Move storage controllers into storage folder in control_strategies

docs/_toc.yml

@@ -47,11 +47,13 @@ parts:
       - file: technology_models/iron_mine.md
       - file: technology_models/iron_dri.md
       - file: technology_models/steel_eaf.md
-      - file: technology_models/storage_models_index.md
+  - caption: Storage Models
+    chapters:
+      - file: storage/storage_models_index.md
         sections:
-          - file: technology_models/simple_generic_storage.md
-          - file: technology_models/pysam_battery.md
-          - file: technology_models/hydrogen_storage.md
+          - file: storage/simple_generic_storage.md
+          - file: storage/pysam_battery.md
+          - file: storage/hydrogen_storage.md
   - caption: Resource Models
     chapters:
       - file: resource/resource_index

docs/control/control_overview.md

@@ -19,4 +19,4 @@ The second systematic control approach, [pyomo control](#pyomo-control), allows
 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.
 
 Supported controllers:
-- [`HeuristicLoadFollowingController`](#heuristic-load-following-controller)
+- [`HeuristicLoadFollowingStorageController`](#heuristic-load-following-controller)

docs/control/pyomo_controllers.md

@@ -13,15 +13,15 @@ The Pyomo control framework currently supports both a simple heuristic method an
 (heuristic-load-following-controller)=
 ## Heuristic Load Following Controller
 
-The simple heuristic method is specified by setting the storage control to `HeuristicLoadFollowingController`. 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 `PyomoDispatchGenericConverter` for generating technologies, and `PyomoRuleStorageBaseclass` for storage technologies. More complex rule sets may be developed as needed.
+The simple heuristic method is specified by setting the storage control to `HeuristicLoadFollowingStorageController`. 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 `PyomoDispatchGenericConverter` for generating technologies, and `PyomoRuleStorageBaseclass` for storage technologies. More complex rule sets may be developed as needed.
 
-For an example of how to use the heuristic Pyomo control framework with the `HeuristicLoadFollowingController`, see
+For an example of how to use the heuristic Pyomo control framework with the `HeuristicLoadFollowingStorageController`, 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  `OptimizedDispatchController`. Unlike the heuristic method, the optimized dispatch method does not use `dispatch_rule_set` as an input in the `tech_config`. The `OptimizedDispatchController` method maximizes the load met while minimizing the cost of the system (operating cost) over each specified time window.
+The optimized dispatch method is specified by setting the storage control to  `OptimizedDispatchStorageController`. Unlike the heuristic method, the optimized dispatch method does not use `dispatch_rule_set` as an input in the `tech_config`. The `OptimizedDispatchStorageController` method maximizes the load met while minimizing the cost of the system (operating cost) over each specified time window.
 
 The optimized dispatch using Pyomo is implemented differently than the heuristic dispatch in order to be able to properly aggregate the individual Pyomo technology models into a cohesive Pyomo plant model for the optimization solver. Practically, this means that the Pyomo elements of the dispatch (including the individual technology models and the plant model) are not exposed to the main H2I code flow, and do not appear in the N2 diagram. The figure below shows a flow diagram of how the dispatch is implemented. The green blocks below represent what is represented in the N2 diagram of the system. The dispatch routine is currently self-contained within the storage technology of the system, though it includes solving an aggregated plant model in the optimization
 
