diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e8d0ef24f..35a0a2e49 100644
--- a/.pre-commit-config.yaml
+++ b/.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/.*)$
+        exclude: ^(h2integrate/core/test/inputs/.*|examples/11_hybrid_energy_plant/tech_inputs/desired_schedules/.*|examples/33_peak_load_management/demand_profiles/.*)$
   - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
     rev: v0.8.1
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 54d62246c..527e18e2f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,7 @@
 - Bugfix for round-trip efficiency handling when calling `check_inputs` around `StoragePerformanceModel` [PR 684](https://github.com/NatLabRockies/H2Integrate/pull/684)
 - Bugfix. Include nuclear in electricity producing tech list and improve error message for zero-length electricity producing techs in model when electricity is specified as the commodity. [PR 685](https://github.com/NatLabRockies/H2Integrate/pull/685)
 - Added electricity and water consumption profiles as outputs to the `ECOElectrolyzerPerformanceModel` [PR 690](https://github.com/NatLabRockies/H2Integrate/pull/690)
+- Add `PeakLoadManagementHeuristicOpenLoopStorageController` as a storage control strategy. [PR 641](https://github.com/NatLabRockies/H2Integrate/pull/641)
 
 ## 0.8 [April 15, 2026]
 - Updated README and docs intro page with expanded H2I description, reorganized sections, and streamlined installation instructions [PR 677](https://github.com/NatLabRockies/H2Integrate/pull/677)
diff --git a/docs/control/control_overview.md b/docs/control/control_overview.md
index 141606339..1b18fd85b 100644
--- a/docs/control/control_overview.md
+++ b/docs/control/control_overview.md
@@ -9,8 +9,7 @@ The first approach, [open-loop control](#open-loop-control), assumes no feedback
 Supported controllers:
 - [`SimpleStorageOpenLoopController`](#pass-through-controller)
 - [`DemandOpenLoopStorageController`](#demand-open-loop-storage-controller)
-
-
+- [`PeakLoadManagementHeuristicOpenLoopStorageController`](#peak-load-management-open-loop-storage-controller)
 
 (pyomo-control-framework)=
 ## Pyomo control framework
@@ -20,3 +19,4 @@ In the pyomo control framework in H2Integrate, each technology can have control
 
 Supported controllers:
 - [`HeuristicLoadFollowingStorageController`](#heuristic-load-following-controller)
+- [`OptimizedDispatchController`](#optimized-load-following-controller)
diff --git a/docs/control/figures/example_peak_load_dispatch.png b/docs/control/figures/example_peak_load_dispatch.png
new file mode 100644
index 000000000..5acc39034
Binary files /dev/null and b/docs/control/figures/example_peak_load_dispatch.png differ
diff --git a/docs/control/open-loop_controllers.md b/docs/control/open-loop_controllers.md
index dfb7f241c..5ee3b3cdc 100644
--- a/docs/control/open-loop_controllers.md
+++ b/docs/control/open-loop_controllers.md
@@ -2,9 +2,10 @@
 # Open-Loop Controllers
 
 ## Open-Loop Storage Controllers
-The open-loop storage controllers can be attached as the control strategy in the `tech_config` for various storage converters (e.g., battery or hydrogen storage). There are two controller types for storage:
-1. Pass-through Controller - passes the commodity flow to the output without any modification
-2. Demand Open-Loop Storage Controller - uses simple logic to attempt to meet demand using the storage technology.
+The open-loop storage controllers can be attached as the control strategy in the `tech_config` for various storage components (e.g., battery or hydrogen storage). There are three controller types for storage:
+1. [Simple Open-Loop Storage Controller](#pass-through-controller) — passes the commodity flow to the output with only minimal or no modifications.
+2. [Demand Open-Loop Storage Controller](#demand-open-loop-storage-controller) — uses simple logic to attempt to meet demand using the storage technology.
+3. [Peak Load Management Open-Loop Storage Controller](#peak-load-management-open-loop-storage-controller) — computes a peak-shaving dispatch schedule to reduce demand peaks, supporting one or two demand profiles with configurable event limits and time windows.
 
 (pass-through-controller)=
 ### Simple Open-Loop Storage Controller
@@ -26,3 +27,33 @@ An example of an N2 diagram for a system using the open-loop control framework f
 For examples of how to use the `DemandOpenLoopStorageController` open-loop control framework, see the following:
 - `examples/14_wind_hydrogen_dispatch/`
 - `examples/19_simple_dispatch/`
+
+(peak-load-management-open-loop-storage-controller)=
+### Peak Load Management Open-Loop Storage Controller
+The `PeakLoadManagementHeuristicOpenLoopStorageController` computes and executes a peak-shaving dispatch schedule assuming perfect forecasting. It is designed for reducing peak loads, not meeting a specific demand, using either one or two loads for determining peaks.
+
+```{note}
+The algorithm currently only supports daily cycles, but could be adjusted to accommodate alternate cycle rates.
+```
+
+The controller supports two demand profiles:
+
+- **`demand_profile`** — the local or sub-system demand. Peaks within a configurable daily time window (`peak_range`) are identified as candidate discharge targets.
+- **`demand_profile_upstream`** — an optional upstream or supervisory demand. When provided, an operator can override the local peak schedule up to a configurable number of events per period (e.g., three times per week). Peaks are determined as the highest n peaks in each period.
+
+The `dispatch_priority_demand_profile` parameter selects which profile acts as the override schedule. On days where the priority profile flags a peak (up to n override instances in the given time period), the controller follows that schedule; on all other days it falls back to the other profile.
+
+**Dispatch logic (state machine)**
+
+1. **Discharge** — begins `advance_discharge_period` before the next scheduled peak and runs until `min_soc_fraction` is reached.
+2. **Charge** — resumes after `delay_charge_period` has elapsed since the end of discharge, subject to the `allow_charge_in_peak_range` flag which can block recharging during the peak windows.
+3. **Idle** — all other timesteps; set-point is zero.
+
+An example output for the first week of a one-year simulation is shown below. Orange shading marks the 12:00–19:00 daily peak window. The top panel shows both demand profiles; the second panel shows battery state of charge; the third shows battery charge/discharge power; the fourth shows the resulting net demand. Periods where `demand_profile_upstream` takes precedence are marked with vertical dashed lines (three occurrences in the week shown). Note that where `demand_profile_upstream` does not override, the peaks in `demand_profile` are reduced.
+
+![](./figures/example_peak_load_dispatch.png)
+
+For an example of how to use the `PeakLoadManagementHeuristicOpenLoopStorageController`, see:
+- `examples/33_peak_load_management/`
+
+For API details, see the [`PeakLoadManagementHeuristicOpenLoopStorageController` API documentation](../_autosummary/h2integrate.control.control_strategies.storage.plm_openloop_storage_controller).
diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md
index 6523f091d..e4ad7e8bf 100644
--- a/docs/user_guide/model_overview.md
+++ b/docs/user_guide/model_overview.md
@@ -285,6 +285,7 @@ Below summarizes the available performance, cost, and financial models for each
     - `'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
     - `'HeuristicLoadFollowingStorageController'`: open-loop control that works on a time window basis to set dispatch commands; uses Pyomo
+    - `'PeakLoadManagementHeuristicOpenLoopStorageController'`: open-loop control that reduces peaks rather than trying to meet a load
 - Optimized Dispatch:
     - `'OptimizedDispatchStorageController'`: optimization-based dispatch using Pyomo
 
