diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9743b9340..6f93d54a5 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/.*|examples/33_peak_load_management/demand_profiles/.*|examples/34_plm_optimized_dispatch/demand_profiles/.*)$
+        exclude: ^(h2integrate/core/test/inputs/.*|examples/11_hybrid_energy_plant/tech_inputs/desired_schedules/.*|library/demand_profiles/.*|examples/34_plm_optimized_dispatch/demand_profiles/.*)$
   - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
     rev: v0.8.1
diff --git a/CHANGELOG.md b/CHANGELOG.md
index d157ee9d2..7ef2812ad 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -78,6 +78,7 @@
 - Corrected timestamps in OpenMeteo resource downloads when resource data is downloaded in local time [PR #814](https://github.com/NatLabRockies/H2Integrate/pull/814)
 - Added a thermal-nuclear (light-water reactor) model and a high-temperature steam electrolysis model. [PR 807](https://github.com/NatLabRockies/H2Integrate/pull/807)
 - Fixed docs build warnings by correcting inline-literal docstring markup etc, also enabled warning-as-error in the shared docs build script to minimize number of future warnings. [PR 821](https://github.com/NatLabRockies/H2Integrate/pull/821)
+- Added a converter controller that implements a peak-load management heuristic. [PR 773](https://github.com/NatLabRockies/H2Integrate/pull/773)
 
 ## 0.8 [April 15, 2026]
 
diff --git a/docs/_static/class_hierarchy.html b/docs/_static/class_hierarchy.html
index f40a21d07..c9e13fdf5 100644
--- a/docs/_static/class_hierarchy.html
+++ b/docs/_static/class_hierarchy.html
@@ -380,8 +380,8 @@ <h1></h1>
 
 
                   // parsing and collecting nodes and edges from the python
-                  nodes = new vis.DataSet([{"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoRuleBaseClass", "label": "PyomoRuleBaseClass", "shape": "hexagon", "size": 19.42105263157895, "title": "PyomoRuleBaseClass\ncontrol/control_rules/pyomo_rule_baseclass.py\n[Control / General]", "x": 654.0, "y": 0.0}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoDispatchGenericConverter", "label": "PyomoDispatchGenericConverter", "shape": "dot", "size": 18.0, "title": "PyomoDispatchGenericConverter\ncontrol/control_rules/converters/generic_converter.py\n[Converter / Other]", "x": 428.09388111524015, "y": 469.0988894808179}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoRuleStorageBaseclass", "label": "PyomoRuleStorageBaseclass", "shape": "diamond", "size": 18.0, "title": "PyomoRuleStorageBaseclass\ncontrol/control_rules/storage/pyomo_storage_rule_baseclass.py\n[Storage / General]", "x": 428.09388111524004, "y": -469.09888948081795}, {"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoStorageControllerBaseClass", "label": "PyomoStorageControllerBaseClass", "shape": "diamond", "size": 20.13157894736842, "title": "PyomoStorageControllerBaseClass\ncontrol/control_strategies/pyomo_storage_controller_baseclass.py\n[Storage / General]", "x": 455.6085661088584, "y": -385.16822684557377}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DemandOpenLoopStorageController", "label": "DemandOpenLoopStorageController", "shape": "diamond", "size": 18.0, "title": "DemandOpenLoopStorageController\ncontrol/control_strategies/storage/demand_openloop_storage_controller.py\n[Storage / General]", "x": 370.0643470376622, "y": -331.15773226109025}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HeuristicLoadFollowingStorageController", "label": "HeuristicLoadFollowingStorageController", "shape": "diamond", "size": 18.0, "title": "HeuristicLoadFollowingStorageController\ncontrol/control_strategies/storage/heuristic_pyomo_controller.py\n[Storage / General]", "x": 264.590914357365, "y": -368.7926071689721}, {"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "StorageOpenLoopControlBase", "label": "StorageOpenLoopControlBase", "shape": "diamond", "size": 20.13157894736842, "title": "StorageOpenLoopControlBase\ncontrol/control_strategies/storage/openloop_storage_control_base.py\n[Storage / General]", "x": 219.55784982221226, "y": -478.13520688340736}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OptimizedDispatchStorageController", "label": "OptimizedDispatchStorageController", "shape": "diamond", "size": 18.0, "title": "OptimizedDispatchStorageController\ncontrol/control_strategies/storage/optimized_pyomo_controller.py\n[Storage / General]", "x": 270.16454539792574, "y": -589.4304862347785}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PeakLoadManagementHeuristicOpenLoopStorageController", "label": "PeakLoadManagementHeuristicOpenLoopStorageController", "shape": "diamond", "size": 