Converter PLM dispatch

.pre-commit-config.yaml

@@ -27,7 +27,7 @@ repos:
       - id: yamlfix
         # Exclude YAML files that are used as inputs for testing or examples, or ones for desired schedule in HOPP as they
         # expect misformatted YAML files.
-        exclude: ^(h2integrate/core/test/inputs/.*|examples/11_hybrid_energy_plant/tech_inputs/desired_schedules/.*|examples/33_peak_load_management/demand_profiles/.*|examples/34_plm_optimized_dispatch/demand_profiles/.*)$
+        exclude: ^(h2integrate/core/test/inputs/.*|examples/11_hybrid_energy_plant/tech_inputs/desired_schedules/.*|library/demand_profiles/.*|examples/34_plm_optimized_dispatch/demand_profiles/.*)$
   - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
     rev: v0.8.1

examples/33_peak_load_management_heuristics/plm_converter/driver_config.yaml

@@ -0,0 +1,5 @@
+name: driver_config
+description: Driver configuration for peak load management converter heuristic control
+general:
+  folder_output: outputs
+  create_om_reports: false

examples/33_peak_load_management_heuristics/plm_converter/plant_config.yaml

@@ -0,0 +1,13 @@
+name: plant_config
+description: Plant configuration for peak load management converter heuristic control
+plant:
+  plant_life: 30
+  simulation:
+    n_timesteps: 8760
+    dt: 3600
+    timezone: -6
+    start_time: 2025/07/01 00:00:00
+technology_interconnections:
+  - [hydrogen_feedstock, fuel_cell, hydrogen, pipe]
+  - [fuel_cell, electrical_load_demand, [electricity_out, electricity_in]]
+  - [electrical_load_demand, grid_buy, [unmet_electricity_demand_out, electricity_set_point]]

examples/33_peak_load_management_heuristics/plm_converter/run_peak_load_management.py

@@ -0,0 +1,101 @@
+"""
+Example 33: Peak load management converter heuristic dispatch
+
+This example demonstrates peak load management dispatch open loop control of a converter
+with two demand profiles of interest
+
+In this example, the fuel-cell converter command is determined by a simple open-loop
+threshold heuristic that uses two profiles:
+
+1. demand_profile (local set-point)
+2. demand_profile_upstream (supervisory or upstream signal)
+
+At each time step, dispatch is only considered when either profile exceeds its configured
+cutoff. For electricity-mode upstream control, the commanded dispatch is based on the
+larger exceedance of the two profiles and is then limited by both the instantaneous local
+demand and converter capacity. This creates a peak-shaving command profile for the
+converter without storage state-of-charge dynamics or optimization.
+
+The output figure compares the original local demand, upstream demand, cutoff thresholds,
+converter output, resulting unmet demand, and grid purchases.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from h2integrate import H2IntegrateModel
+from h2integrate.core.utilities import build_time_series_from_plant_config
+
+
+# Create, setup, and run the H2Integrate model
+model = H2IntegrateModel("33_plm_converter_heuristic.yaml")
+
+model.setup()
+# om.n2(model.prob)
+model.run()
+model.post_process()
+
+demand_profile = model.prob.get_val("fuel_cell.electricity_command_value", units="kW")
+# plot the results for the first week
+demand_profile_upstream = np.array(
+    model.technology_config["technologies"]["fuel_cell"]["model_inputs"]["control_parameters"][
+        "demand_profile_upstream"
+    ]
+)
+demand_profile_peak_cutoff = model.technology_config["technologies"]["fuel_cell"]["model_inputs"][
+    "control_parameters"
+]["demand_profile_peak_cutoff"]
+demand_profile_upstream_peak_cutoff = model.technology_config["technologies"]["fuel_cell"][
+    "model_inputs"
+]["control_parameters"]["demand_profile_upstream_peak_cutoff"]
+grid_output = model.prob.get_val("grid_buy.electricity_out", units="MW")
+
+time_series = build_time_series_from_plant_config(model.plant_config)
+
+n_plot = 24 * 7
+time_plot = time_series[:n_plot]
+
+fig, ax = plt.subplots(3, 1, sharex=True, figsize=(10, 5))
+ax[0].plot(time_plot, demand_profile_upstream[:n_plot] * 1e-3, label="Upstream demand")
+ax[0].plot(time_plot, demand_profile[:n_plot] * 1e-3, label="Original demand")
+ax[0].axhline(
+    demand_profile_peak_cutoff * 1e-3, label="Demand peak cutoff", color="k", linestyle="--"
+)
+ax[0].axhline(
+    demand_profile_upstream_peak_cutoff * 1e-3,
+    label="Upstream demand peak cutoff",
+    color="k",
+    linestyle=":",
+)
+ax[0].set(ylabel="Power (MW)", ylim=[-2, 2])
+ax[0].legend(frameon=True, ncol=3)
+
+ax[1].plot(time_plot, demand_profile[:n_plot] * 1e-3, label="Original demand (MW)")
+ax[1].plot(
+    time_plot,
+    model.prob.get_val("fuel_cell.electricity_out", units="MW")[:n_plot],
+    label="fuel_cell dispatch",
+)
+ax[1].set(ylabel="Power (MW)", ylim=[-2, 2])
+ax[1].legend(frameon=False, ncol=2)
+
+ax[2].plot(time_plot, demand_profile[:n_plot] * 1e-3, label="Original demand (MW)")
+ax[2].plot(
+    time_plot,
+    model.prob.get_val("electrical_load_demand.unmet_electricity_demand_out", units="MW")[:n_plot],
+    label="New demand profile",
+)
+ax[2].plot(time_plot, grid_output[:n_plot], label="Grid purchase (MW)", linestyle=":")
+
+ax[2].set(ylabel="Power (MW)", ylim=[-2, 2])
+ax[2].legend(frameon=False, ncol=3)
+ax[2].tick_params(axis="x", labelrotation=90)
+
+for axis in ax:
+    axis.minorticks_on()
+    axis.grid(True, which="major", alpha=0.45, linewidth=0.8)
+    axis.grid(True, which="minor", alpha=0.2, linewidth=0.5)
+
+plt.tight_layout()
+plt.savefig("example_peak_load_dispatch.png", transparent=False)