diff --git a/examples/33_peak_load_management/33_peak_load_management.yaml b/examples/33_peak_load_management/33_peak_load_management.yaml
new file mode 100644
index 000000000..3b0321e48
--- /dev/null
+++ b/examples/33_peak_load_management/33_peak_load_management.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
diff --git a/examples/33_peak_load_management/demand_profiles/demand_profile.yaml b/examples/33_peak_load_management/demand_profiles/demand_profile.yaml
new file mode 100644
index 000000000..655da28d6
--- /dev/null
+++ b/examples/33_peak_load_management/demand_profiles/demand_profile.yaml
@@ -0,0 +1,8760 @@
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diff --git a/examples/33_peak_load_management/demand_profiles/demand_profile_upstream.yaml b/examples/33_peak_load_management/demand_profiles/demand_profile_upstream.yaml
new file mode 100644
index 000000000..a333bfce9
--- /dev/null
+++ b/examples/33_peak_load_management/demand_profiles/demand_profile_upstream.yaml
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diff --git a/examples/33_peak_load_management/driver_config.yaml b/examples/33_peak_load_management/driver_config.yaml
new file mode 100644
index 000000000..0991d8889
--- /dev/null
+++ b/examples/33_peak_load_management/driver_config.yaml
@@ -0,0 +1,5 @@
+name: driver_config
+description: Driver configuration for multivariable streams example
+general:
+  folder_output: outputs
+  create_om_reports: false
diff --git a/examples/33_peak_load_management/plant_config.yaml b/examples/33_peak_load_management/plant_config.yaml
new file mode 100644
index 000000000..8f86f593f
--- /dev/null
+++ b/examples/33_peak_load_management/plant_config.yaml
@@ -0,0 +1,14 @@
+name: plant_config
+description: Demonstrates multivariable streams with a gas combiner
+plant:
+  plant_life: 30
+  simulation:
+    n_timesteps: 8760
+    dt: 3600
+    timezone: -6
+    start_time: 2025/07/01 00:00:00
+technology_interconnections:
+  # include battery charge/discharge in the load
+  - [battery, electrical_load_demand, [electricity_out, electricity_in]]
+  # buy power from the grid to fulfill demand including to accommodate battery operation
+  - [electrical_load_demand, grid_buy, [unmet_electricity_demand_out, electricity_demand]]
diff --git a/examples/33_peak_load_management/run_peak_load_management.py b/examples/33_peak_load_management/run_peak_load_management.py
new file mode 100644
index 000000000..7fb7f10b7
--- /dev/null
+++ b/examples/33_peak_load_management/run_peak_load_management.py
@@ -0,0 +1,147 @@
+"""
+Example 33: Peak load management dispatch
+
+This example demonstrates:
+1. Peak load management dispatch open loop control with two demand profiles of interest
+2. Battery charging without an input stream, assuming purchase from the grid
+
+In this example, two load profiles are provided. It is assumed that demand_profile is a
+subset of demand_profile_upstream, where demand_profile_upstream may represent the total demand of a
+larger/upstream system. In this case, the upstream system reserves the right to choose
+when to dispatch the battery up to three times per week. The remaining days the sub-system
+may choose when and how to dispatch the battery. The subsystem has certain expected peak
+windows that may be critical in terms of their cost structure, grid limits, or some other
+reason and so they will only dispatch the battery for peaks in the chosen range. The top-
+level system does not have such a requirement and will simply dispatch to reduce the highest
+peaks. Perfect a priori demand knowledge is assumed. The battery is only allowed to discharge
+once per day and is further restricted to not charge during the expected peak windows defined
+by the sub-system operator.
+
+The output figure indicates peaks from demand_profile_upstream that were selected to override the
+peaks in demand_profile to demonstrate how the peak selection impacts dispatch.
+
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
+
+from h2integrate.core.utilities import build_time_series_from_plant_config
+from h2integrate.core.h2integrate_model import H2IntegrateModel
+
+
+# Create, setup, and run the H2Integrate model
+model = H2IntegrateModel("33_peak_load_management.yaml")
+
+model.setup()
+model.run()
+model.post_process()
+
+# plot the results for the first week
+supervisor_demand = np.array(
+    model.technology_config["technologies"]["battery"]["model_inputs"]["control_parameters"][
+        "demand_profile_upstream"
+    ]
+)
+
+secondary_demand = model.prob.get_val("battery.electricity_demand", units="kW")
+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]
+
+# Shade peak window on every plotted day
+peak_patch = Patch(facecolor="orange", alpha=0.12, label="Expected peak window (12pm to 7pm)")
+
+fig, ax = plt.subplots(4, 1, sharex=True, figsize=(10, 5))
+ax[0].plot(time_plot, secondary_demand[:n_plot] * 1e-3, label="Original demand (MW)")
+ax[0].plot(time_plot, supervisor_demand[:n_plot] * 1e-3, label="Overriding demand (MW)")
+ax[0].set(ylabel="Power (MW)", ylim=[-2, 2])
+ax[0].legend(handles=[*ax[0].get_legend_handles_labels()[0], peak_patch], frameon=True, ncol=3)
+
+ax[1].plot(time_plot, model.prob.get_val("battery.SOC", units="percent")[:n_plot])
+ax[1].set(ylabel="SOC", ylim=[0, 100])
+ax[1].legend(handles=[*ax[1].get_legend_handles_labels()[0]], frameon=False, ncol=2)
+
+ax[2].plot(time_plot, secondary_demand[:n_plot] * 1e-3, label="Original demand (MW)")
+ax[2].plot(
+    time_plot,
+    model.prob.get_val("battery.electricity_out", units="MW")[:n_plot],
+    label="Battery charge/discharge",
+)
+ax[2].set(ylabel="Power (MW)", ylim=[-2, 2])
+ax[2].legend(handles=[*ax[2].get_legend_handles_labels()[0]], frameon=False, ncol=2)
+
+ax[3].plot(time_plot, secondary_demand[:n_plot] * 1e-3, label="Original demand (MW)")
+ax[3].plot(
+    time_plot,
+    model.prob.get_val("electrical_load_demand.unmet_electricity_demand_out", units="MW")[:n_plot],
+    label="New demand profile",
+)
+ax[3].plot(time_plot, grid_output[:n_plot], label="Grid purchase (MW)", linestyle=":")
+
+ax[3].set(ylabel="Power (MW)", ylim=[-2, 2])
+ax[3].legend(handles=[*ax[3].get_legend_handles_labels()[0]], frameon=False, ncol=3)
+ax[3].tick_params(axis="x", labelrotation=90)
+
+days = pd.to_datetime(np.unique(pd.DatetimeIndex(time_plot).normalize()))
+for axis in ax:
+    for day in days:
+        axis.axvspan(
+            day + pd.Timedelta(hours=12),
+            day + pd.Timedelta(hours=19),
+            color="orange",
+            alpha=0.12,
+            linewidth=0,
+            zorder=0,
+        )
+
+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)
+
+############################## annotate override peaks ###########################
+
+# Find top 3 daily peaks in supervisor demand (one per day in first week)
+supervisor_demand_first_week = supervisor_demand[:n_plot] * 1e-3
+days_first_week = pd.to_datetime(np.unique(pd.DatetimeIndex(time_plot).normalize()))
+
+daily_peaks = []
+for day in days_first_week:
+    day_start = day
+    day_end = day + pd.Timedelta(days=1)
+    day_mask = (time_plot >= day_start) & (time_plot < day_end)
+    if day_mask.any():
+        day_indices = np.where(day_mask)[0]
+        peak_idx = day_indices[np.argmax(supervisor_demand_first_week[day_indices])]
+        peak_time = time_plot[peak_idx]
+        peak_value = supervisor_demand_first_week[peak_idx]
+        daily_peaks.append((peak_time, peak_value))
+
+# Sort by peak value and get top 3
+top_3_peaks = sorted(daily_peaks, key=lambda x: x[1], reverse=True)[:3]
+top_3_peaks_sorted_by_time = sorted(top_3_peaks, key=lambda x: x[0])
+
+# Draw vertical dashed lines for top 3 daily peaks across all subplots
+colors = ["k", "k", "k"]
+for idx, (peak_time, _) in enumerate(top_3_peaks_sorted_by_time):
+    for axis in ax:
+        axis.axvline(peak_time, color=colors[idx], linestyle="--", linewidth=1.5, alpha=0.7)
+    # Add label on top subplot
+    ax[0].text(
+        peak_time,
+        ax[0].get_ylim()[1] + 0.3,
+        f"Peak override {idx + 1}",
+        fontsize=9,
+        ha="center",
+        color=colors[idx],
+        weight="bold",
+    )
+###########################################################################################
+
+plt.tight_layout()
+plt.savefig("example_peak_load_dispatch.png", transparent=False)
diff --git a/examples/33_peak_load_management/tech_config.yaml b/examples/33_peak_load_management/tech_config.yaml
new file mode 100644
index 000000000..e1a9ff88f
--- /dev/null
+++ b/examples/33_peak_load_management/tech_config.yaml
@@ -0,0 +1,69 @@
+name: technology_config
+description: This plant charges a battery from the grid to reduce peak demand
+technologies:
+  battery:
+    performance_model:
+      model: StoragePerformanceModel
+    cost_model:
+      model: ATBBatteryCostModel
+    control_strategy:
+      model: PeakLoadManagementHeuristicOpenLoopStorageController
+    model_inputs:
+      shared_parameters:
+        commodity: electricity
+        commodity_rate_units: kW
+        max_charge_rate: 300.0  # kW
+        max_discharge_rate: 300.0  # kW. Defaults to battery power limit when matched to max_charge_rate
+        charge_equals_discharge: true  # enforce symmetric charge and discharge power limits
+        max_capacity: 1200.0  # kWh, 1 MWh
+        max_soc_fraction: 0.9  # percent as decimal
+        min_soc_fraction: 0.1  # percent as decimal
+        init_soc_fraction: 0.9  # percent as decimal
+        charge_efficiency: 1.0  # percent as decimal
+        discharge_efficiency: 1.0  # percent as decimal
+        demand_profile: !include demand_profiles/demand_profile.yaml
+      control_parameters:
+        demand_profile_upstream: !include demand_profiles/demand_profile_upstream.yaml  # demand used to define when the supervisor commands battery dispatch. This may represent an upstream load.
+        dispatch_priority_demand_profile: demand_profile_upstream  # demand profile the controller prioritizes when deciding dispatch
+        n_override_events: 3  # maximum number of peak events from the priority demand profile that the controller will respond to within each event period
+        override_events_period: W  # pandas-style period code for resetting event counts; W means per week, M means month, etc
+        peak_range:
+          start: 12:00:00  # start of the preferred daily peak-shaving window
+          end: 19:00:00  # end of the preferred daily peak-shaving window
+        advance_discharge_period:
+          units: h  # time units applied to the advance-discharge offset
+          val: 1  # begin discharging this far ahead of the detected peak
+        delay_charge_period:
+          units: h  # time units applied to the charge-delay offset
+          val: 4  # wait this long after the peak window before recharging
+        allow_charge_in_peak_range: false  # if false, charging is blocked during the peak window
+        min_peak_proximity:
+          units: h  # time units used for the minimum distance-to-peak threshold
+          val: 4  # throws an error when the next peak is closer than this threshold
+      cost_parameters:
+        cost_year: 2024
+        energy_capex: 408 # $/kWh  # conservative case for 2024 ATB utility scale batteries
+        power_capex: 379 # $/kW  # conservative case for 2024 ATB utility scale batteries
+        opex_fraction: 0.025  # 2.5% percent of capex as per 2024 ATB for utility scale batteries
+  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
diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py
index 4bf2a1229..a38500ee0 100644
--- a/examples/test/test_all_examples.py
+++ b/examples/test/test_all_examples.py
@@ -2782,3 +2782,49 @@ def test_multivariable_streams_example(subtests, temp_copy_of_example):
     with subtests.test("Consumer avg pressure"):
         avg_pres = model.prob.get_val("gas_consumer.avg_pressure", units="bar")
         assert avg_pres[0] == pytest.approx(10.40, rel=1e-3)
+
+
+@pytest.mark.integration
+@pytest.mark.parametrize(
+    "example_folder,resource_example_folder", [("33_peak_load_management", None)]
+)
+def test_peak_load_management_example(subtests, temp_copy_of_example):
+    example_folder = temp_copy_of_example
+
+    model = H2IntegrateModel(example_folder / "33_peak_load_management.yaml")
+    model.setup()
+    model.run()
+
+    with subtests.test("Battery SOC mean"):
+        soc = model.prob.get_val("battery.SOC", units="percent")
+        assert soc.mean() == pytest.approx(63.333, rel=1e-3)
+
+    with subtests.test("Battery SOC stays within bounds"):
+        soc = model.prob.get_val("battery.SOC", units="percent")
+        assert soc.max() <= 90.0 + 1e-3
+        assert soc.min() >= 10.0 - 1e-3
+
+    with subtests.test("Battery set point sum"):
+        set_point = model.prob.get_val("battery.electricity_set_point", units="kW")
+        assert set_point.sum() == pytest.approx(60.0, rel=1e-3)
+
+    with subtests.test("Battery electricity out sum"):
+        elec_out = model.prob.get_val("battery.electricity_out", units="kW")
+        assert elec_out.sum() == pytest.approx(60.0, rel=1e-3)
+
+    with subtests.test("Unmet demand sum"):
+        unmet = model.prob.get_val(
+            "electrical_load_demand.unmet_electricity_demand_out", units="kW"
+        )
+        assert unmet.sum() == pytest.approx(1947378.0, rel=1e-3)
+
+    with subtests.test("Battery CapEx"):
+        capex = model.prob.get_val("battery.CapEx", units="USD")
+        assert capex[0] == pytest.approx(603300.0, rel=1e-3)
+
+    with subtests.test("Unmet demand sum equals purchased electricity"):
+        battery_unmet_demand = model.prob.get_val(
+            "electrical_load_demand.unmet_electricity_demand_out", units="kW"
+        )
+        grid_purchase = model.prob.get_val("grid_buy.electricity_out", units="kW")
+        assert battery_unmet_demand.sum() == pytest.approx(grid_purchase.sum(), rel=1e-3)
diff --git a/h2integrate/control/control_strategies/storage/demand_openloop_storage_controller.py b/h2integrate/control/control_strategies/storage/demand_openloop_storage_controller.py
index cd7cfd78e..4455fb11e 100644
--- a/h2integrate/control/control_strategies/storage/demand_openloop_storage_controller.py
+++ b/h2integrate/control/control_strategies/storage/demand_openloop_storage_controller.py
@@ -1,10 +1,9 @@
 from copy import deepcopy
 
 import numpy as np
-from attrs import field, define
+from attrs import define
 
 from h2integrate.core.utilities import merge_shared_inputs
-from h2integrate.core.validators import gte_zero, range_val, range_val_or_none
 from h2integrate.control.control_strategies.storage.openloop_storage_control_base import (
     StorageOpenLoopControlBase,
     StorageOpenLoopControlBaseConfig,
@@ -18,53 +17,9 @@ class DemandOpenLoopStorageControllerConfig(StorageOpenLoopControlBaseConfig):
 
     This class defines the parameters required to configure the `DemandOpenLoopStorageController`.
 
-    Attributes:
-        commodity (str): Name of the commodity being controlled
-            (e.g., "hydrogen"). Stripped of whitespace.
-        commodity_rate_units (str): Units of the commodity (e.g., "kg/h").
-        demand_profile (int | float | list): Demand values for each timestep, in
-            the same units as `commodity_rate_units`. May be a scalar for constant
-            demand or a list/array for time-varying demand.
-        max_capacity (float): Maximum storage capacity of the commodity (in non-rate units,
-            e.g., "kg" if `commodity_rate_units` is "kg/h").
-        max_soc_fraction (float): Maximum allowable state of charge (SOC) as a fraction
-            of `max_capacity`, between 0 and 1.
-        min_soc_fraction (float): Minimum allowable SOC as a fraction of `max_capacity`,
-            between 0 and 1.
-        init_soc_fraction (float): Initial SOC as a fraction of `max_capacity`,
-            between 0 and 1.
-        max_charge_rate (float): Maximum rate at which the commodity can be charged (in units
-            per time step, e.g., "kg/time step"). This rate does not include the charge_efficiency.
-        charge_equals_discharge (bool, optional): If True, set the max_discharge_rate equal to the
-            max_charge_rate. If False, specify the max_discharge_rate as a value different than
-            the max_charge_rate. Defaults to True.
-        max_discharge_rate (float | None, optional): Maximum rate at which the commodity can be
-            discharged (in units per time step, e.g., "kg/time step"). This rate does not include
-            the discharge_efficiency. Only required if `charge_equals_discharge` is False.
-        charge_efficiency (float | None, optional): Efficiency of charging the storage, represented
-            as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if
-            `round_trip_efficiency` is provided.
-        discharge_efficiency (float | None, optional): Efficiency of discharging the storage,
-            represented as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if
-            `round_trip_efficiency` is provided.
-        round_trip_efficiency (float | None, optional): Combined efficiency of charging and
-            discharging the storage, represented as a decimal between 0 and 1 (e.g., 0.81 for
-            81% efficiency). Optional if `charge_efficiency` and `discharge_efficiency` are
-            provided.
-        commodity_amount_units (str | None, optional): Units of the commodity as an amount
-            (i.e., kW*h or kg). If not provided, defaults to commodity_rate_units*h.
     """
 
-    max_capacity: float = field()
-    max_soc_fraction: float = field(validator=range_val(0, 1))
-    min_soc_fraction: float = field(validator=range_val(0, 1))
-    init_soc_fraction: float = field(validator=range_val(0, 1))
-    max_charge_rate: float = field(validator=gte_zero)
-    charge_equals_discharge: bool = field(default=True)
-    max_discharge_rate: float | None = field(default=None)
-    charge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1))
-    discharge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1))
-    round_trip_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1))
+    require_storage_parameters = True
 
     def __attrs_post_init__(self):
         """
@@ -77,33 +32,7 @@ def __attrs_post_init__(self):
         """
         super().__attrs_post_init__()
 