@@ -40,7 +40,7 @@ We have exposed the optimization cost (weighting) values to the user in this imp
 - The cost values are defined in units of "$/kW".
 ```
 
-For an example of how to use the optimized Pyomo control framework with the `OptimizedDispatchController`, see
+For an example of how to use the optimized Pyomo control framework with the `OptimizedDispatchStorageController`, see
 - `examples/27_pyomo_optimized_dispatch`
 
 

docs/technology_models/storage_models_index.md → docs/storage/storage_models_index.md

@@ -40,11 +40,11 @@ The results that are output per-year of the `plant_life` are:
 - `annual_commodity_produced`: each value is the `total_commodity_produced` scaled to a 1-year (8760 hours) simulation. This value may be negative if the storage charges more than discharges.
 - `capacity_factor`: the storage capacity factor calculated as capacity factors are calculated in converter technologies, a ratio of the sum of *`total_commodity_produced`* to the discharge rate (or `rated_commodity_production`). This value may be negative if the storage charges more than discharges.
     $
-    CF = \frac{\sum_{t=0}^{n_{\text{timesteps}}}(\text{commodity\_out}_t*dt)}{\text{discharge\_rate}*n_{\text{timesteps}}*dt}
+    CF = \frac{\sum_{t=0}^{n_{\text{timesteps}}}(\text{commodity_out}_t*dt)}{\text{\text{discharge}\_\text{rate}*n_{\text{timesteps}}*dt}
     $
 - `standard_capacity_factor`: the storage capacity factor as defined by the [NLR ATB](https://atb.nrel.gov/electricity/2024b/utility-scale_battery_storage). The ratio of the total *commodity discharged* to the discharge rate (or `rated_commodity_production`). This value will always be greater than or equal to zero.
     $
-    CF_{\text{standard}} = \frac{\sum_{t=0}^{n_{\text{timesteps}}}(\text{storage\_commodity\_discharge}_t*dt)}{\text{discharge\_rate}*n_{\text{timesteps}}*dt}
+    CF_{\text{standard}} = \frac{\sum_{t=0}^{n_{\text{timesteps}}}(\text{storage_commodity_discharge}_t*dt)}{\text{discharge_rate}*n_{\text{timesteps}}*dt}
     $
 
 

docs/user_guide/model_overview.md

@@ -284,9 +284,9 @@ Below summarizes the available performance, cost, and financial models for each
 - Storage Controllers:
     - `'SimpleStorageOpenLoopController'`: open-loop control; manages resource flow based on demand and input commodity
     - `'DemandOpenLoopStorageController'`: open-loop control; manages resource flow based on demand and storage constraints
-    - `'HeuristicLoadFollowingController'`: open-loop control that works on a time window basis to set dispatch commands; uses Pyomo
+    - `'HeuristicLoadFollowingStorageController'`: open-loop control that works on a time window basis to set dispatch commands; uses Pyomo
 - Optimized Dispatch:
-    - `'OptimizedDispatchController'`: optimization-based dispatch using Pyomo
+    - `'OptimizedDispatchStorageController'`: optimization-based dispatch using Pyomo
 
 (demand-models)=
 ## Demand Models

examples/01_onshore_steel_mn/tech_config.yaml

@@ -67,7 +67,7 @@ technologies:
     dispatch_rule_set:
       model: PyomoRuleStorageBaseclass
     control_strategy:
-      model: HeuristicLoadFollowingController
+      model: HeuristicLoadFollowingStorageController
     performance_model:
       model: PySAMBatteryPerformanceModel
     cost_model:

examples/02_texas_ammonia/tech_config.yaml

@@ -67,7 +67,7 @@ technologies:
     dispatch_rule_set:
       model: PyomoRuleStorageBaseclass
     control_strategy:
-      model: HeuristicLoadFollowingController
+      model: HeuristicLoadFollowingStorageController
     performance_model:
       model: PySAMBatteryPerformanceModel
     cost_model:

examples/09_co2/direct_ocean_capture/tech_config.yaml

@@ -59,7 +59,7 @@ technologies:
     dispatch_rule_set:
       model: PyomoRuleStorageBaseclass
     control_strategy:
-      model: HeuristicLoadFollowingController
+      model: HeuristicLoadFollowingStorageController
     performance_model:
       model: PySAMBatteryPerformanceModel
     cost_model:

examples/09_co2/ocean_alkalinity_enhancement/tech_config.yaml

@@ -39,7 +39,7 @@ technologies:
     dispatch_rule_set:
       model: PyomoRuleStorageBaseclass
     control_strategy:
-      model: HeuristicLoadFollowingController
+      model: HeuristicLoadFollowingStorageController
     performance_model:
       model: PySAMBatteryPerformanceModel
     cost_model:

examples/12_ammonia_synloop/tech_config.yaml

@@ -67,7 +67,7 @@ technologies:
     dispatch_rule_set:
       model: PyomoRuleStorageBaseclass
     control_strategy:
-      model: HeuristicLoadFollowingController
+      model: HeuristicLoadFollowingStorageController
     performance_model:
       model: PySAMBatteryPerformanceModel
     cost_model:

examples/18_pyomo_heuristic_dispatch/tech_config.yaml

@@ -37,7 +37,7 @@ technologies:
     dispatch_rule_set:
       model: PyomoRuleStorageBaseclass
     control_strategy:
-      model: HeuristicLoadFollowingController
+      model: HeuristicLoadFollowingStorageController
     performance_model:
       model: PySAMBatteryPerformanceModel
     cost_model:

examples/30_pyomo_optimized_dispatch/tech_config.yaml

@@ -36,7 +36,7 @@ technologies:
         commodity_rate_units: kW
   battery:
     control_strategy:
-      model: OptimizedDispatchController
+      model: OptimizedDispatchStorageController
     performance_model:
       model: PySAMBatteryPerformanceModel
     cost_model:

h2integrate/control/control_rules/converters/generic_converter_min_operating_cost.py

@@ -78,7 +78,7 @@ def dispatch_block_rule_function(self, pyomo_model: pyo.ConcreteModel):
 
         This method sets up all model elements (parameters, variables, constraints,
         and ports) associated with a technology block within the dispatch model.
-        It is typically called in the setup_pyomo() method of the PyomoControllerBaseClass.
+        It is typically called in the setup_pyomo() method of the PyomoStorageControllerBaseClass.
 
         Args:
             pyomo_model (pyo.ConcreteModel): The Pyomo model to which the technology

h2integrate/control/control_rules/pyomo_rule_baseclass.py

@@ -56,7 +56,7 @@ def dispatch_block_rule_function(self, pyomo_model: pyo.ConcreteModel, tech_name
 
         This method sets up all model elements (parameters, variables, constraints,
         and ports) associated with a technology block within the dispatch model.
-        It is typically called in the setup_pyomo() method of the PyomoControllerBaseClass.
+        It is typically called in the setup_pyomo() method of the PyomoStorageControllerBaseClass.
 
         Args:
             pyomo_model (pyo.ConcreteModel): The Pyomo model to which the technology

h2integrate/control/control_strategies/pyomo_controller_baseclass.py → h2integrate/control/control_strategies/pyomo_storage_controller_baseclass.py

@@ -14,7 +14,7 @@
 
 
 @define(kw_only=True)
-class PyomoControllerBaseConfig(BaseConfig):
+class PyomoStorageControllerBaseConfig(BaseConfig):
     """
     Configuration data container for Pyomo-based storage / dispatch controllers.
 
@@ -74,7 +74,7 @@ def __attrs_post_init__(self):
             ] * self.n_control_window
 
 
-class PyomoControllerBaseClass(om.ExplicitComponent):
+class PyomoStorageControllerBaseClass(om.ExplicitComponent):
     _time_step_bounds = (
         3600,
         3600,

h2integrate/control/control_strategies/heuristic_pyomo_controller.py → h2integrate/control/control_strategies/storage/heuristic_pyomo_controller.py

@@ -6,9 +6,9 @@
 
 from h2integrate.core.utilities import merge_shared_inputs
 from h2integrate.core.validators import range_val_or_none
-from h2integrate.control.control_strategies.pyomo_controller_baseclass import (
-    PyomoControllerBaseClass,
-    PyomoControllerBaseConfig,
+from h2integrate.control.control_strategies.pyomo_storage_controller_baseclass import (
+    PyomoStorageControllerBaseClass,