18.0, "title": "PeakLoadManagementHeuristicOpenLoopStorageController\ncontrol/control_strategies/storage/plm_openloop_storage_controller.py\n[Storage / General]", "x": 388.2687164324305, "y": -630.4775561122241}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PeakLoadManagementOptimizedStorageController", "label": "PeakLoadManagementOptimizedStorageController", "shape": "diamond", "size": 18.0, "title": "PeakLoadManagementOptimizedStorageController\ncontrol/control_strategies/storage/plm_optimized_storage_controller.py\n[Storage / General]", "x": 501.4805766605486, "y": -572.7844347513467}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleStorageOpenLoopController", "label": "SimpleStorageOpenLoopController", "shape": "diamond", "size": 18.0, "title": "SimpleStorageOpenLoopController\ncontrol/control_strategies/storage/simple_openloop_controller.py\n[Storage / General]", "x": 538.9625776291, "y": -449.75172147563603}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CostMinimizationControl", "label": "CostMinimizationControl", "shape": "hexagon", "size": 18.0, "title": "CostMinimizationControl\ncontrol/control_strategies/system_level/cost_minimization_control.py\n[Control / General]", "x": 681.5146849936184, "y": 83.93066263524416}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DemandFollowingControl", "label": "DemandFollowingControl", "shape": "hexagon", "size": 18.0, "title": "DemandFollowingControl\ncontrol/control_strategies/system_level/demand_following_control.py\n[Control / General]", "x": 595.9704659224221, "y": 137.9411572197277}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ProfitMaximizationControl", "label": "ProfitMaximizationControl", "shape": "hexagon", "size": 18.0, "title": "ProfitMaximizationControl\ncontrol/control_strategies/system_level/profit_maximization_control.py\n[Control / General]", "x": 490.497033242125, "y": 100.30628231184586}, {"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SystemLevelControlBase", "label": "SystemLevelControlBase", "shape": "hexagon", "size": 20.13157894736842, "title": "SystemLevelControlBase\ncontrol/control_strategies/system_level/system_level_control_base.py\n[Control / General]", "x": 445.4639687069722, "y": -9.036317402589397}, {"borderWidth": 4.0, "color": {"background": "#F5C542", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GenericConverterCostModel", "label": "GenericConverterCostModel", "shape": "dot", "size": 18.0, "title": "GenericConverterCostModel\nconverters/generic_converter_cost.py\n[Converter / Other]", "x": 455.6085661088585, "y": 553.0295521160621}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AmmoniaSynLoopCostModel", "label": "AmmoniaSynLoopCostModel", "shape": "dot", "size": 18.0, "title": "AmmoniaSynLoopCostModel\nconverters/ammonia/ammonia_synloop_cost.py\n[Converter / Ammonia]", "x": 370.0643470376623, "y": 607.0400467005456}, {"borderWidth": 3.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AmmoniaSynLoopPerformanceModel", "label": "AmmoniaSynLoopPerformanceModel", "shape": "dot", "size": 18.0, "title": "AmmoniaSynLoopPerformanceModel\nconverters/ammonia/ammonia_synloop_performance.py\n[Converter / Ammonia]", "x": 264.59091435736514, "y": 569.4051717926637}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleAmmoniaPerformanceModel", "label": "SimpleAmmoniaPerformanceModel", "shape": "dot", "size": 18.0, "title": "SimpleAmmoniaPerformanceModel\nconverters/ammonia/simple_ammonia_model.py\n[Converter / Ammonia]", "x": 219.55784982221238, "y": 460.06257207822847}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleAmmoniaCostModel", "label": "SimpleAmmoniaCostModel", "shape": "dot", "size": 18.0, "title": "SimpleAmmoniaCostModel\nconverters/ammonia/simple_ammonia_model.py\n[Converter / Ammonia]", "x": 270.16454539792585, "y": 348.7672927268573}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DOCPerformanceModel", "label": "DOCPerformanceModel", "shape": "dot", "size": 18.0, "title": "DOCPerformanceModel\nconverters/co2/marine/direct_ocean_capture.py\n[Converter / CO2]", "x": 388.2687164324306, "y": 307.72022284941175}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DOCCostModel", "label": "DOCCostModel", "shape": "dot", "size": 18.0, "title": "DOCCostModel\nconverters/co2/marine/direct_ocean_capture.py\n[Converter / CO2]", "x": 501.4805766605487, "y": 365.4133442102892}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OAEPerformanceModel", "label": "OAEPerformanceModel", "shape": "dot", "size": 