NatLabRockies · genevievestarke · Oct 2, 2025 · Oct 9, 2025 · Nov 17, 2025 · Nov 18, 2025
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -7,6 +7,7 @@
 - Added standalone 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
 - Added postprocessing function to save timeseries
 - Minor reorg for profast tools

diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md
@@ -2,15 +2,24 @@
 # Pyomo control framework
 [Pyomo](https://www.pyomo.org/about) is an open-source optimization software package. It is used in H2Integrate to facilitate modeling and solving control problems, specifically to determine optimal dispatch strategies for dispatchable technologies.
 
-Pyomo control, allows for the possibility of feedback control at specified intervals, but can also be used for open-loop control if desired. In the pyomo control framework in H2Integrate, each technology can have control rules associated with them that are in turn passed to the pyomo control component, which is owned by the storage technology. The pyomo control component combines the technology rules into a single pyomo model, which is then passed to the storage technology performance model inside a callable dispatch function. The dispatch function also accepts a simulation method from the performance model and iterates between the pyomo model for dispatch commands and the performance simulation function to simulated performance with the specified commands. The dispatch function runs in specified time windows for dispatch and performance until the whole simulation time has been run.
+Pyomo control allows for the possibility of feedback control at specified intervals, but can also be used for open-loop control if desired. In the pyomo control framework in H2Integrate, each technology can have control rules associated with them that are in turn passed to the pyomo control component, which is owned by the storage technology. The pyomo control component combines the technology rules into a single pyomo model, which is then passed to the storage technology performance model inside a callable dispatch function. The dispatch function also accepts a simulation method from the performance model and iterates between the pyomo model for dispatch commands and the performance simulation function to simulate performance with the specified commands. The dispatch function runs in specified time windows for dispatch and performance until the whole simulation time has been run.
 
 An example of an N2 diagram for a system using the pyomo control framework for hydrogen storage and dispatch is shown below ([click here for an interactive version](./figures/pyomo-n2.html)). Note the control rules being passed to the dispatch component and the dispatch function, containing the full pyomo model, being passed to the performance model for the battery/storage technology. Another important thing to recognize, in contrast to the open-loop control framework, is that the storage technology outputs (commodity out, SOC, unused commodity, etc) are passed out of the performance model when using the Pyomo control framework rather than from the control component.
 
 ![](./figures/pyomo-n2.png)
 
+The pyomo control framework currently supports both a simple heuristic method and an optimized dispatch method for load following control.
+
 (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 simple heuristic method is specified by setting the storage control to  `heuristic_load_following_controller`. 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.
 
-For an example of how to use the pyomo control framework with the `heuristic_load_following_controller`, see
+For an example of how to use the heuristic pyomo control framework with the `heuristic_load_following_controller`, see
 - `examples/18_pyomo_heuristic_wind_battery_dispatch`
+
+(optimized-load-following-controller)=
+## Optimized Load Following Controller
+The optimized dispatch method is specified by setting the storage control to  `optimized_dispatch_controller`. The same `dispatch_rule_set` for each technology connected to the storage technology is followed as in the heuristic case. This method maximizes the load met while minimizing the cost of the system (operating cost) over each specified time window.
+
+For an example of how to use the optimized pyomo control framework with the `optimized_dispatch_controller`, see
+- `examples/27_pyomo_optimized_dispatch`
diff --git a/examples/01_onshore_steel_mn/tech_config.yaml b/examples/01_onshore_steel_mn/tech_config.yaml
@@ -82,6 +82,7 @@ technologies:
     model_inputs:
       shared_parameters:
         commodity_name: "electricity"
+        commodity_storage_units: "kW"
         max_charge_rate: 375740.4 #kW
         max_capacity: 375745.2 #kWh
         n_control_window: 24
@@ -100,11 +101,7 @@ technologies:
         power_capex: 311 # $/kW from 2024 ATB year 2025
         opex_fraction: 0.024999840573439444
       control_parameters:
-        commodity_storage_units: "kW"
         tech_name: "battery"
-      dispatch_rule_parameters:
-        commodity_name: "electricity"
-        commodity_storage_units: "kW"
 
   electrolyzer:
     performance_model:

diff --git a/examples/02_texas_ammonia/tech_config.yaml b/examples/02_texas_ammonia/tech_config.yaml
@@ -80,6 +80,7 @@ technologies:
     model_inputs:
       shared_parameters:
         commodity_name: "electricity"
+        commodity_storage_units: "kW"
         max_charge_rate: 96.0 #kW
         max_capacity: 96.0 #kWh
         n_control_window: 24
@@ -98,11 +99,8 @@ technologies:
         power_capex: 311 # $/kW from 2024 ATB year 2025
         opex_fraction: 0.025
       control_parameters:
-        commodity_storage_units: "kW"
         tech_name: "battery"
-      dispatch_rule_parameters:
-        commodity_name: "electricity"
-        commodity_storage_units: "kW"
+
   electrolyzer:
     performance_model:
       model: "eco_pem_electrolyzer_performance"

