Enable load following optimization dispatch with Pyomo

CHANGELOG.md

@@ -5,6 +5,7 @@
 - Added standlone iron DRI and steel EAF performance and cost models
 - Added capability to have transport models that require user input parameters
 - Add geologic hydrogen surface processing converter
+- Add optimal dispatch of storage for load following
 - Add baseclass for caching functionality
 
 ## 0.5.1 [December 18, 2025]

docs/control/pyomo_controllers.md

@@ -10,7 +10,7 @@ An example of an N2 diagram for a system using the pyomo control framework for h
 
 (heuristic-load-following-controller)=
 ## Heuristic Load Following Controller
-The pyomo control framework currently supports only a simple heuristic method, `heuristic_load_following_controller`, but we plan to extend the framework to be able to run a full dispatch optimization using a pyomo solver. When using the pyomo framework, a `dispatch_rule_set` for each technology connected to the storage technology must also be specified. These will typically be `pyomo_dispatch_generic_converter` for generating technologies, and `pyomo_dispatch_generic_storage` for storage technologies. More complex rule sets may be developed as needed.
+The pyomo control framework currently supports only a simple heuristic method, `heuristic_load_following_controller`, but we plan to extend the framework to be able to run a full dispatch optimization using a pyomo solver. When using the pyomo framework, a `dispatch_rule_set` for each technology connected to the storage technology must also be specified. These will typically be `pyomo_generic_converter` for generating technologies, and `pyomo_generic_storage` for storage technologies. More complex rule sets may be developed as needed.
 
 For an example of how to use the pyomo control framework with the `heuristic_load_following_controller`, see
 - `examples/18_pyomo_heuristic_wind_battery_dispatch`

examples/18_pyomo_heuristic_dispatch/tech_config.yaml

@@ -8,7 +8,7 @@ technologies:
     cost_model:
       model: "atb_wind_cost"
     dispatch_rule_set:
-      model: "pyomo_dispatch_generic_converter"
+      model: "pyomo_generic_converter"
     resource:
       type: "pysam_wind"
       wind_speed: 9.
@@ -39,7 +39,7 @@ technologies:
         commodity_storage_units: "kW"
   battery:
     dispatch_rule_set:
-      model: "pyomo_dispatch_generic_storage"
+      model: "pyomo_generic_storage"
     control_strategy:
       model: "heuristic_load_following_controller"
     performance_model:

examples/18_pyomo_heuristic_dispatch/tech_config_error_for_testing.yaml

@@ -8,7 +8,7 @@ technologies:
     cost_model:
       model: "atb_wind_cost"
     dispatch_rule_set:
-      model: "pyomo_dispatch_generic_converter"
+      model: "pyomo_generic_converter"
     resource:
       type: "pysam_wind"
       wind_speed: 9.
@@ -39,7 +39,7 @@ technologies:
         commodity_storage_units: "kW"
   battery:
     dispatch_rule_set:
-      model: "pyomo_dispatch_generic_storage"
+      model: "pyomo_generic_storage"
     control_strategy:
       model: "heuristic_load_following_controller"
     performance_model:

examples/25_pyomo_optimized_dispatch/driver_config.yaml

@@ -0,0 +1,5 @@
+name: "driver_config"
+description: "This analysis runs a hybrid plant to meet an electrical load."
+
+general:
+  folder_output: outputs

