", + "This is made visible in the [installation phases section](#installation-phases) where the value\n", + "\"example_wtiv\" is provided to the \"wtiv\" key. When the configuration is loaded, `ProjectManager`\n", + "will attempt to find the `example_wtiv.yaml` file in the ORBIT default library under the `vessels/`\n", + "folder. Below is the expected folder structure of the library. I\n", + "\n", + "```console\n", + "# /path/to/library/\n", + "\u251c\u2500\u2500 defaults <- Top-level default data\n", + "\u251c\u2500\u2500 project\n", + "\u2502 \u251c\u2500\u2500 config <- Configuration dictionary repository\n", + "\u2502 \u251c\u2500\u2500 port <- Port specific data setttings\n", + "\u2502 \u251c\u2500\u2500 plant <- Wind farm specific data setttings\n", + "\u2502 \u251c\u2500\u2500 site <- Project site data settings\n", + "\u2502 \u251c\u2500\u2500 development <- Project development cost settings\n", + "\u251c\u2500\u2500 cables <- Cable data files: array cables, export cables\n", + "\u251c\u2500\u2500 substructures <- Substructure data files: monopiles, jackets, etc.\n", + "\u251c\u2500\u2500 turbines <- Turbine data files\n", + "\u251c\u2500\u2500 vessels <- Vessel data files\n", + "\u2502 \u251c\u2500\u2500 defaults <- Default data related to vessel tasks\n", + "\u251c\u2500\u2500 weather <- Weather profiles\n", + "\u251c\u2500\u2500 results\n", + "```\n", + "\n", + "## Vessels\n", + "\n", + "All installation models rely on at least one vessel to perform the installation routines. Similar\n", + "to turbine and cable configuration files, these should be stored in the YAML format in the\n", + "`vessels` library folder. Below are the\n", + "\n", + "- `vessel_specs` - General vessel parameters including day rate.\n", + " - `day_rate`: Daily cost to operate the vessel, $USD/day.\n", + " - `min_draft`: Minimum distance between the waterline and the bottom of the hull, m.\n", + " - `overall_length`: Length of the vessel, m.\n", + " - `mobilization_days`: Number days required to mobilize the vessel to site.\n", + " - `mobilization_mult`: Mobilization multiplier applied to `day_rate`.\n", + " - Any other custom input that will override logistics defaults.\n", + "- `transport_specs` - Transit related parameters and constraints.\n", + " - `transit_speed`: Average transiting speed, km/h.\n", + " - `max_waveheight`: Maximum operational wave height, m/s.\n", + " - `max_windspeed`: Maximum operational wind speed, m/s.\n", + "- `storage_specs` - Storage related parameters. Required to transport items\n", + " on deck.\n", + " - `max_cargo`: Maximum cargo capacity, metric tonnes.\n", + " - `max_deck_load`: Maximum capacity to be loaded on deck, metric tonnes per square meter, $t/m^2$.\n", + " - `max_deck_space`: Maximum amount of space on deck for loading components, $m^2$.\n", + "- `cable_storage`: Array and export cable carousel storage parameters.\n", + " - `max_mass`: Maximum mass of the cable carousel, in metric tonnes.\n", + "- `spi_specs`: Scouring protection installation vessel storage parameters.\n", + " - `max_cargo_mass`: Maximum mass allowed to be loaded for a single trip, in metric tonnes.\n", + "- `jacksys_specs`: Jacking system related parameters. Currently required\n", + " for all fixed substructure and turbine installations.\n", + " - `leg_length`: Length of the jackup vessel's legs, m.\n", + " - `air_gap`: Distance between sea level and the vessel bottom when fully jacked up, m.\n", + " - `leg_pen`: How far the leg penetrates the sea floor for stability, m.\n", + " - `max_depth`: Maxium water depth, m.\n", + " - `max_extension`: Maximum leg extension, m.\n", + " - `speed_below_depth`: Jackup speed when leg extension has not reached the sea floor, m/min\n", + " - `speed_above_depth`: Jackup speed after the leg has reached the sea floor and the vessel is\n", + " being raised above sea level, m/min.\n", + "- `dynamic_positioning_specs`: Dynamic positioning related parameters\n", + " - `class`: integer of the dynamic positioning class.\n", + "- `crane_specs` - Crane related parameters and constraints. Required for\n", + " any offshore lifts.\n", + " - `max_lift`: Maximum mass that can be lifted, metric tonnes.\n", + " - `max_hook_height`: Maximum height the hook can be raised, m.\n", + " - `max_windspeed`: Maximum operational windspeed, m/s.\n", + " - `crane_rate`: Crane lift rate, m/h.\n", + "\n", + "## Syncing Design and Installation with `ProjectManager`\n", + "\n", + "`ProjectManager` is the primary system for interacting with ORBIT. It provides the ability to\n", + "configure and run one or multiple design and installation at a time, allowing the user to customize\n", + "ORBIT to fit the needs of a specific project. It also provides a helper method to detail what inputs\n", + "are required to run the desired configuration.\n", + "\n", + "Continuing to work with just the monopile, we can provide a barebones configuration to set the\n", + "desired phases, and output the required inputs when running the design and installation phase in\n", + "unison. Notice that the \"monopile\" definition is no longer required for the installation as the\n", + "`design_result` will be automatically passed from the design phase to the installation phase.\n", + "There are now additional project parameters to supply development and other non-modeled fixed costs\n", + "the project will incur. Similar to the design and installation models, anything that is marked as\n", + "optional will have a default value within the model.\n", + "\n", + "For more details on the `ProjectManager`, please see the [tutorial](#project-manager-tutorial)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "401b77a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'design_phases': ['MonopileDesign'],\n", + " 'feeder': 'dict | str (optional)',\n", + " 'install_phases': ['MonopileInstallation'],\n", + " 'monopile_design': {'air_density': 'kg/m3 (optional)',\n", + " 'load_factor': 'float (optional)',\n", + " 'material_factor': 'float (optional)',\n", + " 'monopile_density': 'kg/m3 (optional)',\n", + " 'monopile_modulus': 'Pa (optional)',\n", + " 'monopile_steel_cost': 'USD/t (optional)',\n", + " 'monopile_tp_connection_thickness': 'm (optional)',\n", + " 'soil_coefficient': 'N/m3 (optional)',\n", + " 'tp_steel_cost': 'USD/t (optional)',\n", + " 'transition_piece_density': 'kg/m3 (optional)',\n", + " 'transition_piece_length': 'm (optional)',\n", + " 'transition_piece_thickness': 'm (optional)',\n", + " 'turb_length_scale': 'm (optional)',\n", + " 'weibull_scale_factor': 'float (optional)',\n", + " 'weibull_shape_factor': 'float (optional)',\n", + " 'yield_stress': 'Pa (optional)'},\n", + " 'monopile_supply_chain': {'enabled': '(optional, default: False)',\n", + " 'num_substructures_delivered': 'int (optional: '\n", + " 'default: 1)',\n", + " 'substructure_delivery_time': 'h (optional, '\n", + " 'default: 168)',\n", + " 'substructure_storage': 'int (optional, default: '\n", + " 'inf)'},\n", + " 'num_feeders': 'int (optional)',\n", + " 'orbit_version': '1.2.6',\n", + " 'plant': {'num_turbines': 'int'},\n", + " 'port': {'monthly_rate': 'USD/mo (optional)',\n", + " 'name': 'str (optional)',\n", + " 'num_cranes': 'int (optional, default: 1)'},\n", + " 'project_parameters': {'commissioning': '$/kW (optional, default: value '\n", + " 'calculated using '\n", + " 'commissioning_factor)',\n", + " 'commissioning_factor': 'float (optional, default: '\n", + " '0.0115)',\n", + " 'construction_financing': '$/kW (optional, default: '\n", + " 'value calculated using '\n", + " 'construction_financing_factor))',\n", + " 'construction_financing_factor': ('$/kW (optional, '\n", + " 'default: value '\n", + " 'calculated using '\n", + " 'spend_schedule, '\n", + " 'tax_rate, and '\n", + " 'interest_during_construction))',),\n", + " 'construction_insurance': '$/kW (optional, default: '\n", + " 'value calculated using '\n", + " 'construction_insurance_factor)',\n", + " 'construction_insurance_factor': 'float (optional, '\n", + " 'default: 0.0207)',\n", + " 'construction_plan_cost': '$ (optional, default: 25e6)',\n", + " 'decommissioning': '$/kW (optional, default: value '\n", + " 'calculated using '\n", + " 'decommissioning_factor)',\n", + " 'decommissioning_factor': 'float (optional, default: '\n", + " '0.2)',\n", + " 'discount_rate': 'yearly (optional, default: .025)',\n", + " 'installation_contingency': '$/kW (optional, default: '\n", + " 'value calculated using '\n", + " 'installation_contingency_factor)',\n", + " 'installation_contingency_factor': 'float (optional, '\n", + " 'default: 0.345)',\n", + " 'installation_plan_cost': '$ (optional, default: 25e6)',\n", + " 'interest_during_construction': 'float (optional, '\n", + " 'default: 0.065',\n", + " 'ncf': 'float (optional, default: 0.4)',\n", + " 'offtake_price': '$/MWh (optional, default: 80)',\n", + " 'opex_rate': '$/kW/year (optional, default: 150)',\n", + " 'procurement_contingency': '$/kW (optional, default: '\n", + " 'value calculated using '\n", + " 'procurement_contingency_factor)',\n", + " 'procurement_contingency_factor': 'float (optional, '\n", + " 'default: 0.0575)',\n", + " 'project_lifetime': 'yrs (optional, default: 25)',\n", + " 'site_assessment_cost': '$ (optional, default: 200e6)',\n", + " 'site_auction_price': '$ (optional, default: 105e6)',\n", + " 'spend_schedule': 'dict (optional, default: {0: 0.25, '\n", + " '1: 0.25, 2: 0.3, 3: 0.1, 4: 0.1, 5: '\n", + " '0.0}',\n", + " 'tax_rate': 'float (optional, default: 0.26',\n", + " 'turbine_capex': '$/kW (optional, default: 1300)'},\n", + " 'site': {'depth': 'm', 'distance': 'km', 'mean_windspeed': 'm/s'},\n", + " 'turbine': {'hub_height': 'm',\n", + " 'rated_windspeed': 'm/s',\n", + " 'rotor_diameter': 'm'},\n", + " 'wtiv': 'dict | str'}\n" + ] + } + ], + "source": [ + "phases = [\"MonopileDesign\", \"MonopileInstallation\"]\n", + "config_template = ProjectManager.compile_input_dict(phases)\n", + "pprint(config_template)" + ] + }, + { + "cell_type": "markdown", + "id": "252e6be3", + "metadata": {}, + "source": [ + "Now, we can combine the monopile design and installation configurations that were\n", + "used in the previous examples, and run the model to get a single CapEx alongsie the\n", + "high level category breakdown." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "012e311d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Project Capex: 303.23 M\n", + " Substructure: 269.26 M\n", + " Substructure Installation: 33.97 M\n", + " Onshore Substation: 0.00 M\n", + " Turbine: 780.00 M\n", + " Soft: 274.09 M\n", + " Project: 355.00 M\n" + ] + } + ], + "source": [ + "project_config = deepcopy(design_config)\n", + "project_config[\"wtiv\"] = \"example_wtiv\"\n", + "project_config[\"feeder\"] = \"example_feeder\"\n", + "project_config[\"num_feeders\"] = 2\n", + "project_config[\"site\"] = install_config[\"site\"]\n", + "project_config[\"turbine\"][\"turbine_rating\"] = 12\n", + "project_config[\"design_phases\"] = [\"MonopileDesign\"]\n", + "project_config[\"install_phases\"] = [\"MonopileInstallation\"]\n", + "\n", + "project = ProjectManager(project_config)\n", + "project.run()\n", + "print(f\"{'Project Capex':>30}: {project.bos_capex / 1e6:6,.2f} M\")\n", + "for category, cost in project.capex_breakdown.items():\n", + " print(f\"{category:>30}: {cost / 1e6:6,.2f} M\")" + ] + }, + { + "cell_type": "markdown", + "id": "6e883464", + "metadata": {}, + "source": [ + "To continue with the previous subsection's demonstration, we can also save the final configuration\n", + "in one combined file, so the project could be reloaded and rerun in the future." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "00736074", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Project Capex: 303.23 M\n", + " Substructure: 269.26 M\n", + " Substructure Installation: 33.97 M\n", + " Onshore Substation: 0.00 M\n", + " Turbine: 780.00 M\n", + " Soft: 274.09 M\n", + " Project: 355.00 M\n" + ] + } + ], + "source": [ + "config_fn = Path(\"monopile_demo.yaml\").resolve()\n", + "save_config(project.config, config_fn)\n", + "\n", + "config = load_config(config_fn)\n", + "project = ProjectManager(config)\n", + "project.run()\n", + "\n", + "print(f\"{'Project Capex':>30}: {project.bos_capex / 1e6:6,.2f} M\")\n", + "for category, cost in project.capex_breakdown.items():\n", + " print(f\"{category:>30}: {cost / 1e6:6,.2f} M\")\n", + "\n", + "config_fn.unlink() # delete the demo file" + ] + } + ], + "metadata": { + "jupytext": { + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.19.1" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "source_map": [ + 12, + 27, + 35, + 41, + 45, + 47, + 59, + 63, + 65, + 76, + 92, + 97, + 102, + 113, + 120, + 129, + 155, + 164, + 189, + 204, + 217, + 331, + 335, + 341, + 356, + 361 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/parametric_manager.ipynb b/examples/parametric_manager.ipynb new file mode 100644 index 00000000..d6e4ebac --- /dev/null +++ b/examples/parametric_manager.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "845ac037", + "metadata": {}, + "source": [ + "(parametric-manager-tutorial)=\n", + "# ParametricManager\n", + "\n", + "Similar to the `ProjectManager`, ORIBT provides the `ParametricManager` to run simple parametric\n", + "studies by defining a subset of the inputs as a list. This allows for tradeoff studies to compare\n", + "the effects of siting (e.g., water depth and distance) on cost and installation timing. For complete\n", + "details on using the `ParmetricManager` please see the [API documentation](#parametric-manager-api).\n", + "\n", + "First, we'll import the necessary libraries, and load the example fixed-bottom project to use as\n", + "our project base with the 15 MW turbine." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8b3470ca", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import StrMethodFormatter\n", + "\n", + "from ORBIT import ParametricManager, load_config\n", + "\n", + "here = Path(\".\").resolve()\n", + "example_dir = here.parents[1] / \"examples\" if here.stem == \"tutorials\" else here\n", + "\n", + "config = load_config(example_dir / \"configs/example_fixed_project.yaml\")\n", + "config[\"turbine\"] = \"15MW_generic\"\n", + "\n", + "weather = pd.read_csv(example_dir / \"data/example_weather.csv\").set_index(\"datetime\")" + ] + }, + { + "cell_type": "markdown", + "id": "0ff67c59", + "metadata": {}, + "source": [ + "For all the non-parameterized inputs, they can be left as-is. However, all parameterized variables\n", + "should be provided in a separate dictionary as a list. Because ORBIT uses the `benedict` library\n", + "for more streamlined dictionary access, nested keys can be represented using dot-notation as is\n", + "shown below where we parameterize the key siting details." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37ce5ece", + "metadata": {}, + "outputs": [], + "source": [ + "params = {\n", + " \"site.depth\": list(range(10, 71, 10)),\n", + " \"site.distance\": list(range(20, 201, 20)),\n", + " \"site.distance_to_landfall\": [60, 80, 100],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "5105feaa", + "metadata": {}, + "source": [ + "Similar to the parameterized inputs, we must also define the desired outputs. However, outputs must\n", + "be provided as a dictionary of `lambda` functions for what metrics should be captured. In the\n", + "below example, we extract just the installation and system CapEx." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1f99b07b", + "metadata": {}, + "outputs": [], + "source": [ + "results = {\n", + " \"Installation\": lambda project: project.installation_capex,\n", + " \"System\": lambda project: project.system_capex\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c59937cc", + "metadata": {}, + "source": [ + "If many parameters are configured, it will take a longer time to run, especially if a weather\n", + "profile is provided and `product=True`. To get an idea of the total run time, use the `preview`\n", + "method, as seen below.\n", + "\n", + "Setting `product` to `True` means that all of the parameters will be run as a combination of all\n", + "possible permutations rather than a zipped list. When using `False` extra care must be taken to\n", + "ensure the correct outcomes will be achieved by using equally-lengthed parameterizations. For\n", + "instacnce, in our current example, the shortest parameterization has only 3 values, so the first\n", + "3 values of `depth` and `distance` will be selected for the parameterized run." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e7d711de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ORBIT library intialized at '/Users/rhammond/GitHub_Public/ORBIT/library'\n", + "10 runs elapsed time: 3.75s\n", + "210 runs estimated time: 78.67s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 runs elapsed time: 3.75s\n", + "210 runs estimated time: 78.67s\n" + ] + }, + { + "data": { + "text/html": [ + "
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site.depthsite.distancesite.distance_to_landfallInstallationSystem
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" + ], + "text/plain": [ + " site.depth site.distance site.distance_to_landfall Installation \\\n", + "0 20 40 100 3.445039e+08 \n", + "1 20 100 80 3.525076e+08 \n", + "2 20 160 100 3.751548e+08 \n", + "3 60 180 60 3.800057e+08 \n", + "4 60 60 60 3.386306e+08 \n", + "5 40 40 60 3.264900e+08 \n", + "6 10 60 80 3.401561e+08 \n", + "7 20 160 80 3.655228e+08 \n", + "8 70 140 80 3.764944e+08 \n", + "9 70 100 80 3.644137e+08 \n", + "\n", + " System \n", + "0 1.301853e+09 \n", + "1 1.227865e+09 \n", + "2 1.301853e+09 \n", + "3 1.386662e+09 \n", + "4 1.386662e+09 \n", + "5 1.265779e+09 \n", + "6 1.175408e+09 \n", + "7 1.227865e+09 \n", + "8 1.523631e+09 \n", + "9 1.523631e+09 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "project = ParametricManager(config, params, results, product=True, weather=weather)\n", + "project.preview()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6347de2c", + "metadata": {}, + "outputs": [], + "source": [ + "project.run()" + ] + }, + { + "cell_type": "markdown", + "id": "0c579798", + "metadata": {}, + "source": [ + "The results are saved as a pandas DataFrame in the `results` attribute where each row represents a\n", + "different scenario run and the columns are labeled with with the various parameters and results\n", + "values that were configured.\n", + "\n", + "## Plotting\n", + "\n", + "It is more convenient to plot the results of the `ParametricManager` than it is to view them as a\n", + "table, especially with a large number of parameters. First, we will create a matrix of results\n", + "for each of the installation and system CapEx. Please note the `installation_arr` index is sorted\n", + "in reverse order for convenience in creating the heatmap." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c4cf4f54", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Index contains duplicate entries, cannot reshape", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m results = project.results.set_index([\u001b[33m\"\u001b[39m\u001b[33msite.depth\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33msite.distance\u001b[39m\u001b[33m\"\u001b[39m]) / \u001b[32m1e6\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m installation_arr = \u001b[43mresults\u001b[49m\u001b[43m.\u001b[49m\u001b[43munstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mInstallation\u001b[39m\u001b[33m\"\u001b[39m].sort_index(ascending=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 3\u001b[39m system_arr = results.unstack()[\u001b[33m\"\u001b[39m\u001b[33mSystem\u001b[39m\u001b[33m\"\u001b[39m].sort_index()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/orbit/lib/python3.11/site-packages/pandas/core/frame.py:9928\u001b[39m, in \u001b[36mDataFrame.unstack\u001b[39m\u001b[34m(self, level, fill_value, sort)\u001b[39m\n\u001b[32m 9864\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 9865\u001b[39m \u001b[33;03mPivot a level of the (necessarily hierarchical) index labels.\u001b[39;00m\n\u001b[32m 9866\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 9924\u001b[39m \u001b[33;03mdtype: float64\u001b[39;00m\n\u001b[32m 9925\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 9926\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mreshape\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mreshape\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m unstack\n\u001b[32m-> \u001b[39m\u001b[32m9928\u001b[39m result = \u001b[43munstack\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 9930\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result.__finalize__(\u001b[38;5;28mself\u001b[39m, method=\u001b[33m\"\u001b[39m\u001b[33munstack\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/orbit/lib/python3.11/site-packages/pandas/core/reshape/reshape.py:504\u001b[39m, in \u001b[36munstack\u001b[39m\u001b[34m(obj, level, fill_value, sort)\u001b[39m\n\u001b[32m 502\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, DataFrame):\n\u001b[32m 503\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj.index, MultiIndex):\n\u001b[32m--> \u001b[39m\u001b[32m504\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_unstack_frame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[43msort\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 505\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 506\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m obj.T.stack(future_stack=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/orbit/lib/python3.11/site-packages/pandas/core/reshape/reshape.py:529\u001b[39m, in \u001b[36m_unstack_frame\u001b[39m\u001b[34m(obj, level, fill_value, sort)\u001b[39m\n\u001b[32m 525\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_unstack_frame\u001b[39m(\n\u001b[32m 526\u001b[39m obj: DataFrame, level, fill_value=\u001b[38;5;28;01mNone\u001b[39;00m, sort: \u001b[38;5;28mbool\u001b[39m = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 527\u001b[39m ) -> DataFrame:\n\u001b[32m 528\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj.index, MultiIndex) \u001b[38;5;66;03m# checked by caller\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m529\u001b[39m unstacker = \u001b[43m_Unstacker\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 530\u001b[39m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstructor\u001b[49m\u001b[43m=\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_constructor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[43msort\u001b[49m\n\u001b[32m 531\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 533\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m obj._can_fast_transpose:\n\u001b[32m 534\u001b[39m mgr = obj._mgr.unstack(unstacker, fill_value=fill_value)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/orbit/lib/python3.11/site-packages/pandas/core/reshape/reshape.py:154\u001b[39m, in \u001b[36m_Unstacker.__init__\u001b[39m\u001b[34m(self, index, level, constructor, sort)\u001b[39m\n\u001b[32m 146\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m num_cells > np.iinfo(np.int32).max:\n\u001b[32m 147\u001b[39m warnings.warn(\n\u001b[32m 148\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThe following operation may generate \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_cells\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m cells \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 149\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33min the resulting pandas object.