-        if (self.round_trip_efficiency is not None) and (
-            self.charge_efficiency is None and self.discharge_efficiency is None
-        ):
-            # Calculate charge and discharge efficiencies from round-trip efficiency
-            self.charge_efficiency = np.sqrt(self.round_trip_efficiency)
-            self.discharge_efficiency = np.sqrt(self.round_trip_efficiency)
-
-        if self.charge_efficiency is None or self.discharge_efficiency is None:
-            raise ValueError(
-                "Exactly one of the following sets of parameters must be set: (a) "
-                "`round_trip_efficiency`, or (b) both `charge_efficiency` "
-                "and `discharge_efficiency`."
-            )
-
-        if self.charge_equals_discharge:
-            if (
-                self.max_discharge_rate is not None
-                and self.max_discharge_rate != self.max_charge_rate
-            ):
-                msg = (
-                    "Max discharge rate does not equal max charge rate but charge_equals_discharge "
-                    f"is True. Discharge rate is {self.max_discharge_rate} and charge rate "
-                    f"is {self.max_charge_rate}."
-                )
-                raise ValueError(msg)
-
-            self.max_discharge_rate = self.max_charge_rate
+        self.common_post_init_operations()
 
 
 class DemandOpenLoopStorageController(StorageOpenLoopControlBase):
@@ -189,21 +118,8 @@ def compute(self, inputs, outputs):
             None
         """
         commodity = self.config.commodity
-        if np.all(inputs[f"{commodity}_demand"] == 0.0):
-            msg = "Demand profile is zero, check that demand profile is input"
-            raise UserWarning(msg)
-        if inputs["max_charge_rate"][0] < 0:
-            msg = (
-                f"max_charge_rate cannot be less than zero and has value of "
-                f"{inputs['max_charge_rate']}"
-            )
-            raise UserWarning(msg)
-        if inputs["storage_capacity"][0] < 0:
-            msg = (
-                f"storage_capacity cannot be less than zero and has value of "
-                f"{inputs['storage_capacity']}"
-            )
-            raise UserWarning(msg)
+
+        self.common_checks_needed_in_compute(inputs)
 
         max_capacity = inputs["storage_capacity"].item()
         max_charge_rate = inputs["max_charge_rate"].item()
diff --git a/h2integrate/control/control_strategies/storage/openloop_storage_control_base.py b/h2integrate/control/control_strategies/storage/openloop_storage_control_base.py
index 9f013d311..d7a93c59b 100644
--- a/h2integrate/control/control_strategies/storage/openloop_storage_control_base.py
+++ b/h2integrate/control/control_strategies/storage/openloop_storage_control_base.py
@@ -1,7 +1,11 @@
+from typing import ClassVar
+
+import numpy as np
 import openmdao.api as om
-from attrs import field, define
+from attrs import field, define, validators
 
 from h2integrate.core.utilities import BaseConfig
+from h2integrate.core.validators import gte_zero, range_val_or_none
 
 
 @define(kw_only=True)
@@ -12,23 +16,127 @@ class StorageOpenLoopControlBaseConfig(BaseConfig):
      Attributes:
         commodity (str): Name of the commodity being stored (e.g., "hydrogen").
         commodity_rate_units (str): Rate units of the commodity (e.g., "kg/h" or "kW").
-        demand_profile (int | float | list): Demand values for each timestep, in
+        demand_profile (int | float | list | dict): Demand values for each timestep, in
             the same units as `commodity_rate_units`. May be a scalar for constant
-            demand or a list/array for time-varying demand.
+            demand or a list/array/dict for time-varying demand. If a dict is provided, it
+            it should have two keys: "time_date" and "demand".
         commodity_amount_units (str | None, optional): Units of the commodity as an amount
             (i.e., kW*h or kg). If not provided, defaults to `commodity_rate_units*h`.
-
+        require_storage_parameters (ClassVar[bool]): Class-level flag used by child config
+            classes to require storage sizing and efficiency parameters. Leave False for
+            controllers that do not need storage-specific fields. Set to True in child
+            config classes that require optional attributes.
+
+    Optional Attributes:
+        max_capacity (float): Maximum storage capacity of the commodity (in non-rate units,
+            e.g., "kg" if `commodity_rate_units` is "kg/h").
+        max_soc_fraction (float): Maximum allowable state of charge (SOC) as a fraction
+            of `max_capacity`, between 0 and 1.
+        min_soc_fraction (float): Minimum allowable SOC as a fraction of `max_capacity`,
+            between 0 and 1.
+        init_soc_fraction (float): Initial SOC as a fraction of `max_capacity`,
+            between 0 and 1.
+        max_charge_rate (float): Maximum rate at which the commodity can be charged (in units
+            per time step, e.g., "kg/time step"). This rate does not include the charge_efficiency.
+        charge_equals_discharge (bool, optional): If True, set the max_discharge_rate equal to the
+            max_charge_rate. If False, specify the max_discharge_rate as a value different than
+            the max_charge_rate. Defaults to True.
+        max_discharge_rate (float | None, optional): Maximum rate at which the commodity can be
+            discharged (in units per time step, e.g., "kg/time step"). This rate does not include
+            the discharge_efficiency. Only required if `charge_equals_discharge` is False.
+        charge_efficiency (float | None, optional): Efficiency of charging the storage, represented
+            as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if
+            `round_trip_efficiency` is provided.
+        discharge_efficiency (float | None, optional): Efficiency of discharging the storage,
+            represented as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if
+            `round_trip_efficiency` is provided.
+        round_trip_efficiency (float | None, optional): Combined efficiency of charging and
+            discharging the storage, represented as a decimal between 0 and 1 (e.g., 0.81 for
+            81% efficiency). Optional if `charge_efficiency` and `discharge_efficiency` are
+            provided.
     """
 
     commodity: str = field()
     commodity_rate_units: str = field()
-    demand_profile: int | float | list = field()
+    demand_profile: int | float | list | dict = field()
     commodity_amount_units: str = field(default=None)
 
+    # Child classes can set this to True to require the storage sizing/efficiency fields.
+    require_storage_parameters: ClassVar[bool] = False
+
+    max_capacity: float | None = field(default=None)
+    max_soc_fraction: float | None = field(default=None, validator=range_val_or_none(0, 1))
+    min_soc_fraction: float | None = field(default=None, validator=range_val_or_none(0, 1))
+    init_soc_fraction: float | None = field(default=None, validator=range_val_or_none(0, 1))
+    max_charge_rate: float | None = field(default=None, validator=validators.optional(gte_zero))
+    charge_equals_discharge: bool = field(default=True)
+    max_discharge_rate: float | None = field(default=None)
+    charge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1))
+    discharge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1))
+    round_trip_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1))
+
     def __attrs_post_init__(self):
         if self.commodity_amount_units is None:
             self.commodity_amount_units = f"({self.commodity_rate_units})*h"
 
+        if self.require_storage_parameters:
+            self._validate_required_storage_parameters()
+
+    def _validate_required_storage_parameters(self):
+        required_param_names = [
+            "max_capacity",
+            "max_soc_fraction",
+            "min_soc_fraction",
+            "init_soc_fraction",
+            "max_charge_rate",
+        ]
+        missing = [name for name in required_param_names if getattr(self, name) is None]
+        if missing:
+            raise ValueError(
+                "Missing required storage configuration parameter(s): " f"{', '.join(missing)}"
+            )
+
+        if not self.charge_equals_discharge and self.max_discharge_rate is None:
+            raise ValueError(
+                "max_discharge_rate must be provided when charge_equals_discharge is False."
+            )
+
+    def common_post_init_operations(self):
+        """
+        Post-initialization logic to validate and calculate efficiencies.
+
+        Ensures that either `charge_efficiency` and `discharge_efficiency` are provided,
+        or `round_trip_efficiency` is provided. If `round_trip_efficiency` is provided,
+        it calculates `charge_efficiency` and `discharge_efficiency` as the square root
+        of `round_trip_efficiency`.
+        """
+        if (self.round_trip_efficiency is not None) and (
+            self.charge_efficiency is None and self.discharge_efficiency is None
+        ):
+            # Calculate charge and discharge efficiencies from round-trip efficiency
+            self.charge_efficiency = np.sqrt(self.round_trip_efficiency)
+            self.discharge_efficiency = np.sqrt(self.round_trip_efficiency)
+        if self.charge_efficiency is None or self.discharge_efficiency is None:
+            raise ValueError(
+                "Exactly one of the following sets of parameters must be set: (a) "
+                "`round_trip_efficiency`, or (b) both `charge_efficiency` "
+                "and `discharge_efficiency`."
+            )
+
+        if self.charge_equals_discharge:
+            if (
+                self.max_discharge_rate is not None
+                and self.max_discharge_rate != self.max_charge_rate
+            ):
+                msg = (
+                    "Max discharge rate does not equal max charge rate but charge_equals_discharge "
+                    f"is True. Discharge rate is {self.max_discharge_rate} and charge rate "
+                    f"is {self.max_charge_rate}."
+                )
+                raise ValueError(msg)
+
+            self.max_discharge_rate = self.max_charge_rate
+
 
 class StorageOpenLoopControlBase(om.ExplicitComponent):
     """Base OpenMDAO component for open-loop demand tracking.
@@ -53,9 +161,11 @@ def setup(self):
 