18.0, "title": "OAEPerformanceModel\nconverters/co2/marine/ocean_alkalinity_enhancement.py\n[Converter / CO2]", "x": 538.9625776291001, "y": 488.44605748599986}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OAECostModel", "label": "OAECostModel", "shape": "dot", "size": 18.0, "title": "OAECostModel\nconverters/co2/marine/ocean_alkalinity_enhancement.py\n[Converter / CO2]", "x": 475.93189116793957, "y": 601.9588898891661}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OAECostAndFinancialModel", "label": "OAECostAndFinancialModel", "shape": "dot", "size": 18.0, "title": "OAECostAndFinancialModel\nconverters/co2/marine/ocean_alkalinity_enhancement.py\n[Converter / CO2]", "x": 349.5705117805884, "y": 635.8507248662497}, {"borderWidth": 4.0, "color": {"background": "#1B3A5C", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GridPerformanceModel", "label": "GridPerformanceModel", "shape": "dot", "size": 18.0, "title": "GridPerformanceModel\nconverters/grid/grid.py\n[Converter / Grid]", "x": 236.61361523178945, "y": 568.2423536759715}, {"borderWidth": 4.0, "color": {"background": "#1B3A5C", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GridCostModel", "label": "GridCostModel", "shape": "dot", "size": 18.0, "title": "GridCostModel\nconverters/grid/grid.py\n[Converter / Grid]", "x": 206.39396475153276, "y": 439.409697663304}, {"borderWidth": 4.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HOPPComponent", "label": "HOPPComponent", "shape": "dot", "size": 18.0, "title": "HOPPComponent\nconverters/hopp/hopp_wrapper.py\n[Converter / HOPP]", "x": 278.165573095184, "y": 327.54056326717114}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "BasicElectrolyzerCostModel", "label": "BasicElectrolyzerCostModel", "shape": "dot", "size": 18.0, "title": "BasicElectrolyzerCostModel\nconverters/hydrogen/basic_cost_model.py\n[Converter / Hydrogen]", "x": 408.929515746538, "y": 301.0719911584519}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CustomElectrolyzerCostModel", "label": "CustomElectrolyzerCostModel", "shape": "dot", "size": 18.0, "title": "CustomElectrolyzerCostModel\nconverters/hydrogen/custom_electrolyzer_cost_model.py\n[Converter / Hydrogen]", "x": 519.3422437620703, "y": 376.741275969993}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ElectrolyzerPerformanceBaseClass", "label": "ElectrolyzerPerformanceBaseClass", "shape": "dot", "size": 19.42105263157895, "title": "ElectrolyzerPerformanceBaseClass\nconverters/hydrogen/electrolyzer_baseclass.py\n[Converter / Hydrogen]", "x": 541.9933368656093, "y": 509.0547616483423}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ElectrolyzerCostBaseClass", "label": "ElectrolyzerCostBaseClass", "shape": "dot", "size": 20.842105263157894, "title": "ElectrolyzerCostBaseClass\nconverters/hydrogen/electrolyzer_baseclass.py\n[Converter / Hydrogen]", "x": 462.61867180963463, "y": 617.7338834509918}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "LinearH2FuelCellPerformanceModel", "label": "LinearH2FuelCellPerformanceModel", "shape": "dot", "size": 18.0, "title": "LinearH2FuelCellPerformanceModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 329.04975630639217, "y": 636.513453478361}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "H2FuelCellCostModel", "label": "H2FuelCellCostModel", "shape": "dot", "size": 18.0, "title": "H2FuelCellCostModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 222.32576716621537, "y": 553.5863417347226}, {"borderWidth": 2.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HTSEPerformanceModel", "label": "HTSEPerformanceModel", "shape": "dot", "size": 18.0, "title": "HTSEPerformanceModel\nconverters/hydrogen/htse_electrolyzer.py\n[Converter / Hydrogen]", "x": 207.46113153897124, "y": 419.00371238109653}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HTSECostModel", "label": "HTSECostModel", "shape": "dot", "size": 18.0, "title": "HTSECostModel\nconverters/hydrogen/htse_electrolyzer.py\n[Converter / Hydrogen]", "x": 293.81016729091453, "y": 314.4201619012048}, {"borderWidth": 2.