examples/33_peak_load_management_heuristics/plm_converter/tech_config.yaml

@@ -0,0 +1,66 @@
+name: technology_config
+description: This plant dispatches a fuel cell to reduce peak demand
+technologies:
+  hydrogen_feedstock:
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: hydrogen
+        commodity_rate_units: kg/h
+      performance_parameters:
+        rated_capacity: 50.0  # kg of H2/hour
+      cost_parameters:
+        cost_year: 2022
+        price: 5.0  # USD/kg H2
+        annual_cost: 0.
+        start_up_cost: 0.0
+  fuel_cell:
+    performance_model:
+      model: LinearH2FuelCellPerformanceModel
+    cost_model:
+      model: H2FuelCellCostModel
+    control_strategy:
+      model: PeakLoadManagementHeuristicOpenLoopConverterController
+    model_inputs:
+      shared_parameters:
+        system_capacity_kw: 1000.0
+      performance_parameters:
+        fuel_cell_efficiency_hhv: 0.50
+        uptime_hours_until_eol: 80000
+      cost_parameters:
+        capex_per_kw: 200.0
+        fixed_opex_per_kw_per_year: 10.0
+        variable_opex_per_kwh: 0.0
+        cost_year: 2018
+      control_parameters:
+        commodity: electricity
+        commodity_rate_units: kW
+        demand_profile: !include demand_profiles/demand_profile.yaml
+        demand_profile_upstream: !include demand_profiles/demand_profile_upstream.yaml
+        demand_profile_peak_cutoff: 400.0
+        demand_profile_upstream_peak_cutoff: 1000.0
+  electrical_load_demand:
+    performance_model:
+      model: GenericDemandComponent
+    model_inputs:
+      performance_parameters:
+        commodity: electricity
+        commodity_rate_units: kW
+        demand_profile: !include demand_profiles/demand_profile.yaml
+  grid_buy:
+    performance_model:
+      model: GridPerformanceModel
+    cost_model:
+      model: GridCostModel
+    model_inputs:
+      shared_parameters:
+        interconnection_size: 100000
+      cost_parameters:
+        cost_year: 2024
+        fixed_interconnection_cost: 0.0
+        interconnection_capex_per_kw: 0.0
+        interconnection_opex_per_kw: 0.0
+        electricity_buy_price: 0.09

examples/33_peak_load_management_heuristics/plm_storage/33_plm_storage_heuristic.yaml

@@ -0,0 +1,5 @@
+name: H2Integrate_config
+system_summary: Peak load management dispatch
+driver_config: driver_config.yaml
+technology_config: tech_config.yaml
+plant_config: plant_config.yaml

examples/33_peak_load_management_heuristics/plm_storage/driver_config.yaml

@@ -0,0 +1,5 @@
+name: driver_config
+description: Driver configuration for peak load management storage heuristic control
+general:
+  folder_output: outputs
+  create_om_reports: false

examples/33_peak_load_management/plant_config.yaml → examples/33_peak_load_management_heuristics/plm_storage/plant_config.yaml

@@ -1,5 +1,5 @@
 name: plant_config
-description: Demonstrates multivariable streams with a gas combiner
+description: Plant configuration for peak load management storage heuristic control
 plant:
   plant_life: 30
   simulation:

examples/33_peak_load_management/run_peak_load_management.py → examples/33_peak_load_management_heuristics/plm_storage/run_peak_load_management.py

@@ -1,5 +1,5 @@
 """
-Example 33: Peak load management dispatch
+Example 33: Peak load management storage heuristic dispatch
 
 This example demonstrates:
 1. Peak load management dispatch open loop control with two demand profiles of interest
@@ -32,7 +32,7 @@
 
 
 # Create, setup, and run the H2Integrate model
-model = H2IntegrateModel("33_peak_load_management.yaml")
+model = H2IntegrateModel("33_plm_storage_heuristic.yaml")
 
 model.setup()
 model.run()

examples/33_peak_load_management/tech_config.yaml → examples/33_peak_load_management_heuristics/plm_storage/tech_config.yaml

@@ -1,5 +1,5 @@
 name: technology_config
-description: This plant charges a battery from the grid to reduce peak demand
+description: This plant dispatches a battery to reduce peak demand
 technologies:
   battery:
     performance_model:

h2integrate/control/control_strategies/converters/__init__.py

@@ -0,0 +1,3 @@
+from h2integrate.control.control_strategies.converters.plm_openloop_converter_controller import (
+    PeakLoadManagementHeuristicOpenLoopConverterController,
+)