         commodity = self.config.commodity
 
+        demand_data = self.config.demand_profile
+
         self.add_input(
             f"{commodity}_demand",
-            val=self.config.demand_profile,
+            val=demand_data if not isinstance(demand_data, dict) else demand_data["demand"],
             shape=self.n_timesteps,
             units=self.config.commodity_rate_units,
             desc=f"Demand profile of {commodity}",
@@ -85,3 +195,20 @@ def compute():
             NotImplementedError: Always, unless implemented in a subclass.
         """
         raise NotImplementedError("This method should be implemented in a subclass.")
+
+    def common_checks_needed_in_compute(self, inputs):
+        if np.all(inputs[f"{self.config.commodity}_demand"] == 0.0):
+            msg = "Demand profile is zero, check that demand profile is input"
+            raise UserWarning(msg)
+        if inputs["max_charge_rate"][0] < 0:
+            msg = (
+                f"max_charge_rate cannot be less than zero and has value of "
+                f"{inputs['max_charge_rate']}"
+            )
+            raise UserWarning(msg)
+        if inputs["storage_capacity"][0] < 0:
+            msg = (
+                f"storage_capacity cannot be less than zero and has value of "
+                f"{inputs['storage_capacity']}"
+            )
+            raise UserWarning(msg)
diff --git a/h2integrate/control/control_strategies/storage/plm_openloop_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_openloop_storage_controller.py
new file mode 100644
index 000000000..31624ed8a
--- /dev/null
+++ b/h2integrate/control/control_strategies/storage/plm_openloop_storage_controller.py
@@ -0,0 +1,685 @@
+import warnings
+from copy import deepcopy
+from datetime import datetime, timedelta
+
+import numpy as np
+import pandas as pd
+from attrs import field, define
+
+from h2integrate.core.utilities import merge_shared_inputs, build_time_series_from_plant_config
+from h2integrate.core.validators import contains, has_required_keys
+from h2integrate.control.control_strategies.storage.openloop_storage_control_base import (
+    StorageOpenLoopControlBase,
+    StorageOpenLoopControlBaseConfig,
+)
+
+
+@define(kw_only=True)
+class PeakLoadManagementHeuristicOpenLoopStorageControllerConfig(StorageOpenLoopControlBaseConfig):
+    """
+    Configuration class for the PeakLoadManagementHeuristicOpenLoopStorageController.
+
+    Defines peak-selection and dispatch-priority rules used to pre-compute
+    an open-loop discharge and recharge schedule.
+
+    Attributes:
+        demand_profile_upstream (int | float | list | None, optional): Demand values for
+            additional connected system for each timestep, in the same units as
+            `commodity_rate_units`. May be a scalar for constant demand or a list/array for
+            time-varying demand.
+        dispatch_priority_demand_profile (str | None, optional): which demand profile takes
+            precedence for dispatch decisions. One of ["demand_profile", "demand_profile_upstream"].
+        n_override_events: (int | None, optional): The maximum number of discharge events
+            allowed for the priority profile in the period specified in override_events_period,
+            or across all time steps if override_events_period is None.
+        override_events_period: (int | None, optional): Duration, in time steps, of the period
+            in which the n_override_events must occur or a str indicating the time period (e.g.
+            W for week, M for month). Defaults to the length of the simulation.
+        peak_range (dict): Daily time window restricting which timesteps are considered as peak
+            candidates in the primary demand profile. Keys ``start`` and ``end`` must be
+            ``HH:MM:SS`` strings (e.g. ``{'start': '12:00:00', 'end': '17:00:00'}``). Only
+            the highest-demand timestep within this window is marked as a candidate peak for
+            each day.
+        advance_discharge_period (dict): Lead time before a detected peak at which discharge
+            mode activates. Dict with keys ``units`` (pandas timedelta unit string, e.g. ``'h'``)
+            and ``val`` (numeric). For example ``{'units': 'h', 'val': 2}`` begins discharge two
+            hours before the identified peak.
+        delay_charge_period (dict): Minimum time to wait after the battery reaches minimum SOC
+            before recharging is permitted. Dict with keys ``units`` and ``val``, using the same
+            format as ``advance_discharge_period``.
+        allow_charge_in_peak_range (bool, optional): If ``True``, charging is never suppressed.
+            If ``False``, charging is blocked for timesteps that fall inside ``peak_range`` to
+            prevent charging whilst peak demand is expected. Defaults to ``True``.
+        min_peak_proximity (dict): Minimum required time separation between consecutive retained
+            peak events. A ``ValueError`` is raised during setup if selected peaks violate this
+            constraint. Dict with keys ``units`` and ``val``, using the same format as
+            ``advance_discharge_period``.
+
+    """
+
+    require_storage_parameters = True
+
+    demand_profile_upstream: int | float | list | None = field()
+    dispatch_priority_demand_profile: str = field(
+        validator=contains(["demand_profile", "demand_profile_upstream"]),
+    )
+    n_override_events: int | None = field(default=None)
+    override_events_period: int | str | None = field(default=None)
+    peak_range: dict = field(validator=has_required_keys(["start", "end"]))
+    advance_discharge_period: dict = field(validator=has_required_keys(["units", "val"]))
+    delay_charge_period: dict = field(validator=has_required_keys(["units", "val"]))
+    allow_charge_in_peak_range: bool = field(default=True)
+    min_peak_proximity: dict = field(validator=has_required_keys(["units", "val"]))
+
+    def __attrs_post_init__(self):
+        super().__attrs_post_init__()
+
+        self.common_post_init_operations()
+
+        # Validate and normalize dict parameters
+        # peak_range: must have 'start' and 'end' keys as HH:MM:SS strings.
+        # YAML automatically converts HH:MM:SS to an integer number of seconds,
+        # so non-string values are converted back here.
+        for _key in ("start", "end"):
+            if _key not in self.peak_range:
+                raise ValueError(
+                    f"peak_range is missing required key '{_key}'. "
+                    "Expected dict with 'start' and 'end' as HH:MM:SS strings."
+                )
+        for key, value in self.peak_range.items():
+            if not isinstance(value, str):
+                self.peak_range[key] = str(timedelta(seconds=value))
+
+        # advance_discharge_period / delay_charge_period / min_peak_proximity:
+        # must each be a dict with 'units' (str) and 'val' (int or float).
+        for _param_name, _param_val in (
+            ("advance_discharge_period", self.advance_discharge_period),
+            ("delay_charge_period", self.delay_charge_period),
+            ("min_peak_proximity", self.min_peak_proximity),
+        ):
+            if not isinstance(_param_val, dict):
+                raise ValueError(
+                    f"'{_param_name}' must be a dict with keys 'units' and 'val', "
+                    f"got {type(_param_val).__name__}."
+                )
+            for _key in ("units", "val"):
+                if _key not in _param_val:
+                    raise ValueError(
+                        f"'{_param_name}' is missing required key '{_key}'. "
+                        "Expected dict with 'units' (str) and 'val' (int or float)."
+                    )
+            if not isinstance(_param_val["units"], str) or not _param_val["units"].strip():
+                raise ValueError(
+                    f"'{_param_name}[\"units\"]' must be a non-empty string, "
+                    f"got {_param_val['units']!r}."
+                )
+            if not isinstance(_param_val["val"], int | float):
+                raise ValueError(
+                    f"'{_param_name}[\"val\"]' must be a numeric value (int or float), "
+                    f"got {type(_param_val['val']).__name__}."
+                )
+
+
+class PeakLoadManagementHeuristicOpenLoopStorageController(StorageOpenLoopControlBase):
+    """
+    Peak-load management storage controller implementing an open-loop control strategy.
+
+    This controller manages commodity (e.g., hydrogen) storage to reduce detected demand peaks.
+    It detects peaks in the demand profile using configurable time
+    windows and event limits, then uses multi-stage state machine control to:
+
+    1. Discharge storage in advance of peaks (configurable lead time)
+    2. Charge storage during expected low-demand periods (using provided charging window bounds)
+    3. Enforce SOC, rate, and efficiency limits throughout
+
+    The controller uses an open-loop architecture where peak discharge/charge decisions are
+    pre-planned during setup() rather than dynamically optimized during compute().
+    """
+
+    def setup(self):
+        """Initialize controller configuration, storage inputs, and compute peak schedules.
+
+        During setup:
+        1. Loads and validates configuration from tech_config and plant_config options
+        2. Registers OpenMDAO inputs for storage parameters (capacity, charge rates, etc.)
+        3. Detects peaks in the demand profile (demand_profile and demand_profile_upstream)
+        4. Merges peaks with demand_profile_upstream prioritization if configured
+        5. Computes time-to-next-peak for each timestep
+        6. Identifies allowed charging windows based on peak_range configuration
+
+        Raises:
+            ValueError: If configuration is invalid or required keys are missing
+        """
+        self.config = PeakLoadManagementHeuristicOpenLoopStorageControllerConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control"),
+            strict=False,
+            additional_cls_name=self.__class__.__name__,
+        )
+        super().setup()
+
+        if (
+            self.config.demand_profile_upstream is None
+            and self.config.dispatch_priority_demand_profile == "demand_profile_upstream"
+        ):
+            raise (
+                ValueError(
+                    "If demand_profile_upstream is None, then dispatch_priority_demand_profile"
+                    "must be demand_profile"
+                )
+            )
+
+        # Register storage system design constraint inputs
+        self.add_input(
+            "max_charge_rate",
+            val=self.config.max_charge_rate,
+            units=self.config.commodity_rate_units,
+            desc="Maximum charging rate for the storage system",
+        )
+
+        self.add_input(
+            "storage_capacity",
+            val=self.config.max_capacity,
+            units=self.config.commodity_amount_units,
+            desc="Total storage capacity (including unusable amounts)",
+        )
+
+        if not self.config.charge_equals_discharge:
+            self.add_input(
+                "max_discharge_rate",
+                val=self.config.max_discharge_rate,
+                units=self.config.commodity_rate_units,
+                desc="Maximum discharging rate for the storage system",
+            )
+
+        # Store simulation parameters for later use
+        self.time_index = build_time_series_from_plant_config(self.options["plant_config"])
+
+        if len(self.time_index) != self.n_timesteps:
+            raise (
+                ValueError(
+                    f"Time series is of length {len(self.time_index)}, "
+                    f"but self.n_timesteps is {self.n_timesteps}."
+                )
+            )
+
+        # Detect peaks in demand profile 2 (if provided)
+        if self.config.demand_profile_upstream is not None:
+            demand_profile_upstream = self._build_demand_profile_dict(
+                self.config.demand_profile_upstream,
+                self.time_index,
+            )
+            self.peaks_2_df = self.get_peaks(
+                demand_profile=demand_profile_upstream,
+                n_override_events=self.config.n_override_events,
+                override_events_period=self.config.override_events_period,
+                min_proximity=self.config.min_peak_proximity,
+            )
+        else:
+            self.peaks_2_df = None
+
+    def compute(self, inputs, outputs):
+        """
+        Compute storage state of charge (SOC), delivered output, curtailment, and unmet
+        demand over the simulation horizon.
+
+        This method applies an open-loop storage control strategy to balance the
+        commodity demand and input flow. When input exceeds demand, excess commodity
+        is used to charge storage (subject to rate, efficiency, and SOC limits). When
+        demand exceeds input, storage is discharged to meet the deficit (also subject
+        to constraints). SOC is updated at each time step, ensuring it remains within
+        allowable bounds.
+
+        Dispatch strategy outline:
+        - Discharge:
+          * Starting when time_to_peak <= advance_discharge_period
+          * Discharge at max rate (or less to reach targets)
+          * Stop discharging only when SOC reaches min_soc
+        - Charge:
+          * When not discharging, SOC < max, and allow_charge window is active
+          * Start charging only after delay_charge_period since last discharge
+          * Charge at max rate (or less to reach target)
+          * Stop charging when SOC reaches max_soc
+
+        Expected input keys:
+            * ``<commodity>_in``: Timeseries of commodity available at each time step.
+            * ``<commodity>_demand``: Timeseries demand profile.
+            * ``max_charge_rate``: Maximum charge rate permitted.
+            * ``max_capacity``: Maximum total storage capacity.
+
+        Outputs populated:
+            * ``<commodity>_set_point``: Dispatch command to storage,
+                negative when charging, positive when discharging.
+
+        Raises:
+            UserWarning: If the demand profile is entirely zero.
+            UserWarning: If ``max_charge_rate`` or ``max_capacity`` is negative.
+
+        Returns:
+            None
+        """
+
+        self.common_checks_needed_in_compute(inputs)
+
+        commodity = self.config.commodity
+
+        if self.config.charge_equals_discharge:
+            max_discharge_rate = inputs["max_charge_rate"].item()
+        else:
+            max_discharge_rate = inputs["max_discharge_rate"].item()
+
+        max_capacity = inputs["storage_capacity"].item()
+        max_charge_rate = inputs["max_charge_rate"].item()
+
+        soc_max = self.config.max_soc_fraction
+        soc_min = self.config.min_soc_fraction
+        init_soc_fraction = self.config.init_soc_fraction
+
+        charge_eff = float(self.config.charge_efficiency)
+        discharge_eff = float(self.config.discharge_efficiency)
+
+        # Build timestamped demand dictionaries from simulation timeline.
+        demand_profile = self._build_demand_profile_dict(
+            inputs[f"{commodity}_demand"],
+            self.time_index,
+        )
+
+        # Detect daily peaks in demand profile (always computed)
+        # Respects the configured peak_range time window for each day
+        self.peaks_1_df = self.get_peaks(
+            demand_profile=demand_profile,
+            peak_range=self.config.peak_range,
+        )
+
+        if self.config.dispatch_priority_demand_profile == "demand_profile_upstream":
+            self.peaks_df = self.merge_peaks(self.peaks_2_df, self.peaks_1_df)
+        else:
+            self.peaks_df = self.merge_peaks(self.peaks_1_df, self.peaks_2_df)
+