examples/25_pyomo_optimized_dispatch/plant_config.yaml

@@ -0,0 +1,72 @@
+name: "plant_config"
+description: "This plant is located in TX, USA..."
+
+site:
+  latitude: 35.2018863
+  longitude: -101.945027
+
+  resources:
+    wind_resource:
+      resource_model: "wind_toolkit_v2_api"
+      resource_parameters:
+        resource_year: 2012
+
+plant:
+  plant_life: 30
+
+# array of arrays containing left-to-right technology
+# interconnections; can support bidirectional connections
+# with the reverse definition.
+# this will naturally grow as we mature the interconnected tech
+technology_interconnections: [
+  ["wind", "battery", "electricity", "cable"],
+]
+
+# array of arrays containing left-to-right technology, technology doing the dispatching
+# in this case, battery is connected to battery because there are controls rules for
+# the battery and battery is controlling the dispatching
+tech_to_dispatch_connections: [
+  ["wind", "battery"],
+  ["battery", "battery"],
+]
+
+resource_to_tech_connections: [
+  # connect the wind resource to the wind technology
+  ['wind_resource', 'wind', 'wind_resource_data'],
+]
+
+finance_parameters:
+  finance_groups:
+    commodity: "electricity"
+    finance_model: "ProFastComp"
+    model_inputs:
+      params:
+        analysis_start_year: 2032
+        installation_time: 36 # months
+        inflation_rate: 0.0 # 0 for nominal analysis
+        discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind
+        debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind
+        property_tax_and_insurance: 0.03 # p-tax https://www.house.mn.gov/hrd/issinfo/clsrates.aspx # insurance percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf
+        total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8)
+        capital_gains_tax_rate: 0.15 # H2FAST default
+        sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/
+        debt_interest_rate: 0.07 # based on 2024 ATB nominal interest rate for land-based wind
+        debt_type: "Revolving debt" # can be "Revolving debt" or "One time loan". Revolving debt is H2FAST default and leads to much lower LCOH
+        loan_period_if_used: 0 # H2FAST default, not used for revolving debt
+        cash_onhand_months: 1 # H2FAST default
+        admin_expense: 0.00 # percent of sales H2FAST default
+      capital_items:
+        depr_type: "MACRS" # can be "MACRS" or "Straight line" - MACRS may be better and can reduce LCOH by more than $1/kg and is spec'd in the IRS MACRS schedule https://www.irs.gov/publications/p946#en_US_2020_publink1000107507
+        depr_period: 5 # years - for clean energy facilities as specified by the IRS MACRS schedule https://www.irs.gov/publications/p946#en_US_2020_publink1000107507
+  cost_adjustment_parameters:
+    cost_year_adjustment_inflation: 0.025 # used to adjust modeled costs to target_dollar_year
+    target_dollar_year: 2022
+  finance_subgroups:
+      all_electricity:
+        commodity: "electricity"
+        commodity_stream: "wind" # use all electricity generated from wind in finance calc
+        technologies: ["wind", "battery"]
+      dispatched_electricity:
+        commodity: "electricity"
+        commodity_stream: "battery" #use only dispatched electricity from battery in finance calc
+        technologies: ["wind", "battery"]

examples/25_pyomo_optimized_dispatch/pyomo_heuristic_dispatch_error_for_testing.yaml

@@ -0,0 +1,7 @@
+name: "H2Integrate_config"
+
+system_summary: "This hybrid plant contains wind and battery storage technologies. The system is designed to meet a specific electrical load."
+
+driver_config: "driver_config.yaml"
+technology_config: "tech_config_error_for_testing.yaml"
+plant_config: "plant_config.yaml"

examples/25_pyomo_optimized_dispatch/pyomo_optimized_dispatch.yaml

@@ -0,0 +1,7 @@
+name: "H2Integrate_config"
+
+system_summary: "This hybrid plant contains wind and battery storage technologies. The system is designed to dispatch storage optimally meet a specific electrical load."
+
+driver_config: "driver_config.yaml"
+technology_config: "tech_config.yaml"
+plant_config: "plant_config.yaml"