diff --git a/examples/09_co2/direct_ocean_capture/tech_config.yaml b/examples/09_co2/direct_ocean_capture/tech_config.yaml
@@ -73,6 +73,7 @@ technologies:
     model_inputs:
       shared_parameters:
         commodity_name: "electricity"
+        commodity_storage_units: "kW"
         max_charge_rate: 50000 #kW
         max_capacity: 200000 #kWh
         n_control_window: 24
@@ -91,11 +92,8 @@ technologies:
         power_capex: 317 # $/kW from 2024 ATB year 2025
         opex_fraction: 0.02536510376633359
       control_parameters:
-        commodity_storage_units: "kW"
         tech_name: "battery"
-      dispatch_rule_parameters:
-        commodity_name: "electricity"
-        commodity_storage_units: "kW"
+
   doc:
     performance_model:
       model: "direct_ocean_capture_performance"

diff --git a/examples/09_co2/ocean_alkalinity_enhancement/tech_config.yaml b/examples/09_co2/ocean_alkalinity_enhancement/tech_config.yaml
@@ -50,6 +50,7 @@ technologies:
     model_inputs:
       shared_parameters:
         commodity_name: "electricity"
+        commodity_storage_units: "kW"
         max_charge_rate: 50000 #kW
         max_capacity: 200000 #kWh
         n_control_window: 24
@@ -68,11 +69,8 @@ technologies:
         power_capex: 317 # $/kW from 2024 ATB year 2025
         opex_fraction: 0.02536510376633359
       control_parameters:
-        commodity_storage_units: "kW"
         tech_name: "battery"
-      dispatch_rule_parameters:
-        commodity_name: "electricity"
-        commodity_storage_units: "kW"
+
   oae:
     performance_model:
       model: "ocean_alkalinity_enhancement_performance"

diff --git a/examples/12_ammonia_synloop/tech_config.yaml b/examples/12_ammonia_synloop/tech_config.yaml
@@ -80,6 +80,7 @@ technologies:
     model_inputs:
       shared_parameters:
         commodity_name: "electricity"
+        commodity_storage_units: "kW"
         max_charge_rate: 96.0 #kW
         max_capacity: 96.0 #kWh
         n_control_window: 24
@@ -98,11 +99,8 @@ technologies:
         power_capex: 311 # $/kW from 2024 ATB year 2025
         opex_fraction: 0.025
       control_parameters:
-        commodity_storage_units: "kW"
         tech_name: "battery"
-      dispatch_rule_parameters:
-        commodity_name: "electricity"
-        commodity_storage_units: "kW"
+
   electrolyzer:
     performance_model:
       model: "eco_pem_electrolyzer_performance"

diff --git a/examples/18_pyomo_heuristic_dispatch/run_pyomo_heuristic_dispatch.py b/examples/18_pyomo_heuristic_dispatch/run_pyomo_heuristic_dispatch.py
@@ -67,7 +67,7 @@
     range(start_hour, end_hour),
     demand_profile[start_hour:end_hour],
     linestyle="--",
-    label="Eletrical Demand (MW)",
+    label="Electrical Demand (MW)",
 )
 ax[1].set_ylim([-7e2, 7e2])
 ax[1].set_ylabel("Electricity Hourly (MW)")

diff --git a/examples/18_pyomo_heuristic_dispatch/tech_config.yaml b/examples/18_pyomo_heuristic_dispatch/tech_config.yaml
@@ -46,6 +46,7 @@ technologies:
     model_inputs:
       shared_parameters:
         commodity_name: "electricity"
+        commodity_storage_units: "kW"
         max_charge_rate: 100000
         max_capacity: 500000
         n_control_window: 24
@@ -64,8 +65,4 @@ technologies:
         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"
-      dispatch_rule_parameters:
-        commodity_name: "electricity"
-        commodity_storage_units: "kW"
diff --git a/examples/18_pyomo_heuristic_dispatch/tech_config_error_for_testing.yaml b/examples/18_pyomo_heuristic_dispatch/tech_config_error_for_testing.yaml
@@ -48,6 +48,8 @@ technologies:
       model: "atb_battery_cost"
     model_inputs:
       shared_parameters:
+        commodity_name: "electricity"
+        commodity_storage_units: "kW"
         max_charge_rate: 100000
         max_capacity: 500000
         n_control_window: 24
@@ -65,9 +67,4 @@ technologies:
         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"
diff --git a/examples/27_pyomo_optimized_dispatch/driver_config.yaml b/examples/27_pyomo_optimized_dispatch/driver_config.yaml
@@ -0,0 +1,5 @@
+name: "driver_config"
+description: "This analysis runs a hybrid plant to dispatch storage optimally to meet an electrical load."
+
+general:
+  folder_output: outputs
diff --git a/examples/27_pyomo_optimized_dispatch/plant_config.yaml b/examples/27_pyomo_optimized_dispatch/plant_config.yaml
@@ -0,0 +1,73 @@
+name: "plant_config"
+description: "This plant is located in TX, USA..."
+
+sites:
+  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
+  ['site.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"]
diff --git a/examples/27_pyomo_optimized_dispatch/pyomo_optimized_dispatch.yaml b/examples/27_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"