+        self.get_time_to_peak()
+
+        self.get_allowed_charge()
+
+        # Initialize time-step state of charge prior to loop so the loop starts with
+        # the previous time step's value
+        soc = deepcopy(init_soc_fraction)
+
+        # initialize outputs
+        soc_array = np.zeros(self.n_timesteps)
+        set_point_array = np.zeros(self.n_timesteps)
+
+        # State machine to track discharge/charge mode
+        discharging = False
+        charging = False
+
+        advance_discharge_period = pd.Timedelta(
+            value=self.config.advance_discharge_period["val"],
+            unit=self.config.advance_discharge_period["units"],
+        )
+        delay_charge_period = pd.Timedelta(
+            value=self.config.delay_charge_period["val"],
+            unit=self.config.delay_charge_period["units"],
+        )
+
+        # Initialize: no discharge has occurred yet
+        last_discharge = self.peaks_df["date_time"].iloc[0] - delay_charge_period
+
+        # Process each timestep using the pre-computed peak schedule
+        for i in range(self.n_timesteps):
+            time_stamp = self.peaks_df["date_time"].iloc[i]
+            time_to_peak = self.peaks_df["time_to_peak"].iloc[i]
+
+            # Get the input flow at the current time step
+            inputs[f"{commodity}_in"][i]
+
+            # Calculate the available charge/discharge capacity
+            available_charge = float((soc_max - soc) * max_capacity)
+            available_discharge = float((soc - soc_min) * max_capacity)
+
+            # start discharging when we approach a peak and have some charge
+            if time_to_peak <= advance_discharge_period and soc > soc_min:
+                discharging = True
+                charging = False
+
+            if not discharging and soc < soc_max:
+                if self.peaks_df["allow_charge"].iloc[i]:
+                    if (time_stamp - last_discharge) > delay_charge_period:
+                        charging = True
+                        discharging = False
+
+            if discharging:
+                # DISCHARGE MODE: Supply commodity to meet peak demand
+                # Note: available_discharge is internal (storage view),
+                # max_discharge_rate is external
+                discharge = min(available_discharge, max_discharge_rate / discharge_eff)
+
+                soc -= discharge / max_capacity  # Deplete storage state of charge
+                # Output setpoint is the external (delivered) rate after efficiency loss
+                set_point_array[i] = discharge * discharge_eff
+
+                # Mark discharge completion time for charging delay calculation
+                if soc <= soc_min:
+                    last_discharge = time_stamp
+
+            elif charging:
+                # CHARGE MODE: Store commodity by charging from assumed infinite source
+                # `charge` is as seen by the storage, but the things being compared should all be as
+                # seen outside the storage so we need to adjust `available_charge` outside the
+                # storage view and the final result back into the storage view.
+                charge = min(available_charge / charge_eff, max_charge_rate) * charge_eff
+                soc += charge / max_capacity  # soc is a ratio with value between 0 and 1
+                set_point_array[i] = -1 * charge / charge_eff
+
+            # Ensure SOC stays within bounds
+            soc = max(soc_min, min(soc_max, soc))
+
+            # Record the SOC for the current time step
+            soc_array[i] = deepcopy(soc)
+
+            # stay in discharge mode until the battery is fully discharged
+            if soc <= soc_min:
+                discharging = False
+            if soc >= soc_max:
+                charging = False
+
+        outputs[f"{commodity}_set_point"] = set_point_array
+
+        # insert warning message if for any time step the magnitude of
+        # any negative entry in set_point_array is greater than inputs[f"{commodity}_in"]
+        charging_requested = -np.minimum(set_point_array, 0.0)
+        available_input = np.asarray(inputs[f"{commodity}_in"])
+        exceeds_available_input = charging_requested > available_input
+
+        if np.any(exceeds_available_input):
+            first_idx = int(np.where(exceeds_available_input)[0][0])
+            msg = (
+                f"WARNING: At time step index {first_idx}, requested charging rate "
+                f"({charging_requested[first_idx]}) exceeds available {commodity} input "
+                f"({available_input[first_idx]})."
+            )
+            warnings.warn(msg, UserWarning)
+
+    @staticmethod
+    def _build_demand_profile_dict(demand_profile, time_series):
+        """Convert scalar/list demand input into a timestamped demand dictionary."""
+        n_timesteps = len(time_series)
+        if np.isscalar(demand_profile):
+            demand_values = np.full(n_timesteps, float(demand_profile), dtype=float)
+        else:
+            demand_values = np.asarray(demand_profile, dtype=float)
+
+        if len(demand_values) != n_timesteps:
+            raise ValueError(
+                "demand_profile length must equal n_timesteps "
+                f"({len(demand_values)} != {n_timesteps})"
+            )
+
+        return {
+            "date_time": time_series,
+            "demand": demand_values,
+        }
+
+    @staticmethod
+    def _parse_peak_range(peak_range):
+        """Validate and parse peak_range values from HH:MM:SS strings.
+
+        Returns a dict with datetime.time objects.
+        """
+        if not isinstance(peak_range, dict):
+            raise ValueError("peak_range must be a dict with keys 'start' and 'end'")
+        if "start" not in peak_range or "end" not in peak_range:
+            raise ValueError("peak_range must be a dict with keys 'start' and 'end'")
+
+        parsed = {}
+        for key, value in peak_range.items():
+            if not isinstance(value, str):
+                raise ValueError(f"peak_range['{key}'] must be an HH:MM:SS string")
+            parsed[key] = datetime.strptime(value, "%H:%M:%S").time()
+
+        return parsed
+
+    @staticmethod
+    def get_peaks(
+        demand_profile: dict,
+        n_override_events=None,
+        override_events_period=None,
+        min_proximity=None,
+        peak_range={"start": "00:00:00", "end": "23:59:59"},
+    ):
+        """Detect demand peaks using configurable time windows and event limits.
+
+        Identifies peak demand periods from a demand profile, with control over:
+        - Daily time windows (e.g., peak detection only 12:00-17:00 each day)
+        - Event frequency (e.g., max 1 peak per week)
+        - Temporal spacing (e.g., minimum 24 hours between peaks)
+
+        Args:
+            demand_profile (dict): Timeseries data with keys:
+                - 'date_time': timestamps (list or DatetimeIndex convertible)
+                - 'demand': demand values (list or array)
+            n_override_events (int | None): Maximum number of peaks to keep globally or per period.
+                If None, returns all daily peaks. Defaults to None.
+            override_events_period (int | str | None): Grouping period for n_override_events limit.
+                - None: apply n_override_events limit globally (keep top-N peaks overall)
+                - int: group by timestep intervals (e.g., 288 for 24-hour periods)
+                - str: pandas period frequency (e.g., 'W' for week, 'M' for month)
+                Defaults to None.
+            min_proximity (dict | None): Minimum time gap between sequential peaks.
+                Dict with keys {'units': <pandas timedelta unit str>, 'val': <numeric>}.
+                Example: {'units': 'D', 'val': 1} enforces 1-day minimum gap.
+                Raises ValueError if violated. Defaults to None (no constraint).
+            peak_range (dict, optional): Daily time window for peak detection. Dict with keys:
+                - 'start': HH:MM:SS string (inclusive)
+                - 'end': HH:MM:SS string (exclusive)
+                Defaults to full day.
+
+        Returns:
+            pd.DataFrame: Input demand_profile with added 'is_peak' boolean column.
+                Each row is True if that timestep is a peak, False otherwise.
+
+        Raises:
+            ValueError: If configuration is invalid (bad period frequency, type mismatches, etc.)
+        """
+
+        if not isinstance(demand_profile, dict):
+            raise ValueError("demand_profile must be a dict with 'date_time' and 'demand' keys")
+
+        peak_range = PeakLoadManagementHeuristicOpenLoopStorageController._parse_peak_range(
+            peak_range
+        )
+
+        demand_df = pd.DataFrame(demand_profile)
+        if "date_time" not in demand_df or "demand" not in demand_df:
+            raise ValueError("demand_profile must include 'date_time' and 'demand' keys")
+
+        # Normalize timestamps and tag by day
+        demand_df["date_time"] = pd.to_datetime(demand_df["date_time"])
+        demand_df["period_day"] = demand_df["date_time"].dt.floor("D")
+
+        # Validate and apply time-of-day window
+        time_of_day = demand_df["date_time"].dt.time
+        if peak_range["start"] <= peak_range["end"]:
+            # Normal window: 12:00-17:00
+            in_peak_range = (time_of_day >= peak_range["start"]) & (
+                time_of_day <= peak_range["end"]
+            )
+        else:
+            raise ValueError("Peak range start must come before peak range end in the same day")
+
+        # Identify highest-demand timestep within each day's peak window
+        demand_df["is_peak"] = False
+        daily_peak_idx = demand_df.loc[in_peak_range].groupby("period_day")["demand"].idxmax()
+        demand_df.loc[daily_peak_idx, "is_peak"] = True
+
+        # Optional: Limit number of peaks globally or per period
+        if n_override_events is not None:
+            if n_override_events < 0:
+                raise ValueError("n_override_events must be >= 0 or None")
+
+            peak_candidates = demand_df.loc[demand_df["is_peak"]].copy()
+            keep_idx = []
+
+            if override_events_period is None:
+                # Global limit: keep the N largest peaks across all time
+                keep_idx = peak_candidates.nlargest(n_override_events, "demand").index.tolist()
+            else:
+                # Period-based limit: keep top-N peaks within each period
+                if isinstance(override_events_period, int):
+                    if override_events_period <= 0:
+                        raise ValueError(
+                            "override_events_period must be positive when provided as an int"
+                        )
+
+                    # Group by timestep intervals (e.g., 288 timesteps = 1 day)
+                    demand_df["period_id"] = np.arange(len(demand_df)) // override_events_period
+                    peak_candidates["period_id"] = demand_df.loc[peak_candidates.index, "period_id"]
+
+                elif isinstance(override_events_period, str):
+                    # Group by pandas period frequency (W=week, M=month, etc.)
+                    period_freq = override_events_period.strip()
+                    try:
+                        demand_df["period_id"] = demand_df["date_time"].dt.to_period(period_freq)
+                    except ValueError as exc:
+                        raise ValueError(
+                            "Invalid override_events_period string. Use a pandas period frequency "
+                            "(for example 'Y', 'Q', 'M', 'W', 'D', 'H')."
+                        ) from exc
+
+                    peak_candidates["period_id"] = demand_df.loc[peak_candidates.index, "period_id"]
+                else:
+                    raise ValueError(
+                        "override_events_period must be None, a positive integer, or a pandas "
+                        "period frequency string"
+                    )
+
+                # Within each period, retain only the top-N peaks by demand
+                for _, period_group in peak_candidates.groupby("period_id"):
+                    keep_idx.extend(
+                        period_group.nlargest(n_override_events, "demand").index.tolist()
+                    )
+
+                demand_df = demand_df.drop(columns=["period_id"])
+
+            # Reset "is_peak" flags and reapply only to surviving indices
+            demand_df["is_peak"] = False
+            demand_df.loc[keep_idx, "is_peak"] = True
+
+        # Optional: Validate minimum spacing between consecutive peaks
+        if min_proximity is not None:
+            if not isinstance(min_proximity, dict):
+                raise ValueError("min_proximity must be a dict with keys 'units' and 'val'")
+            if "units" not in min_proximity or "val" not in min_proximity:
+                raise ValueError("min_proximity must include keys 'units' and 'val'")
+
+            units = min_proximity["units"]
+            val = min_proximity["val"]
+            if not isinstance(units, str) or not units.strip():
+                raise ValueError("min_proximity['units'] must be a non-empty string")
+            if not isinstance(val, int | float) or val < 0:
+                raise ValueError("min_proximity['val'] must be a non-negative number")
+
+            # Convert specification to timedelta
+            min_delta = pd.to_timedelta(val, unit=units.strip())
+            if min_delta > pd.Timedelta(0):
+                # Check consecutive peak spacing
+                selected_peaks = demand_df.loc[demand_df["is_peak"], ["date_time", "demand"]]
+                selected_peaks = selected_peaks.sort_values("date_time")
+
+                if len(selected_peaks) > 1:
+                    deltas = selected_peaks["date_time"].diff().dropna()
+                    if (deltas < min_delta).any():
+                        raise ValueError(
+                            "Selected peaks violate min_proximity. "
+                            "Increase spacing between events or relax min_proximity."
+                        )
+
+        return demand_df.drop(columns=["period_day"])
+
+    @staticmethod
+    def merge_peaks(peaks_1, peaks_2):
+        """Merge peaks_1 and peak_2 schedules with peak_1 precedence.
+
+        Combines two peak schedules (primary and fallback) using day-level precedence:
+        - For each day, if the peaks_1 profile has any peaks on that day,
+          use all peaks_1 peaks for that day
+        - Otherwise, use the peaks_2 peaks for that day
+
+        This allows overriding peaks (peaks_1) to take scheduling precedence while
+        falling back to peaks_2 peaks for days with no overriding peaks.
+
+        Args:
+            peaks_1 (pd.DataFrame | None): primary peak schedule with columns
+                ['date_time', 'is_peak', 'demand', ...].
+            peaks_2 (pd.DataFrame): fallback peak schedule with same columns.
+
+        Returns:
+            pd.DataFrame: Merged peak schedule. If peaks_2 is None, returns peaks_1 unchanged.
+                Otherwise, returns peaks_2 with 'is_peak' flags overridden on peak_1 peak
+                days.
+        """
+        if peaks_1 is None:
+            raise (ValueError("Input, peaks_1, must contain a dataframe, but None was given."))
+        elif peaks_2 is None:
+            peaks_df = peaks_1.copy()
+        else:
+            peaks_df = peaks_2.copy()
+            for day in peaks_2["date_time"].dt.floor("D").unique():
+                day_df = peaks_1[peaks_1["date_time"].dt.floor("D") == day]
+                # For each day in the data, check if peaks_1 has any peaks
+                # If peaks_1 has peaks on the day, use peaks_1's flags for all rows that day
+                if any(day_df["is_peak"]):