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ECOElectrolyzerPerformanceModel", "label": "ECOElectrolyzerPerformanceModel", "shape": "dot", "size": 18.710526315789473, "title": "ECOElectrolyzerPerformanceModel\nconverters/hydrogen/pem_electrolyzer.py\n[Converter / Hydrogen]", "x": 429.1980303433203, "y": 303.5048975398027}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SingliticoCostModel", "label": "SingliticoCostModel", 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+                  edges = new vis.DataSet([{"arrows": "to", "from": "SiteBaseComponent", "to": "SiteLocationComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "ResizeablePerformanceModelBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SolarPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "LinearH2FuelCellPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SteamMethaneReformerPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "GeoH2SubsurfacePerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "GeoH2SurfacePerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DesalinationPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleThermalNuclearReactorPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "QuinnNuclearPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "CMUElectricArcFurnaceScrapOnlyPerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "ElectricArcFurnacePlantBasePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "CMUElectricArcFurnaceDRIPerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SteelPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleASUPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "WindPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "WindArdPerformanceCompatibilityComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "MethanolPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "HumbertEwinPerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "IronReductionPlantBasePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "MartinIronMinePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "HOPPComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "PySAMTidalPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "RunOfRiverHydroPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleAmmoniaPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DOCPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "OAEPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "GridPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "NaturalGasPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleGasProducerPerformance"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleGasConsumerPerformance"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "StoragePerformanceBase"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DemandComponentBase"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GenericConverterCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBUtilityPVCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBResComPVCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ElectrolyzerCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "H2FuelCellCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SteamMethaneReformerCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GeoH2SubsurfaceCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GeoH2SurfaceCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "DesalinationCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleThermalNuclearReactorCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "QuinnNuclearCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "CMUElectricArcFurnaceCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ElectricArcFurnacePlantBaseCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SteelCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleASUCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBWindPlantCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "WindArdCostCompatibilityComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "MethanolCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "MartinIronMineCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "IronTransportCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "IronReductionPlantBaseCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "HumbertStinnEwinCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "PySAMMarineCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "RunOfRiverHydroCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "AmmoniaSynLoopCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleAmmoniaCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "DOCCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "OAECostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "OAECostAndFinancialModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GridCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "NaturalGasCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleGasProducerCost"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleGasConsumerCost"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GenericStorageCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "MCHTOLStorageCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "HydrogenStorageBaseCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBBatteryCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "FeedstockCostModel"}, {"arrows": "to", "from": "ResizeablePerformanceModelBaseClass", "to": "ElectrolyzerPerformanceBaseClass"}, {"arrows": "to", "from": "ResizeablePerformanceModelBaseClass", "to": "AmmoniaSynLoopPerformanceModel"}, {"arrows": "to", "from": "CacheBaseClass", "to": "FlorisWindPlantPerformanceModel"}, {"arrows": "to", "from": "CacheBaseClass", "to": "HOPPComponent"}, {"arrows": "to", "from": "SolarPerformanceBaseClass", "to": "PYSAMSolarPlantPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerPerformanceBaseClass", "to": "ECOElectrolyzerPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerPerformanceBaseClass", "to": "HTSEPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "SingliticoCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "BasicElectrolyzerCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "CustomElectrolyzerCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "HTSECostModel"}, {"arrows": "to", "from": "ECOElectrolyzerPerformanceModel", "to": "WOMBATElectrolyzerModel"}, {"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "NaturalGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "StimulatedGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfaceCostBaseClass", "to": "GeoH2SubsurfaceCostModel"}, {"arrows": "to", "from": "GeoH2SurfacePerformanceBaseClass", "to": "AspenGeoH2SurfacePerformanceModel"}, {"arrows": "to", "from": "GeoH2SurfaceCostBaseClass", "to": "AspenGeoH2SurfaceCostModel"}, {"arrows": "to", "from": "DesalinationPerformanceBaseClass", "to": "ReverseOsmosisPerformanceModel"}, {"arrows": "to", "from": "DesalinationCostBaseClass", "to": "ReverseOsmosisCostModel"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBasePerformanceComponent", "to": "HydrogenEAFPlantPerformanceComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBasePerformanceComponent", "to": "NaturalGasEAFPlantPerformanceComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBaseCostComponent", "to": "HydrogenEAFPlantCostComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBaseCostComponent", "to": "NaturalGasEAFPlantCostComponent"}, {"arrows": "to", "from": "SteelPerformanceBaseClass", "to": "SteelPerformanceModel"}, {"arrows": "to", "from": "SteelCostBaseClass", "to": "SteelCostAndFinancialModel"}, {"arrows": "to", "from": "WindPerformanceBaseClass", "to": "PYSAMWindPlantPerformanceModel"}, {"arrows": "to", "from": "WindPerformanceBaseClass", "to": "FlorisWindPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolPerformanceBaseClass", "to": "SMRMethanolPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolPerformanceBaseClass", "to": "CO2HMethanolPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolCostBaseClass", "to": "SMRMethanolPlantCostModel"}, {"arrows": "to", "from": "MethanolCostBaseClass", "to": "CO2HMethanolPlantCostModel"}, {"arrows": "to", "from": "MethanolFinanceBaseClass", "to": "SMRMethanolPlantFinanceModel"}, {"arrows": "to", "from": "MethanolFinanceBaseClass", "to": "CO2HMethanolPlantFinanceModel"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "HydrogenIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "NaturalGasIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "HydrogenIronReductionPlantCostComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "NaturalGasIronReductionPlantCostComponent"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "StoragePerformanceModel"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "StorageAutoSizingModel"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "PySAMBatteryPerformanceModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "LinedRockCavernStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "SaltCavernStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "PipeStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "CompressedGasStorageCostModel"}, {"arrows": "to", "from": "FeedstockCostModel", "to": "EIANaturalGasFeedstockCostModel"}, {"arrows": "to", "from": "ProFastBase", "to": "ProFastLCO"}, {"arrows": "to", "from": "ProFastBase", "to": "ProFastNPV"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "SolarResourceBaseAPIModel"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "WindResourceBaseAPIModel"}, {"arrows": "to", "from": "SolarResourceBaseAPIModel", "to": "OpenMeteoHistoricalSolarResource"}, {"arrows": "to", "from": "SolarResourceBaseAPIModel", "to": "NLRDeveloperAPISolarResourceBase"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "MeteosatPrimeMeridianSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "MeteosatPrimeMeridianTMYSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "Himawari7SolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "Himawari8SolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "HimawariTMYSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESAggregatedSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESConusSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESFullDiscSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESTMYSolarAPI"}, {"arrows": "to", "from": "WindResourceBaseAPIModel", "to": "OpenMeteoHistoricalWindResource"}, {"arrows": "to", "from": "WindResourceBaseAPIModel", "to": "WTKNLRDeveloperAPIWindResource"}, {"arrows": "to", "from": "DemandComponentBase", "to": "GenericDemandComponent"}, {"arrows": "to", "from": "DemandComponentBase", "to": "FlexibleDemandComponent"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "PeakLoadManagementOptimizedStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "HeuristicLoadFollowingStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "OptimizedDispatchStorageController"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "ProfitMaximizationControl"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "DemandFollowingControl"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "CostMinimizationControl"}, {"arrows": "to", "from": "StorageOpenLoopControlBase", "to": "PeakLoadManagementHeuristicOpenLoopConverterController"}, {"arrows": "to", "from": "StorageOpenLoopControlBase", "to": "DemandOpenLoopStorageController"}, {"arrows": "to", "from": "StorageOpenLoopControlBase", "to": "SimpleStorageOpenLoopController"}, {"arrows": "to", "from": "StorageOpenLoopControlBase", "to": "PeakLoadManagementHeuristicOpenLoopStorageController"}, {"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoDispatchGenericConverter"}, {"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoRuleStorageBaseclass"}]);
 
                   nodeColors = {};
                   allNodes = nodes.get({ returnType: "Object" });
diff --git a/docs/control/technology_level_control/open-loop_controllers.md b/docs/control/technology_level_control/open-loop_controllers.md
index 0e82473a2..a7b95ecdb 100644
--- a/docs/control/technology_level_control/open-loop_controllers.md
+++ b/docs/control/technology_level_control/open-loop_controllers.md
@@ -106,6 +106,31 @@ An example output for the first week of a one-year simulation is shown below. Or
 ![](./figures/example_peak_load_dispatch.png)
 
 For an example of how to use the `PeakLoadManagementHeuristicOpenLoopStorageController`, see:
-- `examples/33_peak_load_management/`
+- `examples/33_peak_load_management_heuristics/plm_storage/`
 
 For API details, see the [`PeakLoadManagementHeuristicOpenLoopStorageController` API documentation](../../_autosummary/h2integrate.control.control_strategies.storage.plm_openloop_storage_controller).
+
+## Open-Loop Converter Controllers
+The open-loop converter controllers can be attached as the control strategy in the `tech_config` for converter technologies (e.g., a fuel cell). Unlike the storage controllers, converter controllers do not track a state of charge; they translate a set-point into a `{commodity}_command_value` that is bounded by the converter's rated production. There is currently one converter controller:
+1. [Peak Load Management Open-Loop Converter Controller](#peak-load-management-open-loop-converter-controller) — computes a peak-shaving converter command from one or two demand profiles using simple threshold heuristics.
+
+(peak-load-management-open-loop-converter-controller)=
+### Peak Load Management Open-Loop Converter Controller
+The `PeakLoadManagementHeuristicOpenLoopConverterController` computes a per-timestep converter command that shaves demand peaks using threshold-based heuristics rather than storage state-of-charge dynamics or optimization. It is designed for dispatching a converter (such as a fuel cell) to reduce peak loads using either one or two demand profiles.
+
+The command at each timestep is determined by three limits:
+
+- **A primary set-point cutoff (`demand_profile_peak_cutoff`)** — dispatch is only considered when the local `{commodity}_set_point` exceeds this threshold. The desired dispatch is the amount by which the set-point exceeds the cutoff.