+    PyomoStorageControllerBaseConfig,
 )
 
 
@@ -17,8 +17,8 @@
 
 
 @define(kw_only=True)
-class HeuristicLoadFollowingControllerConfig(PyomoControllerBaseConfig):
-    """Configuration class for the HeuristicLoadFollowingController.
+class HeuristicLoadFollowingStorageControllerConfig(PyomoStorageControllerBaseConfig):
+    """Configuration class for the HeuristicLoadFollowingStorageController.
 
     Attributes:
         charge_efficiency (float | None, optional): Efficiency of charging the storage, represented
@@ -61,7 +61,7 @@ def __attrs_post_init__(self):
             self.discharge_efficiency = np.sqrt(self.round_trip_efficiency)
 
 
-class HeuristicLoadFollowingController(PyomoControllerBaseClass):
+class HeuristicLoadFollowingStorageController(PyomoStorageControllerBaseClass):
     """Operates storage based on heuristic rules to meet the demand profile based on
         available commodity from generation profiles and demand profile.
 
@@ -77,7 +77,7 @@ class HeuristicLoadFollowingController(PyomoControllerBaseClass):
 
     def setup(self):
         """Initialize the heuristic load-following controller."""
-        self.config = HeuristicLoadFollowingControllerConfig.from_dict(
+        self.config = HeuristicLoadFollowingStorageControllerConfig.from_dict(
             merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control"),
             additional_cls_name=self.__class__.__name__,
             strict=False,

h2integrate/control/control_strategies/optimized_pyomo_controller.py → h2integrate/control/control_strategies/storage/optimized_pyomo_controller.py

@@ -8,12 +8,12 @@
 from h2integrate.core.utilities import merge_shared_inputs
 from h2integrate.core.validators import range_val
 from h2integrate.control.control_rules.plant_dispatch_model import PyomoDispatchPlantModel
-from h2integrate.control.control_strategies.pyomo_controller_baseclass import (
+from h2integrate.control.control_strategies.controller_opt_problem_state import DispatchProblemState
+from h2integrate.control.control_strategies.pyomo_storage_controller_baseclass import (
     SolverOptions,
-    PyomoControllerBaseClass,
-    PyomoControllerBaseConfig,
+    PyomoStorageControllerBaseClass,
+    PyomoStorageControllerBaseConfig,
 )
-from h2integrate.control.control_strategies.controller_opt_problem_state import DispatchProblemState
 from h2integrate.control.control_rules.storage.pyomo_storage_rule_min_operating_cost import (
     PyomoRuleStorageMinOperatingCosts,
 )
@@ -27,7 +27,7 @@
 
 
 @define
-class OptimizedDispatchControllerConfig(PyomoControllerBaseConfig):
+class OptimizedDispatchStorageControllerConfig(PyomoStorageControllerBaseConfig):
     """
     Configuration data container for Pyomo-based optimal dispatch.
 
@@ -92,7 +92,7 @@ def make_dispatch_inputs(self):
         return dispatch_inputs
 
 
-class OptimizedDispatchController(PyomoControllerBaseClass):
+class OptimizedDispatchStorageController(PyomoStorageControllerBaseClass):
     """Operates storage based on optimization to meet the demand profile based on
         available commodity from generation profiles and demand profile while minimizing costs.
 
@@ -107,7 +107,7 @@ class OptimizedDispatchController(PyomoControllerBaseClass):
 
     def setup(self):
         """Initialize the optimized dispatch controller."""
-        self.config = OptimizedDispatchControllerConfig.from_dict(
+        self.config = OptimizedDispatchStorageControllerConfig.from_dict(
             merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control")
         )
 

h2integrate/control/test/test_heuristic_controllers.py → h2integrate/control/control_strategies/storage/test/test_heuristic_controllers.py

@@ -6,12 +6,12 @@
 from h2integrate.storage.battery.pysam_battery import PySAMBatteryPerformanceModel
 from h2integrate.storage.storage_performance_model import StoragePerformanceModel
 from h2integrate.storage.simple_storage_auto_sizing import StorageAutoSizingModel