+                    peaks_df.loc[peaks_df["date_time"].dt.floor("D") == day, "is_peak"] = day_df[
+                        "is_peak"
+                    ]
+
+        return peaks_df
+
+    def get_time_to_peak(self):
+        """Compute time delta from each timestep to the next detected peak.
+
+        For each row in peaks_df, determines how long until the next peak (marked
+        as is_peak=True) will occur. This enables the discharge trigger: when
+        time_to_peak <= advance_discharge_period, discharge mode activates.
+
+        Timesteps after the final peak receive time.max as their time_to_peak value.
+        This default prevents charging at simulation end (since advance_discharge_period
+        will never be reached). TODO: Consider configurable end-of-horizon behavior.
+
+        Side effect: Modifies self.peaks_df by adding/updating 'time_to_peak' column
+        with pd.Timedelta values or time.max.
+        """
+        # Initialize with sentinel value for "no future peak"
+        self.peaks_df["time_to_peak"] = pd.Timedelta(value=24, unit="h")
+        for _i, idx in enumerate(self.peaks_df.index):
+            # Find next peak at or after current index
+            next_peak_time = self.peaks_df.loc[
+                self.peaks_df["is_peak"] & (self.peaks_df.index >= idx), "date_time"
+            ]
+            if len(next_peak_time) > 0:
+                next_peak_time = next_peak_time.iloc[0]
+                self.peaks_df.loc[idx, "time_to_peak"] = (
+                    next_peak_time - self.peaks_df.loc[idx, "date_time"]
+                )
+
+    def get_allowed_charge(self):
+        """Compute allowed charging time windows based on peak range configuration.
+
+        Determines for each timestep whether charging is permitted. If
+        allow_charge_in_peak_range=True, charging is allowed at all times.
+        Otherwise, charging is suppressed during the configured peak_range window
+        (e.g., 12:00-17:00 each day) to prioritize meeting peak demand from storage.
+
+        Side effect: Modifies self.peaks_df by adding/updating 'allow_charge' column
+        with boolean values (True=charging allowed, False=charging suppressed).
+        """
+        if self.config.allow_charge_in_peak_range:
+            # Global allow: charging always permitted
+            self.peaks_df["allow_charge"] = True
+        else:
+            peak_range = self._parse_peak_range(self.config.peak_range)
+            # Selective allow: suppress charging during peak window only
+            self.peaks_df["allow_charge"] = False
+            for i in range(self.n_timesteps):
+                time_of_day = self.peaks_df["date_time"].iloc[i].time()
+                # Allow charging if outside peak window
+                if time_of_day < peak_range["start"] or time_of_day >= peak_range["end"]:
+                    self.peaks_df.loc[i, "allow_charge"] = True
diff --git a/h2integrate/control/control_strategies/storage/test/data/lmp_month_1.csv b/h2integrate/control/control_strategies/storage/test/data/lmp_month_1.csv
new file mode 100644
index 000000000..3a00a1738
--- /dev/null
+++ b/h2integrate/control/control_strategies/storage/test/data/lmp_month_1.csv
@@ -0,0 +1,8893 @@
+,time_mountain,energy
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+8965,2024-01-31 23:05:00-07:00,16.7548
+8966,2024-01-31 23:10:00-07:00,14.067
+8967,2024-01-31 23:15:00-07:00,1.9304
+8968,2024-01-31 23:20:00-07:00,12.8156
+8969,2024-01-31 23:25:00-07:00,14.6573
+8970,2024-01-31 23:30:00-07:00,13.6748
+8971,2024-01-31 23:35:00-07:00,17.898
+8972,2024-01-31 23:40:00-07:00,18.1068
+8973,2024-01-31 23:45:00-07:00,12.9344
+8974,2024-01-31 23:50:00-07:00,15.3492
+8975,2024-01-31 23:55:00-07:00,1.8365
diff --git a/h2integrate/control/control_strategies/storage/test/data/lmp_peaks_month_1.csv b/h2integrate/control/control_strategies/storage/test/data/lmp_peaks_month_1.csv
new file mode 100644
index 000000000..4563a3f1d
--- /dev/null
+++ b/h2integrate/control/control_strategies/storage/test/data/lmp_peaks_month_1.csv
@@ -0,0 +1,11 @@
+,time_mountain,energy
+0,2024-01-03 16:00:00-07:00,680.9031
+1,2024-01-04 08:45:00-07:00,678.7013
+2,2024-01-06 16:45:00-07:00,666.9504
+3,2024-01-07 15:55:00-07:00,694.5076
+4,2024-01-11 21:00:00-07:00,588.4286
+5,2024-01-13 21:15:00-07:00,606.7448
+6,2024-01-17 06:10:00-07:00,705.74
+7,2024-01-19 08:40:00-07:00,649.1205
+8,2024-01-21 16:50:00-07:00,655.7059
+9,2024-01-30 17:55:00-07:00,665.9357
diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_openloop_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_openloop_storage_controller.py
new file mode 100644
index 000000000..492510c3c
--- /dev/null
+++ b/h2integrate/control/control_strategies/storage/test/test_plm_openloop_storage_controller.py
@@ -0,0 +1,754 @@
+from types import SimpleNamespace
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+import openmdao.api as om
+from pytest import fixture
+
+from h2integrate.storage.storage_performance_model import StoragePerformanceModel
+from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import (
+    PeakLoadManagementHeuristicOpenLoopStorageController,
+)
+
+
+@fixture
+def tech_config_base():
+    """Base technology configuration fixture for storage controller tests."""
+    tech_config_dict = {
+        "name": "technology_config",
+        "description": "This hybrid plant produces hydrogen",
+        "technologies": {
+            "h2_storage": {
+                "control_strategy": {
+                    "model": "SimpleStorageOpenLoopController",
+                },
+                "performance_model": {
+                    "model": "StoragePerformanceModel",
+                },
+                "model_inputs": {
+                    "shared_parameters": {
+                        "commodity": "hydrogen",
+                        "commodity_rate_units": "kg/h",
+                    },
+                },
+            },
+        },
+    }
+
+    return tech_config_dict
+
+
+@fixture
+def plant_config_base():
+    plant_config = {
+        "plant": {
+            "plant_life": 30,
+            "simulation": {
+                "n_timesteps": 24,
+                "dt": 3600,
+                "timezone": 0,
+                "start_time": "01/01/2000 00:00:00",
+            },
+        }
+    }
+
+    return plant_config
+
+
+def _controller_without_setup():
+    """Create a controller instance for testing pure helper methods."""
+    return object.__new__(PeakLoadManagementHeuristicOpenLoopStorageController)
+
+
+@pytest.mark.unit
+def test_get_peaks_daily_expected_peaks():
+    controller = _controller_without_setup()
+
+    demand_profile = {
+        "date_time": pd.date_range("2025-01-01", periods=8, freq="6h"),
+        "demand": [1.0, 9.0, 3.0, 2.0, 4.0, 5.0, 2.0, 8.0],
+    }
+
+    expected_peak_times = [
+        pd.Timestamp("2025-01-01 06:00:00"),
+        pd.Timestamp("2025-01-02 18:00:00"),
+    ]
+
+    peaks = controller.get_peaks(demand_profile)
+    actual_peak_times = peaks.loc[peaks["is_peak"], "date_time"].tolist()
+
+    assert actual_peak_times == expected_peak_times
+
+
+@pytest.mark.unit
+def test_get_peaks_with_global_event_limit_expected_peak():
+    controller = _controller_without_setup()
+
+    demand_profile = {
+        "date_time": pd.date_range("2025-01-01", periods=8, freq="6h"),
+        "demand": [1.0, 9.0, 3.0, 2.0, 4.0, 5.0, 2.0, 8.0],
+    }
+
+    expected_peak_times = [pd.Timestamp("2025-01-01 06:00:00")]
+
+    peaks = controller.get_peaks(demand_profile, n_override_events=1, override_events_period=None)
+    actual_peak_times = peaks.loc[peaks["is_peak"], "date_time"].tolist()
+
+    assert actual_peak_times == expected_peak_times
+
+
+@pytest.mark.unit
+def test_get_peaks_with_month_period_expected_peaks():
+    controller = _controller_without_setup()
+
+    demand_profile = {
+        "date_time": [
+            pd.Timestamp("2025-01-01 00:00:00"),
+            pd.Timestamp("2025-01-01 12:00:00"),
+            pd.Timestamp("2025-01-02 00:00:00"),
+            pd.Timestamp("2025-01-02 12:00:00"),
+            pd.Timestamp("2025-02-01 00:00:00"),
+            pd.Timestamp("2025-02-01 12:00:00"),
+            pd.Timestamp("2025-02-02 00:00:00"),
+            pd.Timestamp("2025-02-02 12:00:00"),
+        ],
+        "demand": [
+            5.0,
+            2.0,
+            9.0,
+            3.0,
+            4.0,
+            12.0,
+            8.0,
+            1.0,
+        ],
+    }
+
+    expected_peak_times = [
+        pd.Timestamp("2025-01-02 00:00:00"),
+        pd.Timestamp("2025-02-01 12:00:00"),
+    ]
+
+    peaks = controller.get_peaks(demand_profile, n_override_events=1, override_events_period="M")
+    actual_peak_times = peaks.loc[peaks["is_peak"], "date_time"].tolist()
+
+    assert actual_peak_times == expected_peak_times
+
+
+@pytest.mark.unit
+def test_get_peaks_with_month_period_from_csv_expected_peaks():
+    controller = _controller_without_setup()
+    data_dir = Path(__file__).resolve().parent / "data"
+    demand_profile_df = pd.read_csv(data_dir / "lmp_month_1.csv")
+    expected_peaks_df = pd.read_csv(data_dir / "lmp_peaks_month_1.csv")
+
+    demand_profile = {
+        "date_time": demand_profile_df["time_mountain"].to_list(),
+        "demand": demand_profile_df["energy"].to_list(),
+    }
+
+    expected_peak_times = pd.to_datetime(expected_peaks_df["time_mountain"]).to_list()
+
+    peaks = controller.get_peaks(demand_profile, n_override_events=10, override_events_period="M")
+    actual_peak_times = pd.to_datetime(peaks.loc[peaks["is_peak"], "date_time"]).tolist()
+
+    assert actual_peak_times == expected_peak_times
+
+
+@pytest.mark.unit
+def test_get_peaks_invalid_period_string_raises():
+    controller = _controller_without_setup()
+
+    demand_profile = {
+        "date_time": pd.date_range("2025-01-01", periods=4, freq="6h"),
+        "demand": [1.0, 2.0, 3.0, 4.0],
+    }
+
+    with pytest.raises(ValueError, match="Invalid override_events_period string"):
+        controller.get_peaks(
+            demand_profile, n_override_events=1, override_events_period="not_a_period"
+        )
+
+
+@pytest.mark.unit
+def test_get_peaks_respects_peak_range_12pm_to_5pm():
+    controller = _controller_without_setup()
+
+    demand_profile = {
+        "date_time": [
+            pd.Timestamp("2025-01-01 09:00:00"),
+            pd.Timestamp("2025-01-01 14:00:00"),
+            pd.Timestamp("2025-01-01 18:00:00"),
+            pd.Timestamp("2025-01-01 22:00:00"),
+            pd.Timestamp("2025-01-02 10:00:00"),
+            pd.Timestamp("2025-01-02 13:00:00"),
+            pd.Timestamp("2025-01-02 20:00:00"),
+            pd.Timestamp("2025-01-02 23:00:00"),
+        ],
+        "demand": [
+            100.0,
+            30.0,
+            40.0,
+            120.0,
+            95.0,
+            50.0,
+            60.0,
+            110.0,
+        ],
+    }
+
+    peaks = controller.get_peaks(
+        demand_profile,
+        peak_range={"start": "12:00:00", "end": "17:00:00"},
+    )
+    actual_peak_times = peaks.loc[peaks["is_peak"], "date_time"].tolist()
+
+    expected_peak_times = [
+        pd.Timestamp("2025-01-01 14:00:00"),
+        pd.Timestamp("2025-01-02 13:00:00"),
+    ]
+
+    assert actual_peak_times == expected_peak_times
+
+
+@pytest.mark.unit
+def test_get_peaks_rejects_non_string_peak_range_values():
+    controller = _controller_without_setup()
+
+    demand_profile = {
+        "date_time": pd.date_range("2025-01-01", periods=4, freq="6h"),
+        "demand": [1.0, 9.0, 3.0, 2.0],
+    }
+
+    with pytest.raises(ValueError, match="HH:MM:SS string"):
+        controller.get_peaks(
+            demand_profile,
+            peak_range={"start": pd.Timestamp("2025-01-01 12:00:00").time(), "end": "17:00:00"},
+        )
+
+
+@pytest.mark.unit
+def test_get_peaks_invalid_min_proximity_raises():
+    controller = _controller_without_setup()
+
+    demand_profile = {
+        "date_time": pd.date_range("2025-01-01", periods=10, freq="6h"),
+        "demand": [1.0, 2.0, 4.0, 3.0, 3.0, 4.0, 3.0, 2.0, 1.0, 2.0],
+    }
+
+    with pytest.raises(ValueError, match="Selected peaks violate min_proximity."):
+        controller.get_peaks(
+            demand_profile,
+            n_override_events=2,
+            override_events_period="W",
+            min_proximity={"units": "D", "val": 1},
+        )
+
+
+@pytest.mark.unit
+def test_merge_peaks_with_single_demand_profile_returns_correct_peaks_flags(subtests):
+    peaks_1_df = pd.DataFrame(
+        {
+            "date_time": pd.to_datetime(
+                [
+                    "2025-01-01 14:00:00",
+                    "2025-01-01 18:00:00",
+                    "2025-01-02 13:00:00",
+                    "2025-01-02 20:00:00",
+                ]
+            ),
+            "is_peak": [False, True, False, True],
+            "demand": [1.0, 5.0, 1.0, 6.0],
+        }
+    )
+
+    merged = PeakLoadManagementHeuristicOpenLoopStorageController.merge_peaks(peaks_1_df, None)
+
+    with subtests.test("peak flags unchanged"):
+        assert merged["is_peak"].tolist() == peaks_1_df["is_peak"].tolist()
+
+
+@pytest.mark.unit
+def test_merge_peaks_profile2_takes_precedence_on_same_day(subtests):
+    peaks_1_df = pd.DataFrame(
+        {
+            "date_time": pd.to_datetime(
+                [
+                    "2025-01-01 14:00:00",
+                    "2025-01-01 18:00:00",
+                    "2025-01-02 13:00:00",
+                    "2025-01-02 20:00:00",
+                ]
+            ),
+            "is_peak": [False, True, False, True],
+            "demand": [1.0, 5.0, 1.0, 6.0],
+        }
+    )
+    peaks_2_df = pd.DataFrame(
+        {
+            "date_time": pd.to_datetime(
+                [
+                    "2025-01-01 14:00:00",
+                    "2025-01-01 18:00:00",
+                    "2025-01-02 13:00:00",
+                    "2025-01-02 20:00:00",
+                ]
+            ),
+            "is_peak": [True, False, False, False],
+            "demand": [9.0, 4.0, 6.0, 4.0],
+        }
+    )
+
+    merged = PeakLoadManagementHeuristicOpenLoopStorageController.merge_peaks(
+        peaks_2_df,
+        peaks_1_df,
+    )
+
+    with subtests.test("day1 follows peaks_2 flags"):
+        np.testing.assert_array_equal(merged["is_peak"].iloc[0:2], peaks_2_df["is_peak"].iloc[0:2])
+
+    with subtests.test("day2 follows peaks_1 flags"):
+        np.testing.assert_array_equal(
+            merged["is_peak"].iloc[2:4],
+            peaks_1_df["is_peak"].iloc[2:4],
+        )
+
+
+@pytest.mark.unit
+def test_get_time_to_peak_single_peak(subtests):
+    """Time-to-peak counted down from each row toward the one True is_peak entry."""
+    controller = _controller_without_setup()
+    controller.n_timesteps = 4
+    times = pd.to_datetime(
+        [
+            "2025-01-01 12:00:00",
+            "2025-01-01 14:00:00",
+            "2025-01-01 16:00:00",  # peak
+            "2025-01-01 18:00:00",
+        ]
+    )
+    controller.peaks_df = pd.DataFrame(
+        {
+            "date_time": times,
+            "is_peak": [False, False, True, False],
+            "demand": [1.0, 2.0, 5.0, 3.0],
+        }
+    )
+
+    controller.get_time_to_peak()
+
+    with subtests.test("four hours before peak"):
+        assert controller.peaks_df["time_to_peak"].iloc[0] == pd.Timedelta(hours=4)
+    with subtests.test("two hours before peak"):
+        assert controller.peaks_df["time_to_peak"].iloc[1] == pd.Timedelta(hours=2)
+    with subtests.test("zero at peak"):
+        assert controller.peaks_df["time_to_peak"].iloc[2] == pd.Timedelta(0)
+
+
+@pytest.mark.unit
+def test_get_time_to_peak_multiple_peaks(subtests):
+    """Each row resolves to the *next upcoming* peak, not a later one."""
+    controller = _controller_without_setup()
+    controller.n_timesteps = 5
+    times = pd.to_datetime(