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 150\u001b[39m PerformanceWarning,\n\u001b[32m 151\u001b[39m stacklevel=find_stack_level(),\n\u001b[32m 152\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m154\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_selectors\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/orbit/lib/python3.11/site-packages/pandas/core/reshape/reshape.py:210\u001b[39m, in \u001b[36m_Unstacker._make_selectors\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 207\u001b[39m mask.put(selector, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 209\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mask.sum() < \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m.index):\n\u001b[32m--> \u001b[39m\u001b[32m210\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mIndex contains duplicate entries, cannot reshape\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 212\u001b[39m \u001b[38;5;28mself\u001b[39m.group_index = comp_index\n\u001b[32m 213\u001b[39m \u001b[38;5;28mself\u001b[39m.mask = mask\n", + "\u001b[31mValueError\u001b[39m: Index contains duplicate entries, cannot reshape" + ] + } + ], + "source": [ + "results = project.results.set_index([\"site.depth\", \"site.distance\"]) / 1e6\n", + "installation_arr = results.unstack()[\"Installation\"].sort_index(ascending=False)\n", + "system_arr = results.unstack()[\"System\"].sort_index()" + ] + }, + { + "cell_type": "markdown", + "id": "43292a47", + "metadata": {}, + "source": [ + "As mentioned in the [`ProjectManager` tutorial](#project-manager-tutorial), the system CapEx will\n", + "not change given certain parameter changes. In this case, the installation CapEx increases both\n", + "as the site's distance and depth increases, as can be seen in the following heatmap." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0d2dbb88", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'installation_arr' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m fig = plt.figure()\n\u001b[32m 2\u001b[39m ax = fig.add_subplot(\u001b[32m111\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m im = ax.imshow(\u001b[43minstallation_arr\u001b[49m.values, vmin=\u001b[32m290\u001b[39m, vmax=\u001b[32m380\u001b[39m)\n\u001b[32m 6\u001b[39m cbar = fig.colorbar(im, ax=ax, shrink=\u001b[32m0.8\u001b[39m)\n\u001b[32m 7\u001b[39m cbar.ax.set_ylabel(\u001b[33m\"\u001b[39m\u001b[33mInstallation CapEx (millions, USD)\u001b[39m\u001b[33m\"\u001b[39m, rotation=-\u001b[32m90\u001b[39m, va=\u001b[33m\"\u001b[39m\u001b[33mbottom\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mNameError\u001b[39m: name 'installation_arr' is not defined" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "\n", + "im = ax.imshow(installation_arr.values, vmin=290, vmax=380)\n", + "\n", + "cbar = fig.colorbar(im, ax=ax, shrink=0.8)\n", + "cbar.ax.set_ylabel(\"Installation CapEx (millions, USD)\", rotation=-90, va=\"bottom\")\n", + "\n", + "ax.set_xticks(range(len(installation_arr.columns)), labels=installation_arr.columns, rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n", + "ax.set_yticks(range(len(installation_arr.index)), labels=installation_arr.index)\n", + "\n", + "ax.set_xlabel(\"Site Distance (km)\")\n", + "ax.set_ylabel(\"Site Depth (m)\")\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "74a3ba85", + "metadata": {}, + "source": [ + "However, the system CapEx only increases as the site's depth increases because only the site's\n", + "distance to port changes, and not the distance to landfall, meaning the export cable length will\n", + "not change across scenarios. This can be seen in the below bar graph." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2cede1a9", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'system_arr' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m fig = plt.figure()\n\u001b[32m 2\u001b[39m ax = fig.add_subplot(\u001b[32m111\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m x = \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[43msystem_arr\u001b[49m.index))\n\u001b[32m 5\u001b[39m ax.bar(x, system_arr.values[:, \u001b[32m0\u001b[39m])\n\u001b[32m 7\u001b[39m ax.set_xticks(x)\n", + "\u001b[31mNameError\u001b[39m: name 'system_arr' is not defined" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "\n", + "x = range(len(system_arr.index))\n", + "ax.bar(x, system_arr.values[:, 0])\n", + "\n", + "ax.set_xticks(x)\n", + "ax.set_xticklabels(system_arr.index.values)\n", + "ax.yaxis.set_major_formatter(StrMethodFormatter(\"{x:,.0f}\"))\n", + "ax.set_xlabel(\"Site Depth (m)\")\n", + "ax.set_ylabel(\"System CapEx (millions, USD)\")\n", + "\n", + "fig.tight_layout()" + ] + } + ], + "metadata": { + "jupytext": { + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.19.1" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "source_map": [ + 12, + 25, + 41, + 48, + 54, + 60, + 65, + 77, + 82, + 84, + 97, + 101, + 107, + 123, + 129 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/project_manager.ipynb b/examples/project_manager.ipynb new file mode 100644 index 00000000..209b6029 --- /dev/null +++ b/examples/project_manager.ipynb @@ -0,0 +1,595 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "af58b3c4", + "metadata": {}, + "source": [ + "(project-manager-tutorial)=\n", + "# `ProjectManager` Deep Dive\n", + "\n", + "`ProjectManager` is the primary system for interacting with ORBIT. It provides the ability to\n", + "configure and run one or multiple models at a time, allowing the user to customize ORBIT to fit the\n", + "needs of a specific project." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f29724f8", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "from pprint import pprint\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from ORBIT import ProjectManager\n", + "\n", + "# Ensure the correct examples directory is used when running this in docs or in examples\n", + "here = Path(\".\").resolve()\n", + "example_dir = here.parents[1] / \"examples\" if here.stem == \"tutorials\" else here" + ] + }, + { + "cell_type": "markdown", + "id": "4220d5f1", + "metadata": {}, + "source": [ + "## Compiling Input Requirements Dynamically\n", + "\n", + "To better understand the input requirements for designing and installing multiple turbine subsystems,\n", + "`ProjectManager` provides the `compile_input_dict()` method that will generate the expected\n", + "configuration of each provided phase in a single configuration dictionary. The example below shows\n", + "how to configure a simple project with a design and multiple installation phases, and return the required configuration parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f7dd9d68", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'design_phases': ['MonopileDesign'],\n", + " 'feeder': 'dict | str (optional)',\n", + " 'install_phases': ['MonopileInstallation', 'TurbineInstallation'],\n", + " 'monopile_design': {'air_density': 'kg/m3 (optional)',\n", + " 'load_factor': 'float (optional)',\n", + " 'material_factor': 'float (optional)',\n", + " 'monopile_density': 'kg/m3 (optional)',\n", + " 'monopile_modulus': 'Pa (optional)',\n", + " 'monopile_steel_cost': 'USD/t (optional)',\n", + " 'monopile_tp_connection_thickness': 'm (optional)',\n", + " 'soil_coefficient': 'N/m3 (optional)',\n", + " 'tp_steel_cost': 'USD/t (optional)',\n", + " 'transition_piece_density': 'kg/m3 (optional)',\n", + " 'transition_piece_length': 'm (optional)',\n", + " 'transition_piece_thickness': 'm (optional)',\n", + " 'turb_length_scale': 'm (optional)',\n", + " 'weibull_scale_factor': 'float (optional)',\n", + " 'weibull_shape_factor': 'float (optional)',\n", + " 'yield_stress': 'Pa (optional)'},\n", + " 'monopile_supply_chain': {'enabled': '(optional, default: False)',\n", + " 'num_substructures_delivered': 'int (optional: '\n", + " 'default: 1)',\n", + " 'substructure_delivery_time': 'h (optional, '\n", + " 'default: 168)',\n", + " 'substructure_storage': 'int (optional, default: '\n", + " 'inf)'},\n", + " 'num_feeders': 'int (optional)',\n", + " 'orbit_version': '1.2.6',\n", + " 'plant': {'num_turbines': 'int'},\n", + " 'port': {'monthly_rate': 'USD/mo (optional)',\n", + " 'name': 'str (optional)',\n", + " 'num_cranes': 'int (optional, default: 1)'},\n", + " 'project_parameters': {'commissioning': '$/kW (optional, default: value '\n", + " 'calculated using '\n", + " 'commissioning_factor)',\n", + " 'commissioning_factor': 'float (optional, default: '\n", + " '0.0115)',\n", + " 'construction_financing': '$/kW (optional, default: '\n", + " 'value calculated using '\n", + " 'construction_financing_factor))',\n", + " 'construction_financing_factor': ('$/kW (optional, '\n", + " 'default: value '\n", + " 'calculated using '\n", + " 'spend_schedule, '\n", + " 'tax_rate, and '\n", + " 'interest_during_construction))',),\n", + " 'construction_insurance': '$/kW (optional, default: '\n", + " 'value calculated using '\n", + " 'construction_insurance_factor)',\n", + " 'construction_insurance_factor': 'float (optional, '\n", + " 'default: 0.0207)',\n", + " 'construction_plan_cost': '$ (optional, default: 25e6)',\n", + " 'decommissioning': '$/kW (optional, default: value '\n", + " 'calculated using '\n", + " 'decommissioning_factor)',\n", + " 'decommissioning_factor': 'float (optional, default: '\n", + " '0.2)',\n", + " 'discount_rate': 'yearly (optional, default: .025)',\n", + " 'installation_contingency': '$/kW (optional, default: '\n", + " 'value calculated using '\n", + " 'installation_contingency_factor)',\n", + " 'installation_contingency_factor': 'float (optional, '\n", + " 'default: 0.345)',\n", + " 'installation_plan_cost': '$ (optional, default: 25e6)',\n", + " 'interest_during_construction': 'float (optional, '\n", + " 'default: 0.065',\n", + " 'ncf': 'float (optional, default: 0.4)',\n", + " 'offtake_price': '$/MWh (optional, default: 80)',\n", + " 'opex_rate': '$/kW/year (optional, default: 150)',\n", + " 'procurement_contingency': '$/kW (optional, default: '\n", + " 'value calculated using '\n", + " 'procurement_contingency_factor)',\n", + " 'procurement_contingency_factor': 'float (optional, '\n", + " 'default: 0.0575)',\n", + " 'project_lifetime': 'yrs (optional, default: 25)',\n", + " 'site_assessment_cost': '$ (optional, default: 200e6)',\n", + " 'site_auction_price': '$ (optional, default: 105e6)',\n", + " 'spend_schedule': 'dict (optional, default: {0: 0.25, '\n", + " '1: 0.25, 2: 0.3, 3: 0.1, 4: 0.1, 5: '\n", + " '0.0}',\n", + " 'tax_rate': 'float (optional, default: 0.26',\n", + " 'turbine_capex': '$/kW (optional, default: 1300)'},\n", + " 'site': {'depth': 'm', 'distance': 'km', 'mean_windspeed': 'm/s'},\n", + " 'turbine': {'blade': {'deck_space': 'm2', 'mass': 't'},\n", + " 'hub_height': 'm',\n", + " 'nacelle': {'deck_space': 'm2', 'mass': 't'},\n", + " 'rated_windspeed': 'm/s',\n", + " 'rotor_diameter': 'm',\n", + " 'tower': {'deck_space': 'm2',\n", + " 'length': 'm',\n", + " 'mass': 't',\n", + " 'sections': 'int (optional)'}},\n", + " 'wtiv': 'dict | str'}\n" + ] + } + ], + "source": [ + "phases = [\n", + " \"MonopileDesign\",\n", + " \"MonopileInstallation\",\n", + " \"TurbineInstallation\",\n", + "]\n", + "\n", + "expected_config = ProjectManager.compile_input_dict(phases)\n", + "pprint(expected_config)" + ] + }, + { + "cell_type": "markdown", + "id": "c73f5fe4", + "metadata": {}, + "source": [ + "Using the results of the `expected_config`, the following configuration is now created to minimally\n", + "define a project running only the monopile phases for design and installation, and the turbine\n", + "installation phase. Note that the turbine is a copy of the\n", + "[12MW generic turbine from ORBIT library](https://github.com/NLRWindSystems/ORBIT/tree/main/library/turbines/12MW_generic.yaml)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "af43c2e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ORBIT library intialized at '/Users/rhammond/GitHub_Public/ORBIT/library'\n" + ] + } + ], + "source": [ + "config = {\n", + " \"site\": {\n", + " \"depth\": 20,\n", + " \"distance\": 50,\n", + " \"mean_windspeed\": 9.5,\n", + " },\n", + " \"plant\": {\n", + " \"num_turbines\": 50,\n", + " },\n", + " \"turbine\": {\n", + " \"name\": \"12MW Generic Turbine\",\n", + " \"rotor_diameter\": 205,\n", + " \"hub_height\": 125,\n", + " \"rated_windspeed\": 11,\n", + " \"blade\": {\n", + " \"deck_space\": 385,\n", + " \"length\": 107,\n", + " \"type\": \"Blade\",\n", + " \"mass\": 54,\n", + " },\n", + " \"nacelle\": {\n", + " \"deck_space\": 203,\n", + " \"type\": \"Nacelle\",\n", + " \"mass\": 604,\n", + " },\n", + " \"tower\": {\n", + " \"deck_space\": 50.24,\n", + " \"sections\": 2,\n", + " \"type\": \"Tower\",\n", + " \"length\": 132,\n", + " \"mass\": 399,\n", + " },\n", + " },\n", + " \"wtiv\": \"example_wtiv\",\n", + " \"design_phases\": [\"MonopileDesign\"],\n", + " \"install_phases\": [\"MonopileInstallation\", \"TurbineInstallation\"],\n", + "}\n", + "\n", + "project = ProjectManager(config)\n", + "project.run()" + ] + }, + { + "cell_type": "markdown", + "id": "29df9d90", + "metadata": {}, + "source": [ + "## Weather Profiles\n", + "\n", + "To include wind and wave conditions in the simulation for vessel and port constraints, pass an\n", + "hourly pandas DataFrame to `ProjectManager` using the `weather` keyword argument. All installation\n", + "phases will now use this time series to account for weather delays." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "78c8b350", + "metadata": {}, + "outputs": [], + "source": [ + "weather = pd.read_csv(example_dir / \"data/example_weather.csv\").set_index(\"datetime\")\n", + "\n", + "project = ProjectManager(config, weather=weather)\n", + "project.run()" + ] + }, + { + "cell_type": "markdown", + "id": "be30c6b0", + "metadata": {}, + "source": [ + "## Accessing Individual Models\n", + "\n", + "The `ProjectManager` provides a dictionary-based attribute `phases` that allows users to access the\n", + "design or installation class for custom results gathering or model inspection. Using the previously\n", + "run project, we now directly access the monopile design costs." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b2be3cea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Monopile Cost: $335.34 M\n" + ] + } + ], + "source": [ + "monopile_design_cost = project.phases[\"MonopileDesign\"].total_cost\n", + "print(f\"Total Monopile Cost: ${monopile_design_cost / 1e6:,.2f} M\")" + ] + }, + { + "cell_type": "markdown", + "id": "3e0c92ef", + "metadata": {}, + "source": [ + "## Phase-Specific Configurations\n", + "\n", + "As was seen in [inputs compilation demonstration](#compiling-input-requirements-dynamically),\n", + "`ProjectManager` compiles the minimum required configuration, combining the same parameter that is\n", + "needed for multiple phases into one input. This isn't always a desired outcome as there are cases\n", + "when inputs need to be different for each phase. For example, the `distance_to_shore` parameter may\n", + "be different for each installation phase if different ports are used to stage monopiles and turbines\n", + "or the installations may use different installation vessels.\n", + "\n", + "In these cases, it is necessary to define phase specific input parameters using the phase's name as\n", + "the dictionary key. Below, we can see how we model a differing staging port where a separate WTIV\n", + "will be used with its much further port distance.\n", + "\n", + "Please note that phase-specific configurations will always override their general counterparts.\n", + "\n", + "```python\n", + "config = {\n", + " \"site\": {\n", + " \"depth\": 20,\n", + " \"distance\": 50,\n", + " \"mean_windspeed\": 9.5,\n", + " },\n", + " \"plant\": {\n", + " \"num_turbines\": 50,\n", + " },\n", + " \"turbine\": {\n", + " \"name\": \"12MW Generic Turbine\",\n", + " \"rotor_diameter\": 205,\n", + " \"hub_height\": 125,\n", + " \"rated_windspeed\": 11,\n", + " \"blade\": {\n", + " \"deck_space\": 385,\n", + " \"length\": 107,\n", + " \"type\": \"Blade\",\n", + " \"mass\": 54,\n", + " },\n", + " \"nacelle\": {\n", + " \"deck_space\": 203,\n", + " \"type\": \"Nacelle\",\n", + " \"mass\": 604,\n", + " },\n", + " \"tower\": {\n", + " \"deck_space\": 50.24,\n", + " \"sections\": 2,\n", + " \"type\": \"Tower\",\n", + " \"length\": 132,\n", + " \"mass\": 399,\n", + " },\n", + " },\n", + " \"TurbineInstallation\": {\n", + " \"wtiv\": \"other_wtiv\",\n", + " \"site\": {\n", + " \"distance\": 100,\n", + " },\n", + " },\n", + " \"wtiv\": \"example_wtiv\",\n", + " \"design_phases\": [\"MonopileDesign\"],\n", + " \"install_phases\": [\"MonopileInstallation\", \"TurbineInstallation\"],\n", + "}\n", + "```\n", + "\n", + "## Phase Timing\n", + "\n", + "By default, all phases will run in the order they are defined in both the `design_phases` and\n", + "`install_phases`. When a weather profile is provided, all phases will start at the beginning of the\n", + "weather profile. To more realistically simulate the timing of installations, phase start dates\n", + "can be customized to start at a specific date, or be reliant on the completion status of a dependent\n", + "phase. The next two subsections will detail how both of these work, and can be used together.\n", + "\n", + ":::{warning}\n", + "ORBIT does not have any safety mechanisms to avoid inappropriate installation overlaps, i.e.,\n", + "installing turbines before the monopiles have been fully installed, so it is important to check\n", + "the installation timing to ensure unrealistic conditions have not been modeled.\n", + ":::\n", + "\n", + "### Defining Start Dates\n", + "\n", + "Instead of defining the `install_phases` as a list of strings for each phase, a dictionary of the\n", + "phase's class name and the string starting date should be provided. In the following example\n", + "configuration (derived from\n", + "[`examples/configs/example_fixed_project.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/examples/configs/example_fixed_project.yaml)) for a complete wind power plant, we can see how each\n", + "of the phases are staggered based on an imagined idealized starting date." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8ef99dca", + "metadata": {}, + "outputs": [], + "source": [ + "config = {\n", + " \"design_phases\": [\n", + " \"MonopileDesign\",\n", + " \"ScourProtectionDesign\",\n", + " \"ArraySystemDesign\",\n", + " \"ExportSystemDesign\",\n", + " \"OffshoreSubstationDesign\",\n", + " ],\n", + " \"install_phases\": {\n", + " \"MonopileInstallation\": \"04/01/2020\",\n", + " \"TurbineInstallation\": \"05/01/2020\",\n", + " \"ArrayCableInstallation\": \"08/01/2020\",\n", + " \"OffshoreSubstationInstallation\": \"08/01/2020\",\n", + " \"ScourProtectionInstallation\": \"03/01/2021\",\n", + " \"ExportCableInstallation\": \"08/15/2020\",\n", + " },\n", + " \"turbine\": \"12MW_generic\",\n", + " \"project_parameters\": {\"turbine_capex\": 1500},\n", + " \"site\": {\n", + " \"depth\": 22.5,\n", + " \"distance\": 124,\n", + " \"distance_to_landfall\": 35,\n", + " \"mean_windspeed\": 9,\n", + " },\n", + " \"plant\": {\n", + " \"layout\": \"grid\",\n", + " \"num_turbines\": 50,\n", + " \"row_spacing\": 7,\n", + " \"substation_distance\": 1,\n", + " \"turbine_spacing\": 7,\n", + " },\n", + " \"array_system_design\": {\"cables\": [\"XLPE_630mm_33kV\", \"XLPE_400mm_33kV\"]},\n", + " \"export_system_design\": {\n", + " \"cables\": \"XLPE_500mm_132kV\",\n", + " \"percent_added_length\": 0.0,\n", + " \"landfall\": {\"interconnection_distance\": 3, \"trench_length\": 2},\n", + " },\n", + " \"scour_protection_design\": {\"cost_per_tonne\": 40, \"scour_protection_depth\": 1},\n", + " \"OffshoreSubstationInstallation\": {\n", + " \"feeder\": \"example_heavy_feeder\",\n", + " \"num_feeders\": 1,\n", + " },\n", + " \"wtiv\": \"example_wtiv\",\n", + " \"feeder\": \"example_heavy_feeder\",\n", + " \"num_feeders\": 2,\n", + " \"spi_vessel\": \"example_scour_protection_vessel\",\n", + " \"oss_install_vessel\": \"example_heavy_lift_vessel\",\n", + " \"array_cable_install_vessel\": \"example_cable_lay_vessel\",\n", + " \"export_cable_bury_vessel\": \"example_cable_lay_vessel\",\n", + " \"export_cable_install_vessel\": \"example_cable_lay_vessel\",\n", + "}\n", + "\n", + "project = ProjectManager(config)\n", + "project.run()" + ] + }, + { + "cell_type": "markdown", + "id": "be12cc7c", + "metadata": {}, + "source": [ + "Now, we can make a quick visualization to see how the start timing plays out. Notice how the\n", + "monopile and turbine installations overlap yet there is only a single WTIV assigned to the site. In\n", + "practice this should not be possible, but is helpful to highlight why care is needed when\n", + "configuring phase timing." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b1cafa79", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame.from_dict(project.phase_dates).T\n", + "df.start = pd.to_datetime(df.start)\n", + "df.end = pd.to_datetime(df.end)\n", + "df.sort_values(\"start\")\n", + "\n", + "fig = plt.figure(figsize=(10, 4))\n", + "ax = fig.add_subplot(111)\n", + "\n", + "ax.barh(y=df.index, width=df.end - df.start, left=df.start);\n", + "\n", + "ax.grid(axis=\"x\")\n", + "ax.set_axisbelow(True)\n", + "ax.set_xlim(pd.to_datetime(\"2020-03\"), pd.to_datetime(\"2021-05\"))\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "032effe3", + "metadata": {}, + "source": [ + "(phase-dependent-timing)=\n", + "### Phase Dependent Timing\n", + "\n", + "The other method to configure installation phase timing is to define dependent phase completion\n", + "rates. For instance, instead of providing `\"TurbineInstallation\": \"05/01/2020\"`, we could simply\n", + "wait until 30% of the monopiles are installed by providing ``\"TurbineInstallation\": (\"MonopileInstallation\", 0.3)`. Below, we rely on dependencies instead of dates for nearly all\n", + "phases and show the results. Note, that mixed date and dependency inputs are allowed." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a3377bb5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dependent_starts = {\n", + " \"MonopileInstallation\": \"04/01/2020\",\n", + " \"TurbineInstallation\": (\"MonopileInstallation\", 0.3),\n", + " \"ArrayCableInstallation\": (\"TurbineInstallation\", 0.25),\n", + " \"OffshoreSubstationInstallation\": \"08/01/2020\",\n", + " \"ScourProtectionInstallation\": (\"ArrayCableInstallation\", 0.8),\n", + " \"ExportCableInstallation\": (\"OffshoreSubstationInstallation\", 1),\n", + "}\n", + "config[\"install_phases\"] = dependent_starts\n", + "project = ProjectManager(config)\n", + "project.run()\n", + "\n", + "df = pd.DataFrame.from_dict(project.phase_dates).T\n", + "df.start = pd.to_datetime(df.start)\n", + "df.end = pd.to_datetime(df.end)\n", + "df.sort_values(\"start\")\n", + "\n", + "fig = plt.figure(figsize=(10, 4))\n", + "ax = fig.add_subplot(111)\n", + "\n", + "ax.barh(y=df.index, width=df.end - df.start, left=df.start);\n", + "\n", + "ax.grid(axis=\"x\")\n", + "ax.set_axisbelow(True)\n", + "ax.set_xlim(pd.to_datetime(\"2020-03\"), pd.to_datetime(\"2021-05\"))\n", + "fig.tight_layout()" + ] + } + ], + "metadata": { + "jupytext": { + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.19.1" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "source_map": [ + 12, + 21, + 33, + 42, + 51, + 58, + 99, + 107, + 112, + 120, + 123, + 208, + 263, + 270, + 285, + 295 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/supply_chain_dev.ipynb b/examples/supply_chain_dev.ipynb deleted file mode 100644 index 040e9664..00000000 --- a/examples/supply_chain_dev.ipynb +++ /dev/null @@ -1,441 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 34, - "id": "ad19946b-c043-4e6a-a62c-c5ba4c94389d", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "from copy import deepcopy\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from ORBIT import ProjectManager, load_config\n", - "from ORBIT.phases.install import MonopileInstallation, JacketInstallation\n", - "\n", - "weather = pd.read_csv(\"data/example_weather.csv\", parse_dates=[\"datetime\"])\\\n", - " .set_index(\"datetime\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "718a655b-1104-4c5d-a8e4-1e74f6353c3c", - "metadata": {}, - "outputs": [], - "source": [ - "fixed_config = load_config(\"configs/example_fixed_project.yaml\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "bc5d3a88-d57d-4d1a-b394-726991a9d07b", - "metadata": {}, - "outputs": [], - "source": [ - "fixed_config[\"jacket\"] = {\n", - " \"diameter\": 