+- **An optional upstream signal and cutoff (`demand_profile_upstream` and `demand_profile_upstream_peak_cutoff`)** — a supervisory or upstream signal that can also trigger or shape dispatch. Its interpretation is controlled by `demand_profile_upstream_kind`:
+  - `"electricity"` (default) — the upstream signal is an upstream demand in commodity-amount units. The command tracks the larger exceedance of the local and upstream profiles.
+  - `"price"` — the upstream signal is a price time series. Dispatch is only enabled on timesteps where the upstream price exceeds `demand_profile_upstream_peak_cutoff`.
+- **A converter capacity ceiling** — the command is clipped so it never exceeds the converter's `rated_{commodity}_production` (read from the performance model) or the instantaneous local demand.
+
+Because the controller reads the performance model's `rated_{commodity}_production` output as its command ceiling, a solver is automatically added to converge the controller/performance-model data cycle within the technology group.
+
+**Configuration parameters**
+
+For an example of how to use the `PeakLoadManagementHeuristicOpenLoopConverterController`, see:
+- `examples/33_peak_load_management_heuristics/plm_converter/`
+
+For API details, see the [`PeakLoadManagementHeuristicOpenLoopConverterController` API documentation](../../_autosummary/h2integrate.control.control_strategies.converters.plm_openloop_converter_controller).
diff --git a/docs/control/technology_level_control/technology_control_overview.md b/docs/control/technology_level_control/technology_control_overview.md
index 79b687b53..fdfd8275c 100644
--- a/docs/control/technology_level_control/technology_control_overview.md
+++ b/docs/control/technology_level_control/technology_control_overview.md
@@ -24,6 +24,7 @@ Supported controllers:
 - [`SimpleStorageOpenLoopController`](#pass-through-controller)
 - [`DemandOpenLoopStorageController`](#demand-open-loop-storage-controller)
 - [`PeakLoadManagementHeuristicOpenLoopStorageController`](#peak-load-management-open-loop-storage-controller)
+- [`PeakLoadManagementHeuristicOpenLoopConverterController`](#peak-load-management-open-loop-converter-controller)
 
 (pyomo-control-framework)=
 ## Pyomo control framework
diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md
index e2cee6f50..45f145a2c 100644
--- a/docs/user_guide/model_overview.md
+++ b/docs/user_guide/model_overview.md
@@ -297,6 +297,10 @@ auto-generated API page.
     + {py:class}`~h2integrate.control.control_rules.storage.pyomo_storage_rule_baseclass.PyomoRuleStorageBaseclass` - Base class defining Pyomo rules for generic commodity storage components.
     + {py:class}`~h2integrate.control.control_rules.storage.pyomo_storage_rule_min_operating_cost.PyomoRuleStorageMinOperatingCosts` - Class defining Pyomo rules for the optimized dispatch for load following for generic commodity storage components.
 
+- `converter_controllers`: converter controllers
+  - controllers:
+    + {py:class}`~h2integrate.control.control_strategies.converters.plm_openloop_converter_controller.PeakLoadManagementHeuristicOpenLoopConverterController` - Open-loop peak-load management controller for converter technologies.
+
 - `storage_controllers`: storage controllers
   - controllers:
     + {py:class}`~h2integrate.control.control_strategies.storage.demand_openloop_storage_controller.DemandOpenLoopStorageController` - A controller that manages commodity flow based on demand and storage constraints.