+        [
+            "2025-01-01 08:00:00",
+            "2025-01-01 10:00:00",  # first peak
+            "2025-01-01 12:00:00",
+            "2025-01-01 16:00:00",  # second peak
+            "2025-01-01 18:00:00",
+        ]
+    )
+    controller.peaks_df = pd.DataFrame(
+        {
+            "date_time": times,
+            "is_peak": [False, True, False, True, False],
+            "demand": [1.0, 8.0, 2.0, 7.0, 1.0],
+        }
+    )
+
+    controller.get_time_to_peak()
+
+    with subtests.test("before first peak resolves to first peak"):
+        assert controller.peaks_df["time_to_peak"].iloc[0] == pd.Timedelta(hours=2)
+
+    with subtests.test("at first peak is zero"):
+        assert controller.peaks_df["time_to_peak"].iloc[1] == pd.Timedelta(0)
+
+    with subtests.test("between peaks resolves to second peak"):
+        assert controller.peaks_df["time_to_peak"].iloc[2] == pd.Timedelta(hours=4)
+
+    with subtests.test("at second peak is zero"):
+        assert controller.peaks_df["time_to_peak"].iloc[3] == pd.Timedelta(0)
+
+
+def _make_controller_with_config(allow_charge_in_peak_range, peak_range):
+    controller = _controller_without_setup()
+    controller.config = SimpleNamespace(
+        allow_charge_in_peak_range=allow_charge_in_peak_range,
+        peak_range=peak_range,
+    )
+    return controller
+
+
+@pytest.mark.unit
+def test_get_allowed_charge_always_true_when_charge_allowed_in_peak_range():
+    """When allow_charge_in_peak_range=True every row should allow charging."""
+    controller = _make_controller_with_config(
+        allow_charge_in_peak_range=True,
+        peak_range={"start": "12:00:00", "end": "17:00:00"},
+    )
+    controller.peaks_df = pd.DataFrame(
+        {
+            "date_time": pd.to_datetime(
+                [
+                    "2025-01-01 09:00:00",
+                    "2025-01-01 14:00:00",  # inside peak range
+                    "2025-01-01 18:00:00",
+                ]
+            ),
+            "is_peak": [False, True, False],
+            "demand": [1.0, 5.0, 2.0],
+        }
+    )
+    controller.n_timesteps = 3
+
+    controller.get_allowed_charge()
+
+    assert controller.peaks_df["allow_charge"].tolist() == [True, True, True]
+
+
+@pytest.mark.unit
+def test_get_allowed_charge_blocks_charge_inside_peak_range(subtests):
+    """When allow_charge_in_peak_range=False, rows inside the window get allow_charge=False."""
+    controller = _make_controller_with_config(
+        allow_charge_in_peak_range=False,
+        peak_range={"start": "12:00:00", "end": "17:00:00"},
+    )
+    controller.peaks_df = pd.DataFrame(
+        {
+            "date_time": pd.to_datetime(
+                [
+                    "2025-01-01 09:00:00",  # before range  → allow
+                    "2025-01-01 14:00:00",  # inside range  → block
+                    "2025-01-01 16:59:00",  # inside range  → block
+                    "2025-01-01 18:00:00",  # after range   → allow
+                ]
+            ),
+            "is_peak": [False, True, False, False],
+            "demand": [1.0, 5.0, 4.0, 2.0],
+        }
+    )
+    controller.n_timesteps = 4
+
+    controller.get_allowed_charge()
+
+    with subtests.test("before range allows charge"):
+        assert controller.peaks_df["allow_charge"].iloc[0] is np.True_
+    with subtests.test("inside range blocks charge (first)"):
+        assert controller.peaks_df["allow_charge"].iloc[1] is np.False_
+    with subtests.test("inside range blocks charge (second)"):
+        assert controller.peaks_df["allow_charge"].iloc[2] is np.False_
+    with subtests.test("after range allows charge"):
+        assert controller.peaks_df["allow_charge"].iloc[3] is np.True_
+
+
+@pytest.mark.regression
+def test_plm_controller_basic_discharge_before_peak(subtests, tech_config_base, plant_config_base):
+    """Test PLM controller discharges before detected peak and charges after."""
+
+    # Load base tech config
+    tech_config = tech_config_base
+
+    # Configure PLM-specific parameters
+    tech_config["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"] = {
+        "commodity": "hydrogen",
+        "commodity_rate_units": "kg/h",
+        "max_capacity": 100.0,  # kg
+        "max_soc_fraction": 1.0,
+        "min_soc_fraction": 0.1,
+        "init_soc_fraction": 0.9,
+        "max_charge_rate": 20.0,  # kg/time step
+        "max_discharge_rate": 30.0,  # kg/time step
+        "charge_equals_discharge": False,
+        "charge_efficiency": 0.95,
+        "discharge_efficiency": 0.95,
+        "demand_profile": np.concatenate(
+            (np.full(10, 10.0), np.full(4, 50.0), np.full(10, 10.0))
+        ),  # Peak at hours 10-14
+        "peak_range": {"start": "10:00:00", "end": "14:00:00"},
+        "advance_discharge_period": {"units": "h", "val": 2},
+        "delay_charge_period": {"units": "h", "val": 1},
+        "allow_charge_in_peak_range": False,
+        "demand_profile_upstream": None,
+        "dispatch_priority_demand_profile": "demand_profile",
+        "min_peak_proximity": {"units": "h", "val": 4},
+    }
+
+    tech_config["technologies"]["h2_storage"]["control_strategy"]["model"] = (
+        "PeakLoadManagementHeuristicOpenLoopStorageController"
+    )
+
+    plant_config = plant_config_base
+
+    # Set up OpenMDAO problem
+    prob = om.Problem()
+
+    prob.model.add_subsystem(
+        name="IVC",
+        subsys=om.IndepVarComp(name="hydrogen_in", val=[30.0] * 24),
+        promotes=["*"],
+    )
+
+    prob.model.add_subsystem(
+        "plm_controller",
+        PeakLoadManagementHeuristicOpenLoopStorageController(
+            plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"]
+        ),
+        promotes=["*"],
+    )
+
+    prob.model.add_subsystem(
+        "storage",
+        StoragePerformanceModel(
+            plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"]
+        ),
+        promotes=["*"],
+    )
+
+    prob.setup()
+    prob.run_model()
+
+    set_point = prob.get_val("hydrogen_set_point", units="kg/h")
+    soc = prob.get_val("SOC", units="unitless")
+
+    with subtests.test("Discharge occurs before peak (hours 8-9)"):
+        # Hours 8-9 are 2 hours before peak at hour 10
+        # We expect discharge (positive set_point)
+        assert set_point[8] > 0 and set_point[9] > 0
+
+    with subtests.test("Peak triggers discharge (hours 10-13)"):
+        # High demand period should be reduced by discharge
+        assert all(set_point[10:13] >= 0)
+
+    with subtests.test("SOC decreases during discharge phase"):
+        # SOC should drop after discharge begins
+        assert soc[8] < soc[7]
+
+    with subtests.test("SOC recovers during charging phase"):
+        # After peak, SOC should increase due to charging
+        assert any(soc[15:] > soc[14])
+
+
+@pytest.mark.regression
+def test_plm_controller_respects_soc_bounds(subtests, tech_config_base, plant_config_base):
+    """Test PLM controller respects min/max SOC constraints."""
+    tech_config = tech_config_base
+
+    tech_config["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"] = {
+        "commodity": "hydrogen",
+        "commodity_rate_units": "kg/h",
+        "max_capacity": 50.0,
+        "max_soc_fraction": 0.95,
+        "min_soc_fraction": 0.15,
+        "init_soc_fraction": 0.5,
+        "max_charge_rate": 15.0,
+        "max_discharge_rate": 15.0,
+        "charge_equals_discharge": True,
+        "charge_efficiency": 0.9,
+        "discharge_efficiency": 0.9,
+        "demand_profile": [5.0] * 12,
+        "peak_range": {"start": "06:00:00", "end": "09:00:00"},
+        "advance_discharge_period": {"units": "h", "val": 1},
+        "delay_charge_period": {"units": "h", "val": 1},
+        "allow_charge_in_peak_range": True,
+        "demand_profile_upstream": None,
+        "dispatch_priority_demand_profile": "demand_profile",
+        "min_peak_proximity": {"units": "h", "val": 4},
+    }
+
+    tech_config["technologies"]["h2_storage"]["control_strategy"]["model"] = (
+        "PeakLoadManagementHeuristicOpenLoopStorageController"
+    )
+
+    plant_config = plant_config_base
+    plant_config["plant"]["simulation"]["n_timesteps"] = 12
+
+    prob = om.Problem()
+
+    prob.model.add_subsystem(
+        name="IVC",
+        subsys=om.IndepVarComp(name="hydrogen_in", val=20, shape=12),
+        promotes=["*"],
+    )
+
+    prob.model.add_subsystem(
+        "plm_controller",
+        PeakLoadManagementHeuristicOpenLoopStorageController(
+            plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"]
+        ),
+        promotes=["*"],
+    )
+
+    prob.model.add_subsystem(
+        "storage",
+        StoragePerformanceModel(
+            plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"]
+        ),
+        promotes=["*"],
+    )
+
+    prob.setup()
+    prob.run_model()
+
+    soc = prob.get_val("SOC", units="unitless")
+    min_soc = 0.15
+    max_soc = 0.95
+
+    with subtests.test("SOC never exceeds maximum"):
+        assert np.all(soc <= max_soc + 1e-6)
+
+    with subtests.test("SOC never falls below minimum"):
+        assert np.all(soc >= min_soc - 1e-6)
+
+
+@pytest.mark.regression
+def test_plm_controller_blocking_charge_in_peak_range(
+    subtests, tech_config_base, plant_config_base
+):
+    """Test PLM controller blocks charging during peak window when configured."""
+    tech_config = tech_config_base
+
+    peak_window_start = "10:00:00"
+    peak_window_end = "14:00:00"
+
+    tech_config["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"] = {
+        "commodity": "hydrogen",
+        "commodity_rate_units": "kg/h",
+        "max_capacity": 80.0,
+        "max_soc_fraction": 1.0,
+        "min_soc_fraction": 0.05,
+        "init_soc_fraction": 0.3,
+        "max_charge_rate": 15.0,
+        "max_discharge_rate": 25.0,
+        "charge_equals_discharge": False,
+        "charge_efficiency": 0.92,
+        "discharge_efficiency": 0.92,
+        "demand_profile": np.full(24, 5.0),
+        "demand_profile_upstream": None,
+        "peak_range": {"start": peak_window_start, "end": peak_window_end},
+        "advance_discharge_period": {"units": "h", "val": 3},
+        "delay_charge_period": {"units": "h", "val": 1},
+        "allow_charge_in_peak_range": False,  # Block charging in peak window
+        "dispatch_priority_demand_profile": "demand_profile",
+        "min_peak_proximity": {"units": "h", "val": 4},
+    }
+
+    tech_config["technologies"]["h2_storage"]["control_strategy"]["model"] = (
+        "PeakLoadManagementHeuristicOpenLoopStorageController"
+    )
+
+    plant_config = plant_config_base
+
+    prob = om.Problem()
+
+    prob.model.add_subsystem(
+        name="IVC",
+        subsys=om.IndepVarComp(name="hydrogen_in", val=40.0, shape=24),
+        promotes=["*"],
+    )
+
+    prob.model.add_subsystem(
+        "plm_controller",
+        PeakLoadManagementHeuristicOpenLoopStorageController(
+            plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"]
+        ),
+        promotes=["*"],
+    )
+
+    prob.model.add_subsystem(
+        "storage",
+        StoragePerformanceModel(
+            plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"]
+        ),
+        promotes=["*"],
+    )
+
+    prob.setup()
+    prob.run_model()
+
+    set_point = prob.get_val("hydrogen_set_point", units="kg/h")
+    soc = prob.get_val("SOC", units="unitless")
+
+    with subtests.test("Controller instantiates and runs without error"):
+        assert len(set_point) == 24
+        assert len(soc) == 24
+
+    with subtests.test("Initial discharge phase occurs before peak window"):
+        # Hours 7-9 are before the peak window (10-14)
+        # Some discharge should occur
+        assert any(set_point[7:10] > 1.0)
+
+    with subtests.test("SOC is lower after peak window than before"):
+        # After discharge and attempted peak meeting, SOC should be lower
+        assert soc[14] < soc[6]
+
+
+@pytest.mark.regression
+def test_plm_controller_warns_when_requested_charge_exceeds_input(
+    tech_config_base, plant_config_base
+):
+    """Warn when controller asks to charge faster than available commodity input."""
+    tech_config = tech_config_base
+
+    tech_config["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"] = {
+        "commodity": "hydrogen",
+        "commodity_rate_units": "kg/h",
+        "max_capacity": 100.0,
+        "max_soc_fraction": 1.0,
+        "min_soc_fraction": 0.1,
+        "init_soc_fraction": 0.2,
+        "max_charge_rate": 25.0,
+        "max_discharge_rate": 25.0,
+        "charge_equals_discharge": False,
+        "charge_efficiency": 0.95,
+        "discharge_efficiency": 0.95,
+        "demand_profile": np.full(24, 1.0),
+        "demand_profile_upstream": None,
+        "peak_range": {"start": "23:00:00", "end": "23:59:59"},
+        "advance_discharge_period": {"units": "h", "val": 1},
+        "delay_charge_period": {"units": "h", "val": 1},
+        "allow_charge_in_peak_range": True,
+        "dispatch_priority_demand_profile": "demand_profile",
+        "min_peak_proximity": {"units": "h", "val": 4},
+    }
+
+    tech_config["technologies"]["h2_storage"]["control_strategy"]["model"] = (
+        "PeakLoadManagementHeuristicOpenLoopStorageController"
+    )
+
+    plant_config = plant_config_base
+
+    prob = om.Problem()
+
+    # Keep available input intentionally low so requested charging exceeds it.
+    prob.model.add_subsystem(
+        name="IVC",
+        subsys=om.IndepVarComp(name="hydrogen_in", val=np.full(24, 1.0), units="kg/h"),
+        promotes=["*"],
+    )
+
+    prob.model.add_subsystem(
+        "plm_controller",
+        PeakLoadManagementHeuristicOpenLoopStorageController(
+            plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"]
+        ),
+        promotes=["*"],
+    )
+
+    prob.setup()
+
+    with pytest.warns(UserWarning, match="WARNING: At time step index 1, requested charging rate"):
+        prob.run_model()
diff --git a/h2integrate/core/inputs/plant_schema.yaml b/h2integrate/core/inputs/plant_schema.yaml
index eabcf4280..1254f65e4 100644
--- a/h2integrate/core/inputs/plant_schema.yaml
+++ b/h2integrate/core/inputs/plant_schema.yaml
@@ -60,6 +60,7 @@ properties:
           timezone:
             type: number
             description: timezone as UTC offset corresponding to start_time
+            default: 0
             minimum: -12
             maximum: 14
         required: [dt, n_timesteps]
diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py
index f57d46a1f..119242242 100644
--- a/h2integrate/core/supported_models.py
+++ b/h2integrate/core/supported_models.py
@@ -163,6 +163,9 @@
 from h2integrate.control.control_strategies.storage.simple_openloop_controller import (
     SimpleStorageOpenLoopController,
 )
+from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import (
+    PeakLoadManagementHeuristicOpenLoopStorageController,
+)
 from h2integrate.control.control_rules.storage.pyomo_storage_rule_min_operating_cost import (
     PyomoRuleStorageMinOperatingCosts,
 )
@@ -278,6 +281,9 @@
     # Control
     "SimpleStorageOpenLoopController": SimpleStorageOpenLoopController,
     "DemandOpenLoopStorageController": DemandOpenLoopStorageController,
+    "PeakLoadManagementHeuristicOpenLoopStorageController": (
+        PeakLoadManagementHeuristicOpenLoopStorageController
+    ),
     "HeuristicLoadFollowingStorageController": HeuristicLoadFollowingStorageController,
     "OptimizedDispatchStorageController": OptimizedDispatchStorageController,
     "GenericDemandComponent": GenericDemandComponent,
diff --git a/h2integrate/core/test/test_utilities.py b/h2integrate/core/test/test_utilities.py
index 62f1ed5ef..92ff8a3a2 100644
--- a/h2integrate/core/test/test_utilities.py
+++ b/h2integrate/core/test/test_utilities.py
@@ -4,12 +4,13 @@
 