10,\n", - " \"height\": 100,\n", - " \"length\": 100,\n", - " \"mass\": 100,\n", - " \"deck_space\": 100,\n", - " \"unit_cost\": 1e6\n", - "}\n", - "\n", - "# fixed_config[\"feeder\"] = \"example_feeder\"\n", - "# fixed_config[\"num_feeders\"] = 2\n", - "\n", - "# fixed_config[\"transition_piece\"] = {\n", - "# \"mass\": 1000,\n", - "# \"deck_space\": 1000,\n", - "# \"unit_cost\": 1e6\n", - "# }\n", - "\n", - "# fixed_config[\"jacket_supply_chain\"] = {\n", - "# \"enabled\": True,\n", - "# \"substructure_delivery_time\": 100,\n", - "# \"num_substructures_delivered\": 2,\n", - "# }" - ] - }, - { - "cell_type": "markdown", - "id": "1bc285b7-68a6-4498-b026-59f9d1b33c5c", - "metadata": {}, - "source": [ - "### Jacket Installation w/ Unlimited Storage at Port" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "8f86edb1-74fb-43b5-97b5-0a815a1a0693", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installation Time: 7720h\n" - ] - } - ], - "source": [ - "project = JacketInstallation(fixed_config, weather=weather)\n", - "project.run()\n", - "\n", - "df = pd.DataFrame(project.env.actions)\n", - "\n", - "print(f\"Installation Time: {project.total_phase_time:.0f}h\")" - ] - }, - { - "cell_type": "markdown", - "id": "a78686b8-3cc9-4ab2-a732-45afb3167017", - "metadata": {}, - "source": [ - "### Insufficient Substructure Fabrication" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "e03c5be5-8636-4b11-a731-70dd19b0797c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installation Time: 12981h\n" - ] - } - ], - "source": [ - "config = deepcopy(fixed_config)\n", - "\n", - "config[\"jacket_supply_chain\"] = {\n", - " \"enabled\": True,\n", - " \"substructure_delivery_time\": 500,\n", - " \"num_substructures_delivered\": 2,\n", - "}\n", - "\n", - "project = JacketInstallation(config, weather=weather)\n", - "project.run()\n", - "\n", - "df = pd.DataFrame(project.env.actions)\n", - "print(f\"Installation Time: {project.total_phase_time:.0f}h\")\n", - "\n", - "# Insufficient fabrication caused a ~5k hour delay" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "e7cab6f9-cddd-4eb6-a613-4e42728799c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "deliveries = df.loc[df['action'].str.contains('Delivered')][['action', 'time']]\n", - "deliveries['number'] = deliveries['action'].apply(lambda x: int(x.split(\" \")[1]))\n", - "# deliveries\n", - "\n", - "installs = df.loc[df['action'].str.contains('Grout Jacket')][['action', 'time']]\n", - "installs['number'] = 1\n", - "\n", - "fig = plt.figure(figsize=(6,3), dpi=200)\n", - "ax = fig.add_subplot(111)\n", - "\n", - "ax.scatter(deliveries['time'], deliveries['number'], s=10, label=\"Substructure(s) Delivered\")\n", - "ax.scatter(installs['time'], installs['number'], s=10, label=\"Completed Installation\")\n", - "\n", - "ax.set_xlim(0, ax.get_xlim()[1])\n", - "ax.set_ylim(0, 5)\n", - "\n", - "ax.set_xlabel(\"Simulation Time\")\n", - "ax.set_ylabel(\"Substructures\")\n", - "\n", - "ax.legend()\n", - "\n", - "# Note period of bad weather at ~10k hours" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "fe12d4b3-1c9a-420a-b3fb-d6fba538822d", - "metadata": {}, - "outputs": [], - "source": [ - "installs_neg = installs.copy()\n", - "installs_neg[\"number\"] *= -1\n", - "\n", - "total = pd.concat([deliveries, installs_neg]).sort_values('time')\n", - "total['storage'] = total['number'].cumsum()\n", - "# total" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "ea3f0a81-5894-42a5-b2f4-f6dcea1b5465", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(6,3), dpi=200)\n", - "ax = fig.add_subplot(111)\n", - "\n", - "ax.plot(total['time'], total['storage'], label=\"Storage Required\")\n", - "\n", - "ax.set_xlim(0, ax.get_xlim()[1])\n", - "# ax.set_ylim(0, 5)\n", - "\n", - "ax.axhline(4, ls=\"--\", lw=0.5, c='k')\n", - "\n", - "ax.set_xlabel(\"Simulation Time (h)\")\n", - "ax.set_ylabel(\"Substructures\")\n", - "\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "2fe95e59-8405-4493-a117-bf147eb2af99", - "metadata": {}, - "source": [ - "### Increased Substructure Fabrication" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "685864c6-b73d-4344-9700-8c9f7daecddd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installation Time: 7850h\n" - ] - } - ], - "source": [ - "config = deepcopy(fixed_config)\n", - "\n", - "config[\"jacket_supply_chain\"] = {\n", - " \"enabled\": True,\n", - " \"substructure_delivery_time\": 250,\n", - " \"num_substructures_delivered\": 2,\n", - "}\n", - "\n", - "project = JacketInstallation(config, weather=weather)\n", - "project.run()\n", - "\n", - "df = pd.DataFrame(project.env.actions)\n", - "print(f\"Installation Time: {project.total_phase_time:.0f}h\")" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "e01ca9cc-9603-4d89-b781-4e646246263d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "deliveries = df.loc[df['action'].str.contains('Delivered')][['action', 'time']]\n", - "deliveries['number'] = deliveries['action'].apply(lambda x: int(x.split(\" \")[1]))\n", - "# deliveries\n", - "\n", - "installs = df.loc[df['action'].str.contains('Grout Jacket')][['action', 'time']]\n", - "installs['number'] = 1\n", - "\n", - "fig = plt.figure(figsize=(6,3), dpi=200)\n", - "ax = fig.add_subplot(111)\n", - "\n", - "ax.scatter(deliveries['time'], deliveries['number'], s=10, label=\"Substructure(s) Delivered\")\n", - "ax.scatter(installs['time'], installs['number'], s=10, label=\"Completed Installation\")\n", - "\n", - "ax.set_xlim(0, ax.get_xlim()[1])\n", - "ax.set_ylim(0, 5)\n", - "\n", - "ax.set_xlabel(\"Simulation Time\")\n", - "ax.set_ylabel(\"Substructures\")\n", - "\n", - "ax.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "cd4e31d9-bbda-4f7d-8df8-33f447231a78", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "installs_neg = installs.copy()\n", - "installs_neg[\"number\"] *= -1\n", - "\n", - "total = pd.concat([deliveries, installs_neg]).sort_values('time')\n", - "total['storage'] = total['number'].cumsum()\n", - "\n", - "fig = plt.figure(figsize=(6,3), dpi=200)\n", - "ax = fig.add_subplot(111)\n", - "\n", - "ax.plot(total['time'], total['storage'], label=\"Storage Required\")\n", - "\n", - "ax.set_xlim(0, ax.get_xlim()[1])\n", - "# ax.set_ylim(0, 5)\n", - "\n", - "ax.axhline(4, ls=\"--\", lw=0.5, c='k')\n", - "\n", - "ax.set_xlabel(\"Simulation Time (h)\")\n", - "ax.set_ylabel(\"Substructures\")\n", - "\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "ea782cce-81ae-426f-ba5c-8d375e6f0abb", - "metadata": {}, - "source": [ - "### Questions\n", - "\n", - "- Is this approach and the results valuable to Equinor?\n", - "- How to quantify the project cost associated with increased storage required?\n", - " - $/substructure/day\n", - "\n", - " - $/laydown space/day\n", - "\n", - "- How to quantify the delivery costs associated with substructure delivery?\n", - " - $/substructure\n", - "\n", - " - $/substructure/km\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee9c8a0b-2003-4424-a551-8cff89241249", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/supply_chains.ipynb b/examples/supply_chains.ipynb new file mode 100644 index 00000000..8cea7d9a --- /dev/null +++ b/examples/supply_chains.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6960a153", + "metadata": {}, + "source": [ + "# Modeling Supply Chains\n", + "\n", + "In this example we will model the effects of the supply chain on substructure fabrication on\n", + "jacket-pile installations. For this example we will consider the following three cases:\n", + "\n", + "1. Jacket installations with unlimited port storage.\n", + "2. Insufficient jacket fabrication to match installation rates.\n", + "3. Increased jacket fabrication to keep pace with installation rates." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f9d4941c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "UserWarning: /var/folders/q5/tfpytqxn0r396dfg7rk5sj8rwq9tvv/T/ipykernel_41557/774869178.py:14\n", + "Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n" + ] + } + ], + "source": [ + "from copy import deepcopy\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from ORBIT import ProjectManager, load_config\n", + "from ORBIT.phases.install import MonopileInstallation, JacketInstallation\n", + "\n", + "# Set the example path for use in the docs and standalone examples usage\n", + "here = Path(\".\").resolve()\n", + "example_path = here.parents[1] / \"examples\" if here.stem == \"topical_guides\" else here\n", + "\n", + "weather = pd.read_csv(\n", + " example_path / \"data/example_weather.csv\", parse_dates=[\"datetime\"]\n", + ").set_index(\"datetime\")" + ] + }, + { + "cell_type": "markdown", + "id": "53a6ee92", + "metadata": {}, + "source": [ + "## Preparing The Cases\n", + "\n", + "Here, we will load the\n", + "[`examples/configs/example_fixed_project.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/examples/configs/example_fixed_project.yaml)\n", + "configuration and modify it to run a jacket installation process. Please note that there is no\n", + "jacket design model in ORBIT, so we must provide the basic jacket parameterizations for the\n", + "design result." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b5ef1a8d", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'configs/example_fixed_project.yaml'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m base_jacket_config = \u001b[43mload_config\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mconfigs/example_fixed_project.yaml\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m base_jacket_config[\u001b[33m\"\u001b[39m\u001b[33mjacket\u001b[39m\u001b[33m\"\u001b[39m] = {\n\u001b[32m 3\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mdiameter\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m10\u001b[39m,\n\u001b[32m 4\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mheight\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m100\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 8\u001b[39m \u001b[33m\"\u001b[39m\u001b[33munit_cost\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m1e6\u001b[39m\n\u001b[32m 9\u001b[39m }\n\u001b[32m 11\u001b[39m slow_prod_config = deepcopy(base_jacket_config)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/GitHub_Public/ORBIT/ORBIT/config.py:29\u001b[39m, in \u001b[36mload_config\u001b[39m\u001b[34m(filepath)\u001b[39m\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mload_config\u001b[39m(filepath):\n\u001b[32m 20\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 21\u001b[39m \u001b[33;03m Load an ORBIT config at `filepath`.\u001b[39;00m\n\u001b[32m 22\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 26\u001b[39m \u001b[33;03m Path to yaml config file.