examples/25_pyomo_optimized_dispatch/run_pyomo_optimized_dispatch.py

@@ -0,0 +1,78 @@
+import numpy as np
+from matplotlib import pyplot as plt
+
+from h2integrate.core.h2integrate_model import H2IntegrateModel
+
+
+# Create an H2Integrate model
+model = H2IntegrateModel("pyomo_optimized_dispatch.yaml")
+
+demand_profile = np.ones(8760) * 100.0
+
+
+# TODO: Update with demand module once it is developed
+model.setup()
+model.prob.set_val("battery.electricity_demand", demand_profile, units="MW")
+
+# Run the model
+model.run()
+
+# Plot the results
+fig, ax = plt.subplots(2, 1, sharex=True)
+
+start_hour = 0
+end_hour = 200
+
+ax[0].plot(
+    range(start_hour, end_hour),
+    model.prob.get_val("battery.SOC", units="percent")[start_hour:end_hour],
+    label="SOC",
+)
+ax[0].set_ylabel("SOC (%)")
+ax[0].set_ylim([0, 110])
+ax[0].axhline(y=90.0, linestyle=":", color="k", alpha=0.5, label="Max Charge")
+ax[0].legend()
+
+ax[1].plot(
+    range(start_hour, end_hour),
+    model.prob.get_val("battery.electricity_in", units="MW")[start_hour:end_hour],
+    linestyle="-",
+    label="Electricity In (MW)",
+)
+ax[1].plot(
+    range(start_hour, end_hour),
+    model.prob.get_val("battery.unused_electricity_out", units="MW")[start_hour:end_hour],
+    linestyle=":",
+    label="Unused Electricity (MW)",
+)
+ax[1].plot(
+    range(start_hour, end_hour),
+    model.prob.get_val("battery.unmet_electricity_demand_out", units="MW")[start_hour:end_hour],
+    linestyle=":",
+    label="Unmet Electrical Demand (MW)",
+)
+ax[1].plot(
+    range(start_hour, end_hour),
+    model.prob.get_val("battery.electricity_out", units="MW")[start_hour:end_hour],
+    linestyle="-",
+    label="Electricity Out (MW)",
+)
+ax[1].plot(
+    range(start_hour, end_hour),
+    model.prob.get_val("battery.battery_electricity_discharge", units="MW")[start_hour:end_hour],
+    linestyle="-.",
+    label="Battery Electricity Out (MW)",
+)
+ax[1].plot(
+    range(start_hour, end_hour),
+    demand_profile[start_hour:end_hour],
+    linestyle="--",
+    label="Eletrical Demand (MW)",
+)
+ax[1].set_ylim([-7e2, 7e2])
+ax[1].set_ylabel("Electricity Hourly (MW)")
+ax[1].set_xlabel("Timestep (hr)")
+
+plt.legend(ncol=2, frameon=False)
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300)

examples/25_pyomo_optimized_dispatch/tech_config.yaml

@@ -0,0 +1,80 @@
+name: "technology_config"
+description: "This hybrid plant produces electricity from wind and battery storage."
+
+
+technologies:
+  wind:
+    performance_model:
+      model: "pysam_wind_plant_performance"
+    cost_model:
+      model: "atb_wind_cost"
+    dispatch_rule_set:
+      model: "pyomo_generic_converter"
+    resource:
+      type: "pysam_wind"
+      wind_speed: 9.
+    model_inputs:
+      performance_parameters:
+        num_turbines: 100
+        turbine_rating_kw: 8300
+        rotor_diameter: 196.
+        hub_height: 130.
+        create_model_from: "default"
+        config_name: "WindPowerSingleOwner"
+        pysam_options: !include pysam_options_8.3MW.yaml
+        run_recalculate_power_curve: False
+        layout:
+          layout_mode: "basicgrid"
+          layout_options:
+            row_D_spacing: 10.0
+            turbine_D_spacing: 10.0
+            rotation_angle_deg: 0.0
+            row_phase_offset: 0.0
+            layout_shape: "square"
+      cost_parameters:
+        capex_per_kW: 1500.0
+        opex_per_kW_per_year: 45
+        cost_year: 2019
+      dispatch_rule_parameters:
+        commodity_name: "electricity"
+        commodity_storage_units: "kW"
+  battery:
+    dispatch_rule_set:
+      model: "pyomo_generic_storage"
+    control_strategy:
+      model: "optimized_dispatch_controller"
+    performance_model:
+      model: "pysam_battery"
+    cost_model:
+      model: "atb_battery_cost"
+    model_inputs:
+      shared_parameters:
+        commodity_name: "electricity"
+        max_charge_rate: 100000
+        max_capacity: 500000
+        n_control_window: 24
+        n_horizon_window: 48
+        init_charge_percent: 0.5
+        max_charge_percent: 0.9
+        min_charge_percent: 0.1
+        system_commodity_interface_limit: 1e12
+        time_weighting_factor: 0.995
+        charge_efficiency: 0.938
+        discharge_efficiency: 0.938
+        commodity_storage_units: "kW"
+        cost_per_charge: 0.05
+        cost_per_discharge: 0.1
+        commodity_met_value: 0.5
+        cost_per_production: 0.01
+      performance_parameters:
+        system_model_source: "pysam"
+        chemistry: "LFPGraphite"
+      cost_parameters:
+        cost_year: 2022
+        commodity_units: "kW"
+        energy_capex: 310 # $/kWh from 2024 ATB year 2025
+        power_capex: 311 # $/kW from 2024 ATB year 2025
+        opex_fraction: 0.25 # 0.25% of capex per year from 2024 ATB
+      control_parameters:
+        # commodity_storage_units: "kW"
+        tech_name: "battery"