 import yaml
 import numpy as np
+import pandas as pd
 import pytest
 import openmdao.api as om
 from attrs import field, define
 
 from h2integrate import ROOT_DIR, EXAMPLE_DIR, RESOURCE_DEFAULT_DIR
-from h2integrate.core.utilities import BaseConfig
+from h2integrate.core.utilities import BaseConfig, build_time_series_from_plant_config
 from h2integrate.core.dict_utils import check_inputs, dict_to_yaml_formatting
 from h2integrate.core.file_utils import get_path, find_file, load_yaml, make_unique_case_name
 from h2integrate.core.supported_models import supported_models
@@ -551,6 +552,34 @@ def traverse_dict(sample_dict):
         load_tech_yaml(inputs / fn)
 
 
+@pytest.mark.unit
+def test_build_time_series_from_plant_config():
+    plant_config = {
+        "plant": {
+            "simulation": {
+                "n_timesteps": 5,
+                "dt": 1800,
+                "start_time": "2025-01-01 06:30:00",
+                "timezone": 0,
+            }
+        }
+    }
+
+    ts = build_time_series_from_plant_config(plant_config)
+
+    expected = pd.to_datetime(
+        [
+            "2025-01-01 06:30:00+00:00",
+            "2025-01-01 07:00:00+00:00",
+            "2025-01-01 07:30:00+00:00",
+            "2025-01-01 08:00:00+00:00",
+            "2025-01-01 08:30:00+00:00",
+        ]
+    ).to_pydatetime()
+
+    assert (ts == expected).all()
+
+
 def create_om_problem(tech_config):
     plant_config_base = {
         "plant": {
diff --git a/h2integrate/core/utilities.py b/h2integrate/core/utilities.py
index 2a6852f82..b1f540573 100644
--- a/h2integrate/core/utilities.py
+++ b/h2integrate/core/utilities.py
@@ -3,6 +3,7 @@
 
 import attrs
 import numpy as np
+import pandas as pd
 from attrs import Attribute, define
 
 
@@ -191,3 +192,75 @@ def attr_filter(inst: Attribute, value: Any) -> bool:
         if value.size == 0:
             return False
     return True
+
+
+def build_time_series_from_plant_config(plant_config):
+    """Build simulation timestamps from the simulation settings in a plant config.
+
+    Extracts ``n_timesteps``, ``dt``, ``timezone``, and ``start_time`` from
+    ``plant_config["plant"]["simulation"]`` and delegates to
+    :func:`build_time_series`.
+
+    Args:
+        plant_config (dict): Plant-level configuration dictionary. Must contain
+            a ``plant.simulation`` sub-dict with the following keys:
+
+            - ``n_timesteps`` (int): Number of simulation timesteps.
+            - ``dt`` (int): Timestep size in seconds.
+            - ``timezone`` (int): UTC offset in integer hours (e.g., ``-6`` for
+              UTC-6).
+            - ``start_time`` (str): Start timestamp parseable by
+              :class:`pandas.Timestamp`.
+
+    Returns:
+        numpy.ndarray: Array of :class:`datetime.datetime` objects of length
+        ``n_timesteps``, equally spaced by ``dt`` seconds, starting at
+        ``start_time`` in the specified timezone.
+    """
+    simulation_cfg = plant_config["plant"]["simulation"]
+    n_timesteps = int(simulation_cfg["n_timesteps"])
+    dt_seconds = int(simulation_cfg["dt"])
+    tz = int(simulation_cfg["timezone"])
+    start_time = simulation_cfg["start_time"]
+
+    return build_time_series(
+        start_time=start_time, dt_seconds=dt_seconds, n_timesteps=n_timesteps, time_zone=tz
+    )
+
+
+def build_time_series(
+    start_time: str,
+    dt_seconds: int,
+    n_timesteps: int,
+    time_zone: int,
+    start_year: int | None = None,
+):
+    """Build an array of evenly spaced timezone-aware datetime objects.
+
+    Constructs a :class:`pandas.DatetimeIndex` of length ``n_timesteps``
+    beginning at ``start_time`` with a fixed frequency of ``dt_seconds``
+    seconds, then converts it to a NumPy array of :class:`datetime.datetime`
+    objects via :meth:`pandas.DatetimeIndex.to_pydatetime`.
+
+    Args:
+        start_time (str): Start timestamp string parseable by
+            :class:`pandas.Timestamp` (e.g., ``"2025-01-01 00:00:00"`` or
+            ``"01-01 00:00:00"`` when ``start_year`` is provided).
+        dt_seconds (int): Timestep duration in seconds (e.g., ``3600`` for
+            hourly, ``1800`` for half-hourly).
+        n_timesteps (int): Number of timestamps to generate.
+        time_zone (int): UTC offset in integer hours applied to the series
+            (e.g., ``-6`` for UTC-6).
+        start_year (int | None, optional): If provided, overrides the year
+            component of ``start_time``. Useful when ``start_time`` omits the
+            year. Defaults to ``None``.
+
+    Returns:
+        numpy.ndarray: Array of :class:`datetime.datetime` objects of length
+        ``n_timesteps``, equally spaced by ``dt_seconds`` seconds.
+    """
+
+    start_timestamp = pd.Timestamp(start_time, tz=time_zone, year=start_year)
+    freq = pd.to_timedelta(dt_seconds, unit="s")
+
+    return pd.date_range(start=start_timestamp, periods=n_timesteps, freq=freq).to_pydatetime()
diff --git a/h2integrate/core/validators.py b/h2integrate/core/validators.py
index 6fcf0b763..27bb59187 100644
--- a/h2integrate/core/validators.py
+++ b/h2integrate/core/validators.py
@@ -47,6 +47,33 @@ def validator(instance, attribute, value):
     return validator
 
 
+def has_required_keys(required_keys):
+    """Validates that a value is a dict containing all required keys.
+
+    Args:
+        required_keys (list[str] | tuple[str, ...]): Keys that must be present
+            in the input dictionary.
+    """
+
+    required_keys = tuple(required_keys)
+
+    def validator(instance, attribute, value):
+        if not isinstance(value, dict):
+            raise ValueError(
+                f"{attribute.name} must be a dict containing keys {required_keys}, "
+                f"got {type(value).__name__}."
+            )
+
+        missing_keys = [key for key in required_keys if key not in value]
+        if missing_keys:
+            raise ValueError(
+                f"{attribute.name} is missing required key(s): {missing_keys}. "
+                f"Expected keys include: {required_keys}."
+            )
+
+    return validator
+
+
 def must_equal(required_value):
     """Validates that an item equals a specific value"""