\u001b[39;00m\n\u001b[32m 27\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m29\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m 30\u001b[39m data = yaml.load(f, Loader=loader)\n\u001b[32m 32\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/orbit/lib/python3.11/pathlib.py:1044\u001b[39m, in \u001b[36mPath.open\u001b[39m\u001b[34m(self, mode, buffering, encoding, errors, newline)\u001b[39m\n\u001b[32m 1042\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1043\u001b[39m encoding = io.text_encoding(encoding)\n\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m io.open(\u001b[38;5;28mself\u001b[39m, mode, buffering, encoding, errors, newline)\n", + "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'configs/example_fixed_project.yaml'" + ] + } + ], + "source": [ + "base_jacket_config = load_config(\"configs/example_fixed_project.yaml\")\n", + "base_jacket_config[\"jacket\"] = {\n", + " \"diameter\": 10,\n", + " \"height\": 100,\n", + " \"length\": 100,\n", + " \"mass\": 100,\n", + " \"deck_space\": 100,\n", + " \"unit_cost\": 1e6\n", + "}\n", + "\n", + "slow_prod_config = deepcopy(base_jacket_config)\n", + "slow_prod_config[\"jacket_supply_chain\"] = {\n", + " \"enabled\": True,\n", + " \"substructure_delivery_time\": 500,\n", + " \"num_substructures_delivered\": 2,\n", + "}\n", + "\n", + "increased_prod_config = deepcopy(base_jacket_config)\n", + "increased_prod_config[\"jacket_supply_chain\"] = {\n", + " \"enabled\": True,\n", + " \"substructure_delivery_time\": 250,\n", + " \"num_substructures_delivered\": 2,\n", + "}\n", + "\n", + "case1_project = JacketInstallation(base_jacket_config, weather=weather)\n", + "case2_project = JacketInstallation(slow_prod_config, weather=weather)\n", + "case3_project = JacketInstallation(increased_prod_config, weather=weather)\n", + "\n", + "case1_project.run()\n", + "case2_project.run()\n", + "case3_project.run()" + ] + }, + { + "cell_type": "markdown", + "id": "55a83b0a", + "metadata": {}, + "source": [ + "## Comparing Results\n", + "\n", + "### Installation Time" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "deb4354f", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'case1_project' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCase 1 Installation Time: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mcase1_project\u001b[49m.total_phase_time\u001b[38;5;250m \u001b[39m/\u001b[38;5;250m \u001b[39m\u001b[32m24\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m days\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCase 2 Installation Time: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcase2_project.total_phase_time\u001b[38;5;250m \u001b[39m/\u001b[38;5;250m \u001b[39m\u001b[32m24\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m days\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCase 3 Installation Time: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcase3_project.total_phase_time\u001b[38;5;250m \u001b[39m/\u001b[38;5;250m \u001b[39m\u001b[32m24\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m days\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mNameError\u001b[39m: name 'case1_project' is not defined" + ] + } + ], + "source": [ + "print(f\"Case 1 Installation Time: {case1_project.total_phase_time / 24:.1f} days\")\n", + "print(f\"Case 2 Installation Time: {case2_project.total_phase_time / 24:.1f} days\")\n", + "print(f\"Case 3 Installation Time: {case3_project.total_phase_time / 24:.1f} days\")" + ] + }, + { + "cell_type": "markdown", + "id": "294d4123", + "metadata": {}, + "source": [ + "### Installation Timing\n", + "\n", + "Below, we plot the timing of jacket deliveries and their subsequent installations without\n", + "considering vessel logistic delays. Note the inclement weather around the 400th day of\n", + "Case 2's installation simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "46264357", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'case2_project' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m case2_df = pd.DataFrame(\u001b[43mcase2_project\u001b[49m.env.actions)\n\u001b[32m 2\u001b[39m case2_deliveries = case2_df.loc[case2_df[\u001b[33m\"\u001b[39m\u001b[33maction\u001b[39m\u001b[33m\"\u001b[39m].str.contains(\u001b[33m\"\u001b[39m\u001b[33mDelivered\u001b[39m\u001b[33m\"\u001b[39m), [\u001b[33m\"\u001b[39m\u001b[33maction\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mtime\u001b[39m\u001b[33m\"\u001b[39m]]\n\u001b[32m 3\u001b[39m case2_deliveries[\u001b[33m\"\u001b[39m\u001b[33mnumber\u001b[39m\u001b[33m\"\u001b[39m] = case2_deliveries[\u001b[33m\"\u001b[39m\u001b[33maction\u001b[39m\u001b[33m\"\u001b[39m].str.split(\u001b[33m\"\u001b[39m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m).str[\u001b[32m1\u001b[39m].astype(\u001b[38;5;28mint\u001b[39m)\n", + "\u001b[31mNameError\u001b[39m: name 'case2_project' is not defined" + ] + } + ], + "source": [ + "case2_df = pd.DataFrame(case2_project.env.actions)\n", + "case2_deliveries = case2_df.loc[case2_df[\"action\"].str.contains(\"Delivered\"), [\"action\", \"time\"]]\n", + "case2_deliveries[\"number\"] = case2_deliveries[\"action\"].str.split(\" \").str[1].astype(int)\n", + "\n", + "case2_installs = case2_df.loc[case2_df[\"action\"].str.contains(\"Grout Jacket\"), [\"action\", \"time\"]]\n", + "case2_installs[\"number\"] = 1\n", + "\n", + "case2_deliveries.time /= 24.0\n", + "case2_installs.time /= 24.0\n", + "\n", + "\n", + "case3_df = pd.DataFrame(case3_project.env.actions)\n", + "case3_deliveries = case3_df.loc[case3_df[\"action\"].str.contains(\"Delivered\"), [\"action\", \"time\"]]\n", + "case3_deliveries[\"number\"] = case3_deliveries[\"action\"].str.split(\" \").str[1].astype(int)\n", + "\n", + "case3_installs = case3_df.loc[case3_df[\"action\"].str.contains(\"Grout Jacket\"), [\"action\", \"time\"]]\n", + "case3_installs[\"number\"] = 1\n", + "\n", + "case3_deliveries.time /= 24.0\n", + "case3_installs.time /= 24.0" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "adef5c81", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'case2_deliveries' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m fig = plt.figure(figsize=(\u001b[32m8\u001b[39m,\u001b[32m4\u001b[39m), dpi=\u001b[32m200\u001b[39m)\n\u001b[32m 2\u001b[39m ax = fig.add_subplot(\u001b[32m111\u001b[39m)\n\u001b[32m 4\u001b[39m ax.scatter(\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[43mcase2_deliveries\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mtime\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m 6\u001b[39m case2_deliveries[\u001b[33m\"\u001b[39m\u001b[33mnumber\u001b[39m\u001b[33m\"\u001b[39m].cumsum(),\n\u001b[32m 7\u001b[39m marker=\u001b[33m\"\u001b[39m\u001b[33mo\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 8\u001b[39m c=\u001b[33m\"\u001b[39m\u001b[33mtab:blue\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 9\u001b[39m label=\u001b[33m\"\u001b[39m\u001b[33mSubstructure Delivered - Slow Fabrication\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 10\u001b[39m )\n\u001b[32m 11\u001b[39m ax.scatter(\n\u001b[32m 12\u001b[39m case2_installs[\u001b[33m\"\u001b[39m\u001b[33mtime\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m 13\u001b[39m case2_installs[\u001b[33m\"\u001b[39m\u001b[33mnumber\u001b[39m\u001b[33m\"\u001b[39m].cumsum(),\n\u001b[32m (...)\u001b[39m\u001b[32m 16\u001b[39m label=\u001b[33m\"\u001b[39m\u001b[33mCompleted Installation - Slow Fabrication\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 17\u001b[39m )\n\u001b[32m 19\u001b[39m ax.scatter(\n\u001b[32m 20\u001b[39m case3_deliveries[\u001b[33m\"\u001b[39m\u001b[33mtime\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m 21\u001b[39m case3_deliveries[\u001b[33m\"\u001b[39m\u001b[33mnumber\u001b[39m\u001b[33m\"\u001b[39m].cumsum(),\n\u001b[32m (...)\u001b[39m\u001b[32m 24\u001b[39m label=\u001b[33m\"\u001b[39m\u001b[33mSubstructure Delivered - Increased Fabrication\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 25\u001b[39m )\n", + "\u001b[31mNameError\u001b[39m: name 'case2_deliveries' is not defined" + ] + }, + { + "data": { + "image/png": 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BAAAAABIIVQEAAAAAEghVAQAAAAASCFUBAAAAABIIVQEAAAAAEghVAQAAAAASCFUBAAAAABIIVQEAAAAAEghVAQAAAAASCFUBAAAAABIIVQEAAAAAEghVAQAAAAASCFUBAAAAABIIVQEAAAAAEghVAQAAAAASCFUBAAAAABIIVQEAAAAAEghVAQAAAAASCFUBAAAAABIIVQEAAAAAEghVAQAAAAASCFUBAAAAAGpjqLp06dIwdOjQ0LFjx9CiRYuw7777hu7du4fRo0eHTz75pNrqPPHEE+Gss84Kbdu2DU2aNMmO8ev4fQAAAACAXVWUy+VyocCmTJkSBgwYEFavXl3h8x06dAhTp04NJSUlVa6xefPmcNlll4UJEyZU2ueSSy4Jv/rVr0KDBtWfJZeWloZ27dpl7WXLlmVhLgAAAACw5+VrBR+punDhwnDeeedlgWrLli3DiBEjwty5c8P06dPDpZdemvVZsmRJ6NevX1izZk2V6/zoRz/KB6pdunQJDz30UJg/f352jF9H48ePDz/+8Y+r6Z0BAAAAAPVRwUeqfvWrXw3PPfdcaNSoUXj22WdDz549t3o+Tv+/4YYbsvYtt9wShg8fnlwjhrJHHXVU2LhxY+jWrVtWp1mzZvnn4/ICvXv3Di+++GJ2HYsWLdqlUbF1PUkHAAAAgNqmtA7lawUdqRpHisZANbr44ou3CVSjuM5qp06dsvbYsWPDZ599llzn5z//eRaoRr/4xS+2ClSj5s2bZ9+PYr+77rqrSu8HAAAAAKCgoerkyZPz7QsvvLDiC2jQIFxwwQVZe9WqVWHGjBlJNeJA20cffTRrx02wvvKVr1TYL37/yCOPzNqxfw0sJQsAAAAA7IEKGqrOnj07O7Zo0SJ07dq10n5xan65OXPmJNV46623wrvvvrvNebZX5+9//3t4++23k+oAAAAAABQ8VI1rl0Zx/dK4lmll4gjTz79mZ7366qsVnqe66wAAAAAARJUnnbvo008/DcuXL8/aO1pUtnXr1tlo1nXr1mWL0KYuYFtuR3XKF7qNdqVORbY833vvvZd0bgAAAACo797bIlMr3z+p3oWqa9asybdbtmy5w/7loeratWsLVifWKJdaZ8tAdkd69OiRdG4AAAAA4P989NFH4dBDDw31bvp/HKlabq+99tph/yZNmmTHsrKygtUpr1GVOgAAAABAzfjggw929yXsnpGqTZs2zbc3bNiww/7r16/Pjs2aNStYnfIaVamzo+UC4oZZX/3qV7P23Llzk0a2ArV/+kH5CPT58+eHgw46aHdfElBN3N+w53J/w57L/Q17rmXLloXjjz9+p/ZO2mND1eLi4qSp9nHq/84uFVDVOuU1qlJnR+u1bikGqin9gboj/sLm/oY9k/sb9lzub9hzub9hz9V0i4GU9Wr6f3zjbdq02alNnlauXJkPPFNHeG75j2fKZlJGkgIAAAAAtSpUjTp37pwd33jjje3u2LV48eJ8u1OnTlWq8fnzVHcdAAAAAICCh6onnHBCdoyjUBcsWFBpv1mzZuXbvXr1Sqpx2GGHhS9+8YvbnKcizz77bHY8+OCDa/XuYQAAAABAPQ1VzzzzzHz7/vvvr7DP5s2bw8SJE7N2q1atQp8+fZJqFBUVhTPOOCM/EvX555+vsF/8fvlI1dg/vg4AAAAAIFVBQ9W4G9+JJ56YtSdMmBDmzZu3TZ8xY8aERYsWZe3BgweHxo0bb/X8zJkzswA0PgYNGlRhnSFDhoSGDRtm7WuuuSaUlZVt9Xz8On4/atSoUdYfAAAAAKDWharR2LFjQ7NmzbI1Vfv27RtGjhyZjRqdMWNGuPzyy8MNN9yQ9evQoUMYOnRolWrE115//fVZ+8UXX8yWEHj44YezdjzGr2M7iv2OOOKIanyHAAAAAEB90qjQBbp06ZIFmwMGDAirV68Ow4YNqzAUnTp1aiguLq5ynREjRoQPP/ww3HfffWHhwoXh29/+9jZ9Lr744nD77bdXuQYAAAAAQFEul8vVRKGlS5dmo1ZjeFpaWhr22muvUFJSEv71X/81XH311aF58+YVvi5O/y9fZ3XgwIHhgQce2G6dadOmhXHjxoUXXnghLF++POy3336he/fu2ajY0047rSDvDQAAAACoP2osVAUAAAAA2BMUfE1VAAAAAIA9iVAVAAAAACCBUBUAAAAAIIFQFQAAAAAggVAVAAAAACCBUBUAAAAAIIFQFQAAAAAggVAVAAAAACCBUPVzli5dGoYOHRo6duwYWrRoEfbdd9/QvXv3MHr06PDJJ59UW50nnnginHXWWaFt27ahSZMm2TF+Hb8P1L37O77+kUceCd///vezc7Zu3To0btw4tGnTJvTs2TMMHz48vP/++9X2XoDd8/N7S/G8X/rSl0JRUVH2OPTQQwtSB+q7mry/n3nmmTBo0KBQUlKS1dpnn31Chw4dwjnnnBPuueeesHbt2mqtB/VdTdzfb7/9drjxxhtD165dQ6tWrbLf0WOd448/Ptx6663hww8/rJY6QMjup8cffzzcfPPN4bTTTgv77bdf/nfl+PO1EB566KHQt2/fcOCBB4amTZuG9u3bhwEDBoR58+aFgsuR99hjj+X23nvvXPzPUtGjQ4cOuddff32XamzatCl38cUXV1ojPi655JKsH1A37u+XXnop17Jly+3e1/ER60+aNKna3xvUdzXx87siQ4cO3apO+/btq70G1Hc1dX+vWLEid8YZZ+zwZ/nChQur5X0BNXN/T5w4MdesWbPt3tf77rtv7qmnnqq29wX1WdjOvTZw4MBqrfXJJ5/kvvnNb1Zar0GDBrnhw4fnCslI1f+1cOHCcN5554XVq1eHli1bhhEjRoS5c+eG6dOnh0svvTTrs2TJktCvX7+wZs2aKtf50Y9+FCZMmJC1u3TpkiXq8+fPz47x62j8+PHhxz/+cTW9M6DQ93c8b/nIlV69eoWRI0eGp59+OvzpT38KTz75ZLj88stDgwYNsn7f+973jEiHOvjzu6K6P//5z7NPw4uLi6vtvEDN398ff/xxOPXUU8Ojjz6afR1nj/32t78Nzz//fHjhhReymSiDBw/OZpYBdef+njNnTjYyrqysLPtd/MILLwyTJ0/O/v7+3e9+F/r375/1W7FiRTjjjDPC3/72t2p9j1DfHXLIIdkI0kK56KKLwrRp07J2nz598vd3zNwOP/zwsHnz5mzG6Lhx4wp2DUaq/q8TTzwxS7IbNWqUmzt37jbP33nnnfm0+5ZbbqlSjddeey07fzxHt27dslR9S+vWrcu+X34dhRhVA/VRoe/vOXPm5M4999zcX//610r7TJ48OVdUVJTVOPzww3ObN29OrgPsnp/fn7dx48Zc165ds3Peeuut2QhVI1Wh7t7f559/fnaOJk2a5B599NFK+8Wf3Z999lmV6wA1e3/369cvf4677767wj7XXXddvs9VV11VpTrA/7n55ptzU6ZMyb3//vvZ12+99VZBRqpOnz49f97+/ftnv59v6aOPPsodcsgh2fOtWrXKZqQUglA1l8v9z//8T/5/xuWXX15hnzgdv1OnTvn/IRs2bEiu8/3vfz9fZ968eRX2id8v73PllVcm1wB2z/29M84+++z8tSxYsKAgNaA+2V3395gxY7LzHXnkkbn169cLVaEO39/PPfdcvs7o0aOr4cqB2nJ/t27dOnt9mzZtKu2zatWq/LUcd9xxyTWA7StUqHraaaflP5hZtmxZhX0eeuihfO34QU0hmP4fQjZEuFycElCROF3gggsuyNqrVq0KM2bMSKoRA+zyKUVxEe6vfOUrFfaL3z/yyCOzduz//y9JAdTm+3tnxSkJ5d58882C1ID6ZHfc33FDjbjwfnTvvfeGvfbaa5fOB+ze+/uXv/xldowbUl199dVVvl6g9t3fGzZsyI6HHXZYpX3ivR830tmyP1C7rVmzJlsqJDrllFMqXZ7nW9/6Vth7772z9h/+8IeCXItQNYQwe/bs7Bh3G4w7Alamd+/eW63PkuKtt94K77777jbn2V6dv//979lOhUDtvr931vr16/Pthg0bFqQG1Ce74/6+8sorw7p168L5558fvva1r+3SuYDde3/HAKV80ENcUzWukRxt2rQpLFu2LPs9/NNPP63iOwB298/v8sFK8W/xysQ1XZcvX75Vf6B2e+GFF/IfgmwvX4uDH8oHNMbXfPbZZ9V+LULVEMKiRYuyY0lJSWjUqFGl/eII08+/Zme9+uqrFZ6nuusANX9/76xZs2bl2506dSpIDahPavr+njRpUrYYfuvWrcOYMWOqfB6gdtzfL730Uj40PeaYY7JwZciQIdmotbi5RhzdFkexxcB15syZVX4vwO75+X3FFVdkx3/84x/Z7JKK3Hbbbdv0B2q3V6uQr23cuDG8/vrr1X4t9T5Ujb9IlX8ytaMdPeMfUfHTtCh+ep2itLQ0395RnXbt2uXbqXWAmr+/d/YPt6lTp+b/cBOqQt26v1euXJmFLdEdd9wR9t9//yqdB6g99/eWf5TFHYK7desWxo4dm001LhdHwjzzzDPhpJNOCqNGjUp8J8Du/PkddwYvX0LgqquuCpdeemmYMmVKePHFF8MjjzwSzjrrrPCzn/0se/5HP/pRNo0YqP1Ka1G+Vu9D1bgWQ7mWLVvusH/5P+pr164tWJ3yGlWpA9T8/b0z0/4vueSSbDphNGLEiGo9P9RHNX1/X3/99eGDDz4IPXv2zP4oA+r+/b1ixYp8OwamcQTLN77xjTB//vws+Pnwww/DPffck41Wjfsc3HTTTfnlAoDa//M7Lrf1m9/8JvzXf/1X+Kd/+qcwfvz4cPrpp4fu3buHs88+O1vbNe558PTTT4fbb789+fzA7lGb8rV6H6puuU7Szmw20aRJk+xYVlZWsDrlNapSB6j5+3tH4sYX8RPxaODAgaF///7Ven6oj2ry/n722WfDfffdl01RjNMHi4qKks8B1L77O66PvGXNOM3/8ccfzwKXeM44Ij1OB47fi5vmRD/84Q9tJAt16PfzuGzAxIkTwyuvvFLh8/PmzQsTJkzI9jMB6oZPa1G+Vu9D1fIF6Xd2t7/yjWaaNWtWsDpbbmaTWgeo+ft7e0aOHJl9Kh7FP9Luvvvuajs31Gc1dX/H11122WVZiDJ48OBw7LHHVuFqgdr++3n5aNWKNpI84YQTsh2EywOaysIZoHb9fv7cc89lM0zilP+DDz44PPjgg+H999/P6sZpwPH38ubNm2drpvfo0SP89a9/Ta4B1LzalK/V+1C1uLg4aShw+SfaOzNVoap1tvzUPLUOUPP3d2V+9atfhWHDhuUXyI4b3Gw5/QCo/fd3XK7jtddey9Zj+slPflKFKwXqwu/ncVRqly5dKu379a9/Pd+OOwgDtfv+jkHKd77znfDxxx+HAw88MDz//PNhwIAB4Qtf+EJo3Lhxtg7jlVdemc1GiQHNu+++m80oA2q/4lqUr1W+1V49Ef8BbdOmTbYj4JaL3Va2SUX5/5AtF7vdGVsunrujOlsunptaB6j5+7siDz30UPaLWtS+fftsraa4mzBQt+7v8o1p4uYVcaRLRcrPHY9xtEt0wAEHZBvbALX3/t6yf8pGFx999FFSHaDm7+8//vGP+Sn911xzTRasVuSoo47KwtY4s2zBggXZ5rJx/VWg9mr7uXwtbjS5u/K1eh+qRp07d86mBrzxxhth48aN2ZppFVm8eHG+nbpzd6xR0Xmquw5Q8/f35z322GPZTqNxJ+GDDjooTJ8+fYd/rAG18/4un1J0//33Z4/tibsZx1ExUe/evYWqUMvv7ximlCvfTLIyWz5f2bUAtef+jkt1lDvuuOO227dr16755bpiTaEq1G6dq5CvxX9njjjiiGq/lno//b98naQofgoWP52qzKxZs/LtXr16JdU47LDDwhe/+MVtzlOROAUhiuu+HHrooUl1gJq/v7cUA9Rzzz03+wUxfgofR6gefvjhVT4fUHvub2DPur/jTJJDDjkka7/99tvb3YDqzTffzLfj7+hA7b6/twxq4+/l2/PZZ59V+DqgdurevXt+g6rt5WtxcERc+qP8NXHpj+omVA0hnHnmmfl2ZaNQ4oizuGtg1KpVq9CnT5+kGnGn4DPOOCOflJf/j/28+P3yJD32t8Mw1P77u9zcuXOz+zau4bTPPvuEJ598cqtRMEDdu79jyLKjRwxmongs/97MmTN36b1BfVdTP7/PPvvs7Lh69ersg9HKPPLII9sEQkDtvb/joKZycVTs9mwZymz5OqD2rql68sknZ+1nnnmm0qVE4s/u+PM9OuusswpzMTkyJ554YvxoOteoUaPc3Llzt3n+zjvvzJ6Pj1tuuWWb52fMmJF/fuDAgRXWeO2113INGzbM+nTr1i33ySefbPV8/Dp+v/w6lixZUo3vEOqvmri/Fy5cmGvVqlXWp0WLFrnZs2cX5L0ANX9/70j79u2z18cjULfu76VLl+aaNm2a9TnmmGNyH3/88TZ9Hnzwwfx5+vXrV03vDuq3Qt/fK1euzDVv3jx7vri4OPfyyy9XeB3Tpk3LNWjQIOt38MEH5zZt2lRN7xCI3nrrreTfte+///7t3v/R9OnT831OP/303MaNG7d6/qOPPsodcsgh2fPx7/QVK1bkCsHY9v81duzYbEpBWVlZ6Nu3b7Zjd/w0LH4dN50YN25c1q9Dhw5h6NChVaoRX3v99deHO+64I7z44otZvRtvvDGbGhynFMXNMBYuXJj1jf0Ksd4D1EeFvr/j/Rt3BV61alX29e23356NVP3LX/5S6WviJjbxAdT+n9/Annt/x+n/t956a7jhhhvCK6+8Enr06JH9fn7sscdmo1viKJd77rkn67v33nuHu+66q1rfI9RXhb6/4+jWm266Kdx8881hzZo14fjjj882rDr11FND69atwwcffBAeffTR8Otf/zobFRvFv9MbNDCZF3bF7Nmzs/WSt9xzoFz8/gMPPLBV/0GDBlWpTty74Nvf/nb270Xc0yTe20OGDMmW3Yw/z0eMGBHeeeedrG/M2uJ9XxAFiWrrqMceeyy3995759Puzz86dOiQe/311yt87c6OdImffF100UWV1oiPiy++2CdkUIfu7y0/SdvZR2WfuAG18+f39hipCnX//r7ppptyRUVFldY54IADKhxNB9Te+3vz5s25IUOGbPfejo/GjRvnRo8eXeB3C/XDwIEDk/4urupI1fLZ3t/85jcrPXcchV7ov7t9DLOF/v37h5dffjlce+212SdizZs3zz7h6tatW34UaUlJyS7ViJ98TZgwIUydOjVbezGm6HGB3XiMX0+bNi3bedAnZFD37m9g93B/w56rpu7vkSNHhjlz5oTzzz8/2yi2SZMm2ayTuLHFbbfdFpYsWRJ69uxZLe8JqJn7O+5PEkeXv/DCC+GKK64IRx99dLYWY8OGDbP7u2vXruG6667LZpf94Ac/qNb3BhRes2bNsmztt7/9bTZSNc4Ejflau3btwne/+91s1Ozw4cMLeg1FMVktaAUAAAAAgD2I4ZAAAAAAAAmEqgAAAAAACYSqAAAAAAAJhKoAAAAAAAmEqgAAAAAACYSqAAAAAAAJhKoAAAAAAAmEqgAAAAAACYSqAAAAAAAJhKoAAAAAAAmEqgAAAAAACYSqAAAAAAAJhKoAAAAAAAmEqgAAAAAACYSqAAAAAAAJhKoAAAAAAAmEqgAAAAAACYSqAAAAAAAJhKoAAAAAAAmEqgAAAAAACYSqAAAAAAAJhKoAAAAAAAmEqgAAAAAACYSqAAAAAAAJhKoAAAAAAGHn/X9zf8V/BMaK+gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8,4), dpi=200)\n", + "ax = fig.add_subplot(111)\n", + "\n", + "ax.scatter(\n", + " case2_deliveries[\"time\"],\n", + " case2_deliveries[\"number\"].cumsum(),\n", + " marker=\"o\",\n", + " c=\"tab:blue\",\n", + " label=\"Substructure Delivered - Slow Fabrication\"\n", + ")\n", + "ax.scatter(\n", + " case2_installs[\"time\"],\n", + " case2_installs[\"number\"].cumsum(),\n", + " marker=\"x\",\n", + " c=\"tab:blue\",\n", + " label=\"Completed Installation - Slow Fabrication\"\n", + ")\n", + "\n", + "ax.scatter(\n", + " case3_deliveries[\"time\"],\n", + " case3_deliveries[\"number\"].cumsum(),\n", + " marker=\"o\",\n", + " c=\"tab:orange\",\n", + " label=\"Substructure Delivered - Increased Fabrication\"\n", + ")\n", + "ax.scatter(\n", + " case3_installs[\"time\"],\n", + " case3_installs[\"number\"].cumsum(),\n", + " marker=\"x\",\n", + " c=\"tab:orange\",\n", + " label=\"Completed Installation - Increased Fabrication\"\n", + ")\n", + "\n", + "ax.set_xlim(0, ax.get_xlim()[1])\n", + "ax.set_ylim(0, 60)\n", + "\n", + "ax.set_xlabel(\"Simulation Time (Days)\")\n", + "ax.set_ylabel(\"Substructures\")\n", + "\n", + "ax.legend()\n", + "ax.grid()\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "d4c63f6e", + "metadata": {}, + "source": [ + "### Port Storage" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ec7f2b39", + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "unterminated string literal (detected at line 28) (3778338468.py, line 28)", + "output_type": "error", + "traceback": [ + " \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 28\u001b[39m\n\u001b[31m \u001b[39m\u001b[31max.axhline(4, ls=\"--\", lw=0.5, c='k', label=\"Theoretical Port Storage Limit)\u001b[39m\n ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m unterminated string literal (detected at line 28)\n" + ] + } + ], + "source": [ + "fig = plt.figure(figsize=(8,4), dpi=200)\n", + "ax = fig.add_subplot(111)\n", + "\n", + "case2_installs_neg = case2_installs.copy()\n", + "case2_installs_neg[\"number\"] *= -1\n", + "case2_total = pd.concat([case2_deliveries, case2_installs_neg]).sort_values('time')\n", + "case2_total['storage'] = case2_total['number'].cumsum()\n", + "\n", + "case3_installs_neg = case3_installs.copy()\n", + "case3_installs_neg[\"number\"] *= -1\n", + "case3_total = pd.concat([case3_deliveries, case3_installs_neg]).sort_values('time')\n", + "case3_total['storage'] = case3_total['number'].cumsum()\n", + "\n", + "ax.plot(\n", + " case2_total['time'],\n", + " case2_total['storage'],\n", + " label=\"Storage Required - Slow Fabrication\"\n", + ")\n", + "ax.plot(\n", + " case3_total['time'],\n", + " case3_total['storage'],\n", + " label=\"Storage Required - Increased Fabrication\"\n", + ")\n", + "\n", + "ax.set_xlim(0, ax.get_xlim()[1])\n", + "# ax.set_ylim(0, 5)\n", + "\n", + "ax.axhline(4, ls=\"--\", lw=0.5, c='k', label=\"Theoretical Port Storage Limit)\n", + "\n", + "ax.set_xlabel(\"Simulation Time (h)\")\n", + "ax.set_ylabel(\"Substructures\")\n", + "\n", + "ax.legend()" + ] + } + ], + "metadata": { + "jupytext": { + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.19.1" + } + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "source_map": [ + 12, + 23, + 40, + 50, + 82, + 88, + 92, + 100, + 123, + 166, + 170 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/library/__init__.py b/library/__init__.py index 5d3ded35..b38dc6ea 100644 --- a/library/__init__.py +++ b/library/__init__.py @@ -1,5 +1,5 @@ __author__ = ["Rob Hammond", "Jake Nunemaker"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = ["jake.nunemaker@nrel.gov", "rob.hammond@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov", "rob.hammond@nlr.gov"] __status__ = "Development" diff --git a/library/ports/__init__.py b/library/ports/__init__.py index 5d3ded35..b38dc6ea 100644 --- a/library/ports/__init__.py +++ b/library/ports/__init__.py @@ -1,5 +1,5 @@ __author__ = ["Rob Hammond", "Jake Nunemaker"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = ["jake.nunemaker@nrel.gov", "rob.hammond@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov", "rob.hammond@nlr.gov"] __status__ = "Development" diff --git a/library/project/plant/__init__.py b/library/project/plant/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/misc/supply_chain_plots.py b/misc/supply_chain_plots.py index 8c289b62..7a0c0ab3 100644 --- a/misc/supply_chain_plots.py +++ b/misc/supply_chain_plots.py @@ -216,7 +216,7 @@ def area_time_plot(x, y, color, fname=None): y_init = np.sum([v[0] for k, v in y.items()]) for k, v in y.items(): - y1 = [yi + vi for yi, vi in zip(y0, v)] + y1 = [yi + vi for yi, vi in zip(y0, v, strict=False)] ax.fill_between(x, y0 / y_init, y1 / y_init, color=color[k], label=k) ax.plot(x, y1 / y_init, "w") y0 = y1 diff --git a/pyproject.toml b/pyproject.toml index c8fb3108..0fc05777 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,9 +6,9 @@ build-backend = "setuptools.build_meta" name = "orbit-nrel" dynamic = ["version"] authors = [ - {name = "Nick Riccobono", email = "nicholas.riccobono@nrel.gov"}, - {name = "Rob Hammond", email = "rob.hammond@nrel.gov"}, - {name = "Jake Nunemaker", email = "jacob.nunemaker@nrel.gov"}, + {name = "Nick Riccobono", email = "nicholas.riccobono@nlr.gov"}, + {name = "Rob Hammond", email = "rob.hammond@nlr.gov"}, + {name = "Jake Nunemaker", email = "jacob.nunemaker@nlr.gov"}, ] readme = {file = "README.rst", content-type = "text/x-rst"} description = "Offshore Renewables Balance of system and Installation Tool" @@ -52,10 +52,10 @@ classifiers = [ ] [project.urls] -source = "https://github.com/WISDEM/ORBIT" -documentation = "https://wisdem.github.io/ORBIT/" -issues = "https://github.com/WISDEM/ORBIT/issues" -changelog = "https://github.com/WISDEM/ORBIT/blob/main/docs/source/changelog.rst" +source = "https://github.com/NLRWindSystems/ORBIT" +documentation = "https://nlrwindsystems.github.io/ORBIT/" +issues = "https://github.com/NLRWindSystems/ORBIT/issues" +changelog = "https://github.com/NLRWindSystems/ORBIT/blob/main/CHANGELOG.md" [project.optional-dependencies] dev = [ @@ -64,12 +64,21 @@ dev = [ "isort", "pytest>=9", "pytest-cov", - "sphinx", - "sphinx-rtd-theme", "ruff", ] plot = ["matplotlib"] +docs = [ + "jupyter-book>=1,<2", + "myst-nb>=0.16", + "myst-parser>=0.17", + "linkify-it-py>=2", + "sphinx-autodoc-typehints", + "sphinxcontrib-autoyaml", + "sphinxcontrib-bibtex>=2.4", + "sphinxcontrib-spelling>=7", +] + [tool.setuptools] include-package-data = true @@ -124,7 +133,6 @@ target-version = "py310" exclude = [ ".git", "__pycache__", - "docs/source/conf.py", "old", "build", "dist", @@ -152,13 +160,14 @@ select = [ "Q", ] ignore = [ - "E731", - "E402", - "D202", - "D212", "C901", + "D202", "D205", + "D212", + "D301", "D401", + "E731", + "E402", "PD901", "PERF203", ] diff --git a/templates/design_module.py b/templates/design_module.py index c2162113..81043736 100644 --- a/templates/design_module.py +++ b/templates/design_module.py @@ -1,9 +1,9 @@ """Provides information about what class or functionality is provided.""" __author__ = ["Jake Nunemaker"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = ["jake.nunemaker@nrel.gov"] +__email__ = ["jake.nunemaker@nlr.gov"] import math diff --git a/tests/__init__.py b/tests/__init__.py index 534ea979..1333d77f 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,6 +1,6 @@ """Tests suite for ORBIT.""" __author__ = ["Jake Nunemaker", "Rob Hammond"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" diff --git a/tests/core/test_components.py b/tests/core/test_components.py index 9a840a91..70059087 100644 --- a/tests/core/test_components.py +++ b/tests/core/test_components.py @@ -1,6 +1,6 @@ """Tests for Vessel components.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" diff --git a/tests/core/test_environment.py b/tests/core/test_environment.py index fbab5705..75b2767e 100644 --- a/tests/core/test_environment.py +++ b/tests/core/test_environment.py @@ -1,9 +1,9 @@ """Tests for the `Vessel` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import pandas as pd import pytest diff --git a/tests/core/test_library.py b/tests/core/test_library.py index 002adb84..503ccbda 100644 --- a/tests/core/test_library.py +++ b/tests/core/test_library.py @@ -1,9 +1,9 @@ """Test suite for the library module.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" import os from copy import deepcopy diff --git a/tests/core/test_port.py b/tests/core/test_port.py index 915af401..f27ec6b6 100644 --- a/tests/core/test_port.py +++ b/tests/core/test_port.py @@ -1,9 +1,9 @@ """Tests for the `Port` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import pytest diff --git a/tests/core/test_vessel.py b/tests/core/test_vessel.py index 6151b6a3..8ed69e0c 100644 --- a/tests/core/test_vessel.py +++ b/tests/core/test_vessel.py @@ -1,6 +1,6 @@ """Tests for the `Vessel` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" diff --git a/tests/data/__init__.py b/tests/data/__init__.py index 4def0abd..79e4688b 100644 --- a/tests/data/__init__.py +++ b/tests/data/__init__.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from pathlib import Path diff --git a/tests/phases/__init__.py b/tests/phases/__init__.py index 2eedaa09..d7de796f 100644 --- a/tests/phases/__init__.py +++ b/tests/phases/__init__.py @@ -1,6 +1,6 @@ """Tests for the `DesignPhase`, `InstallPhase` and subclasses.