examples/25_pyomo_optimized_dispatch/tech_config_error_for_testing.yaml

@@ -0,0 +1,73 @@
+name: "technology_config"
+description: "This hybrid plant produces hydrogen"
+
+technologies:
+  wind:
+    performance_model:
+      model: "pysam_wind_plant_performance"
+    cost_model:
+      model: "atb_wind_cost"
+    dispatch_rule_set:
+      model: "pyomo_generic_converter"
+    resource:
+      type: "pysam_wind"
+      wind_speed: 9.
+    model_inputs:
+      performance_parameters:
+        num_turbines: 100
+        turbine_rating_kw: 8300
+        rotor_diameter: 196.
+        hub_height: 130.
+        create_model_from: "default"
+        config_name: "WindPowerSingleOwner"
+        pysam_options: !include pysam_options_8300MW.yaml
+        run_recalculate_power_curve: False
+        layout:
+          layout_mode: "basicgrid"
+          layout_options:
+            row_D_spacing: 10.0
+            turbine_D_spacing: 10.0
+            rotation_angle_deg: 0.0
+            row_phase_offset: 0.0
+            layout_shape: "square"
+      cost_parameters:
+        capex_per_kW: 1500.0
+        opex_per_kW_per_year: 45
+        cost_year: 2019
+      dispatch_rule_parameters:
+        commodity_name: "electricity"
+        commodity_storage_units: "kW"
+  battery:
+    dispatch_rule_set:
+      model: "pyomo_generic_storage"
+    control_strategy:
+      model: "heuristic_load_following_controller"
+    performance_model:
+      model: "pysam_battery"
+    cost_model:
+      model: "atb_battery_cost"
+    model_inputs:
+      shared_parameters:
+        max_charge_rate: 100000
+        max_capacity: 500000
+        n_control_window: 24
+        n_horizon_window: 48
+        init_charge_percent: 0.5
+        max_charge_percent: 0.9
+        min_charge_percent: 0.1
+        system_commodity_interface_limit: 1e12
+      performance_parameters:
+        system_model_source: "pysam"
+        chemistry: "LFPGraphite"
+      cost_parameters:
+        cost_year: 2022
+        energy_capex: 310 # $/kWh from 2024 ATB year 2025
+        power_capex: 311 # $/kW from 2024 ATB year 2025
+        opex_fraction: 0.25 # 0.25% of capex per year from 2024 ATB
+      control_parameters:
+        commodity_name: "electricity"
+        commodity_storage_units: "kW"
+        tech_name: "wrong_tech_name"
+      dispatch_rule_parameters:
+        commodity_name: "electricity"
+        commodity_storage_units: "kW"