-from h2integrate.control.control_strategies.heuristic_pyomo_controller import (
-    HeuristicLoadFollowingController,
-)
 from h2integrate.control.control_rules.storage.pyomo_storage_rule_baseclass import (
     PyomoRuleStorageBaseclass,
 )
+from h2integrate.control.control_strategies.storage.heuristic_pyomo_controller import (
+    HeuristicLoadFollowingStorageController,
+)
 
 
 @fixture
@@ -39,7 +39,7 @@ def tech_config_battery():
         "technologies": {
             "battery": {
                 "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"},
-                "control_strategy": {"model": "HeuristicLoadFollowingController"},
+                "control_strategy": {"model": "HeuristicLoadFollowingStorageController"},
                 "performance_model": {"model": "PySAMBatteryPerformanceModel"},
                 "model_inputs": {
                     "shared_parameters": {
@@ -97,7 +97,7 @@ def tech_config_generic():
         "technologies": {
             "h2_storage": {
                 "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"},
-                "control_strategy": {"model": "HeuristicLoadFollowingController"},
+                "control_strategy": {"model": "HeuristicLoadFollowingStorageController"},
                 "performance_model": {"model": "StoragePerformanceModel"},
                 "model_inputs": {
                     "shared_parameters": {
@@ -134,7 +134,7 @@ def tech_config_autosizing():
         "technologies": {
             "h2_storage": {
                 "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"},
-                "control_strategy": {"model": "HeuristicLoadFollowingController"},
+                "control_strategy": {"model": "HeuristicLoadFollowingStorageController"},
                 "performance_model": {"model": "StorageAutoSizingModel"},
                 "model_inputs": {
                     "shared_parameters": {
@@ -204,7 +204,7 @@ def test_heuristic_load_following_battery_dispatch(
 
     prob.model.add_subsystem(
         "battery_heuristic_load_following_controller",
-        HeuristicLoadFollowingController(
+        HeuristicLoadFollowingStorageController(
             plant_config=plant_config_battery,
             tech_config=tech_config_battery["technologies"]["battery"],
         ),
@@ -563,7 +563,7 @@ def test_heuristic_load_following_battery_dispatch_change_capacities(
 
     prob.model.add_subsystem(
         "battery_heuristic_load_following_controller",
-        HeuristicLoadFollowingController(
+        HeuristicLoadFollowingStorageController(
             plant_config=plant_config_battery,
             tech_config=tech_config_battery["technologies"]["battery"],
         ),
@@ -787,7 +787,7 @@ def test_heuristic_load_following_dispatch_with_generic_storage(
 
     prob.model.add_subsystem(
         "h2_storage_heuristic_load_following_controller",
-        HeuristicLoadFollowingController(
+        HeuristicLoadFollowingStorageController(
             plant_config=plant_config_h2_storage,
             tech_config=tech_config_generic["technologies"]["h2_storage"],
         ),
@@ -935,7 +935,7 @@ def test_heuristic_dispatch_with_autosizing_storage_demand_less_than_avg_in(
 
     prob.model.add_subsystem(
         "h2_storage_controller",
-        HeuristicLoadFollowingController(
+        HeuristicLoadFollowingStorageController(
             plant_config=plant_config_h2_storage,
             tech_config=tech_config_autosizing["technologies"]["h2_storage"],
         ),
@@ -1021,7 +1021,7 @@ def test_heuristic_dispatch_with_autosizing_storage_demand_is_avg_in(
 
     prob.model.add_subsystem(
         "h2_storage_controller",
-        HeuristicLoadFollowingController(
+        HeuristicLoadFollowingStorageController(
             plant_config=plant_config_h2_storage,
             tech_config=tech_config_autosizing["technologies"]["h2_storage"],
         ),