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" diff --git a/tests/phases/design/test_array_system_design.py b/tests/phases/design/test_array_system_design.py index ccc18244..336ac7c9 100644 --- a/tests/phases/design/test_array_system_design.py +++ b/tests/phases/design/test_array_system_design.py @@ -1,9 +1,9 @@ """Tests for the `ArraySystemDesign` class.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" from copy import deepcopy diff --git a/tests/phases/design/test_cable.py b/tests/phases/design/test_cable.py index 9aad2830..7f3ab2e1 100644 --- a/tests/phases/design/test_cable.py +++ b/tests/phases/design/test_cable.py @@ -1,9 +1,9 @@ """Provides a testing framework for the `Cable` class.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" import copy diff --git a/tests/phases/design/test_electrical_design.py b/tests/phases/design/test_electrical_design.py index f3e0dc1c..454d13cc 100644 --- a/tests/phases/design/test_electrical_design.py +++ b/tests/phases/design/test_electrical_design.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker, Sophie Bredenkamp" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" import warnings diff --git a/tests/phases/design/test_export_system_design.py b/tests/phases/design/test_export_system_design.py index c7134753..b8ef86b7 100644 --- a/tests/phases/design/test_export_system_design.py +++ b/tests/phases/design/test_export_system_design.py @@ -1,9 +1,9 @@ """Tests for the `ExportSystemDesign` class.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" import warnings from copy import deepcopy diff --git a/tests/phases/design/test_monopile_design.py b/tests/phases/design/test_monopile_design.py index d447a97d..c2d19860 100644 --- a/tests/phases/design/test_monopile_design.py +++ b/tests/phases/design/test_monopile_design.py @@ -1,9 +1,9 @@ """Tests for the `MonopileDesign` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/design/test_mooring_system_design.py b/tests/phases/design/test_mooring_system_design.py index 8281bf98..060af63f 100644 --- a/tests/phases/design/test_mooring_system_design.py +++ b/tests/phases/design/test_mooring_system_design.py @@ -1,9 +1,9 @@ """Tests for the `MooringSystemDesign` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/design/test_oss_design.py b/tests/phases/design/test_oss_design.py index 0596d474..856bcf1a 100644 --- a/tests/phases/design/test_oss_design.py +++ b/tests/phases/design/test_oss_design.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/design/test_scour_protection_design.py b/tests/phases/design/test_scour_protection_design.py index 88753a3c..ec64f1e1 100644 --- a/tests/phases/design/test_scour_protection_design.py +++ b/tests/phases/design/test_scour_protection_design.py @@ -1,9 +1,9 @@ """Tests for the `ScourProtectionDesign` class.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Rob Hammond" -__email__ = "rob.hammond@nrel.gov" +__email__ = "rob.hammond@nlr.gov" import pytest diff --git a/tests/phases/design/test_semisubmersible_design.py b/tests/phases/design/test_semisubmersible_design.py index 21579771..f2aee7cf 100644 --- a/tests/phases/design/test_semisubmersible_design.py +++ b/tests/phases/design/test_semisubmersible_design.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/design/test_spar_design.py b/tests/phases/design/test_spar_design.py index c37b21d7..105036a0 100644 --- a/tests/phases/design/test_spar_design.py +++ b/tests/phases/design/test_spar_design.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/cable_install/test_array_install.py b/tests/phases/install/cable_install/test_array_install.py index 81c6ec3c..a5d666b6 100644 --- a/tests/phases/install/cable_install/test_array_install.py +++ b/tests/phases/install/cable_install/test_array_install.py @@ -1,9 +1,9 @@ """Testing framework for the `ArrayCableInstallation` class.""" __author__ = ["Rob Hammond", "Jake Nunemaker"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/cable_install/test_cable_tasks.py b/tests/phases/install/cable_install/test_cable_tasks.py index c416afd5..cf0a8a29 100644 --- a/tests/phases/install/cable_install/test_cable_tasks.py +++ b/tests/phases/install/cable_install/test_cable_tasks.py @@ -1,9 +1,9 @@ """Testing framework for common cable installation tasks.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" import pytest diff --git a/tests/phases/install/cable_install/test_export_install.py b/tests/phases/install/cable_install/test_export_install.py index 044fe60f..9c2bbd02 100644 --- a/tests/phases/install/cable_install/test_export_install.py +++ b/tests/phases/install/cable_install/test_export_install.py @@ -1,9 +1,9 @@ """Testing framework for the `ExportCableInstallation` class.""" __author__ = ["Rob Hammond", "Jake Nunemaker"] -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" import warnings diff --git a/tests/phases/install/jacket_install/test_jacket_install.py b/tests/phases/install/jacket_install/test_jacket_install.py index f6eb7a52..18804d63 100644 --- a/tests/phases/install/jacket_install/test_jacket_install.py +++ b/tests/phases/install/jacket_install/test_jacket_install.py @@ -1,9 +1,9 @@ """Tests for the `JacketInstallation` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2022, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2022, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/monopile_install/test_monopile_install.py b/tests/phases/install/monopile_install/test_monopile_install.py index 15fa3c42..1f79a947 100644 --- a/tests/phases/install/monopile_install/test_monopile_install.py +++ b/tests/phases/install/monopile_install/test_monopile_install.py @@ -1,9 +1,9 @@ """Tests for the `MonopileInstallation` class without feeder barges.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/monopile_install/test_monopile_tasks.py b/tests/phases/install/monopile_install/test_monopile_tasks.py index c43b8aa0..1b5374e0 100644 --- a/tests/phases/install/monopile_install/test_monopile_tasks.py +++ b/tests/phases/install/monopile_install/test_monopile_tasks.py @@ -1,9 +1,9 @@ """Testing framework for common monopile installation tasks.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" import pytest diff --git a/tests/phases/install/mooring_install/test_mooring_install.py b/tests/phases/install/mooring_install/test_mooring_install.py index aabf1330..651f3525 100644 --- a/tests/phases/install/mooring_install/test_mooring_install.py +++ b/tests/phases/install/mooring_install/test_mooring_install.py @@ -1,9 +1,9 @@ """Testing framework for the `MooringSystemInstallation` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/oss_install/test_oss_install.py b/tests/phases/install/oss_install/test_oss_install.py index bcbec6c9..b8d698f1 100644 --- a/tests/phases/install/oss_install/test_oss_install.py +++ b/tests/phases/install/oss_install/test_oss_install.py @@ -1,9 +1,9 @@ """Tests for the `OffshoreSubstationInstallation` class using feeder barges.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/oss_install/test_oss_tasks.py b/tests/phases/install/oss_install/test_oss_tasks.py index c4152fe3..b28b1fc2 100644 --- a/tests/phases/install/oss_install/test_oss_tasks.py +++ b/tests/phases/install/oss_install/test_oss_tasks.py @@ -1,9 +1,9 @@ """Testing framework for common oss installation tasks.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" import pytest diff --git a/tests/phases/install/quayside_assembly_tow/test_common.py b/tests/phases/install/quayside_assembly_tow/test_common.py index fc1801e0..38a046d6 100644 --- a/tests/phases/install/quayside_assembly_tow/test_common.py +++ b/tests/phases/install/quayside_assembly_tow/test_common.py @@ -1,9 +1,9 @@ """Tests for the common infrastructure for the quayside assembly tow-out.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import pandas as pd diff --git a/tests/phases/install/quayside_assembly_tow/test_gravity_based.py b/tests/phases/install/quayside_assembly_tow/test_gravity_based.py index d2dfce71..2e31444b 100644 --- a/tests/phases/install/quayside_assembly_tow/test_gravity_based.py +++ b/tests/phases/install/quayside_assembly_tow/test_gravity_based.py @@ -1,9 +1,9 @@ """Tests for the `GravityBasedInstallation` class and infrastructure.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/quayside_assembly_tow/test_moored.py b/tests/phases/install/quayside_assembly_tow/test_moored.py index fffb807e..59e67292 100644 --- a/tests/phases/install/quayside_assembly_tow/test_moored.py +++ b/tests/phases/install/quayside_assembly_tow/test_moored.py @@ -1,9 +1,9 @@ """Tests for the `MooredSubInstallation` class and related infrastructure.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/scour_protection_install/test_scour_protection.py b/tests/phases/install/scour_protection_install/test_scour_protection.py index 8434b6ba..f9aca348 100644 --- a/tests/phases/install/scour_protection_install/test_scour_protection.py +++ b/tests/phases/install/scour_protection_install/test_scour_protection.py @@ -1,9 +1,9 @@ """Testing framework for the `ScourProtectionInstallation` class.""" __author__ = "Rob Hammond" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/test_install_phase.py b/tests/phases/install/test_install_phase.py index 0d86e887..39a57bb0 100644 --- a/tests/phases/install/test_install_phase.py +++ b/tests/phases/install/test_install_phase.py @@ -1,9 +1,9 @@ """Tests for the `InstallPhase` class.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import pytest diff --git a/tests/phases/install/turbine_install/test_turbine_install.py b/tests/phases/install/turbine_install/test_turbine_install.py index 876d68cf..0801e44c 100644 --- a/tests/phases/install/turbine_install/test_turbine_install.py +++ b/tests/phases/install/turbine_install/test_turbine_install.py @@ -1,9 +1,9 @@ """Tests for the `MonopileInstallation` class without feeder barges.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2019, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2019, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/phases/install/turbine_install/test_turbine_tasks.py b/tests/phases/install/turbine_install/test_turbine_tasks.py index dcd75318..37f2c390 100644 --- a/tests/phases/install/turbine_install/test_turbine_tasks.py +++ b/tests/phases/install/turbine_install/test_turbine_tasks.py @@ -1,9 +1,9 @@ """Testing framework for common turbine installation tasks.""" __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "Jake.Nunemaker@nrel.gov" +__email__ = "Jake.Nunemaker@nlr.gov" import pytest diff --git a/tests/phases/test_base.py b/tests/phases/test_base.py index 209983ac..3a0780b5 100644 --- a/tests/phases/test_base.py +++ b/tests/phases/test_base.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/test_config_management.py b/tests/test_config_management.py index 474ea81c..ce7383cc 100644 --- a/tests/test_config_management.py +++ b/tests/test_config_management.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from ORBIT import ProjectManager, load_config, save_config diff --git a/tests/test_design_install_phase_interactions.py b/tests/test_design_install_phase_interactions.py index 059202fb..a6ef926c 100644 --- a/tests/test_design_install_phase_interactions.py +++ b/tests/test_design_install_phase_interactions.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/test_parametric.py b/tests/test_parametric.py index b7ff561f..6ba97bcf 100644 --- a/tests/test_parametric.py +++ b/tests/test_parametric.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" from copy import deepcopy diff --git a/tests/test_project_manager.py b/tests/test_project_manager.py index 7fabe212..0a57220c 100644 --- a/tests/test_project_manager.py +++ b/tests/test_project_manager.py @@ -1,7 +1,7 @@ __author__ = "Jake Nunemaker" -__copyright__ = "Copyright 2020, National Renewable Energy Laboratory" +__copyright__ = "Copyright 2026, National Laboratory of the Rockies" __maintainer__ = "Jake Nunemaker" -__email__ = "jake.nunemaker@nrel.gov" +__email__ = "jake.nunemaker@nlr.gov" import datetime as dt