diff --git a/examples/33_peak_load_management/driver_config.yaml b/examples/33_peak_load_management/driver_config.yaml
deleted file mode 100644
index 0991d8889..000000000
--- a/examples/33_peak_load_management/driver_config.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-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/33_peak_load_management.yaml b/examples/33_peak_load_management_heuristics/plm_converter/33_plm_converter_heuristic.yaml
similarity index 100%
rename from examples/33_peak_load_management/33_peak_load_management.yaml
rename to examples/33_peak_load_management_heuristics/plm_converter/33_plm_converter_heuristic.yaml
diff --git a/examples/33_peak_load_management_heuristics/plm_converter/driver_config.yaml b/examples/33_peak_load_management_heuristics/plm_converter/driver_config.yaml
new file mode 100644
index 000000000..7e3114fe9
--- /dev/null
+++ b/examples/33_peak_load_management_heuristics/plm_converter/driver_config.yaml
@@ -0,0 +1,5 @@
+name: driver_config
+description: Driver configuration for peak load management converter heuristic control
+general:
+  folder_output: outputs
+  create_om_reports: false
diff --git a/examples/33_peak_load_management_heuristics/plm_converter/plant_config.yaml b/examples/33_peak_load_management_heuristics/plm_converter/plant_config.yaml
new file mode 100644
index 000000000..d51f0ffb1
--- /dev/null
+++ b/examples/33_peak_load_management_heuristics/plm_converter/plant_config.yaml
@@ -0,0 +1,13 @@
+name: plant_config
+description: Plant configuration for peak load management converter heuristic control
+plant:
+  plant_life: 30
+  simulation:
+    n_timesteps: 8760
+    dt: 3600
+    timezone: -6
+    start_time: 2025/07/01 00:00:00
+technology_interconnections:
+  - [hydrogen_feedstock, fuel_cell, hydrogen, pipe]
+  - [fuel_cell, electrical_load_demand, [electricity_out, electricity_in]]
+  - [electrical_load_demand, grid_buy, [unmet_electricity_demand_out, electricity_set_point]]
diff --git a/examples/33_peak_load_management_heuristics/plm_converter/run_peak_load_management.py b/examples/33_peak_load_management_heuristics/plm_converter/run_peak_load_management.py
new file mode 100644
index 000000000..44f43bf96
--- /dev/null
+++ b/examples/33_peak_load_management_heuristics/plm_converter/run_peak_load_management.py
@@ -0,0 +1,101 @@
+"""
+Example 33: Peak load management converter heuristic dispatch
+
+This example demonstrates peak load management dispatch open loop control of a converter
+with two demand profiles of interest
+
+In this example, the fuel-cell converter command is determined by a simple open-loop
+threshold heuristic that uses two profiles:
+
+1. demand_profile (local set-point)
+2. demand_profile_upstream (supervisory or upstream signal)
+
+At each time step, dispatch is only considered when either profile exceeds its configured
+cutoff. For electricity-mode upstream control, the commanded dispatch is based on the
+larger exceedance of the two profiles and is then limited by both the instantaneous local
+demand and converter capacity. This creates a peak-shaving command profile for the
+converter without storage state-of-charge dynamics or optimization.
+
+The output figure compares the original local demand, upstream demand, cutoff thresholds,
+converter output, resulting unmet demand, and grid purchases.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from h2integrate import H2IntegrateModel
+from h2integrate.core.utilities import build_time_series_from_plant_config
+
+
+# Create, setup, and run the H2Integrate model
+model = H2IntegrateModel("33_plm_converter_heuristic.yaml")
+
+model.setup()
+# om.n2(model.prob)
+model.run()
+model.post_process()
+
+demand_profile = model.prob.get_val("fuel_cell.electricity_set_point", units="kW")
+# plot the results for the first week
+demand_profile_upstream = np.array(
+    model.technology_config["technologies"]["fuel_cell"]["model_inputs"]["control_parameters"][
+        "demand_profile_upstream"
+    ]
+)
+demand_profile_peak_cutoff = model.technology_config["technologies"]["fuel_cell"]["model_inputs"][
+    "control_parameters"
+]["demand_profile_peak_cutoff"]
+demand_profile_upstream_peak_cutoff = model.technology_config["technologies"]["fuel_cell"][
+    "model_inputs"
+]["control_parameters"]["demand_profile_upstream_peak_cutoff"]
+grid_output = model.prob.get_val("grid_buy.electricity_out", units="MW")
+
+time_series = build_time_series_from_plant_config(model.plant_config)
+
+n_plot = 24 * 7
+time_plot = time_series[:n_plot]
+
+fig, ax = plt.subplots(3, 1, sharex=True, figsize=(10, 5))
+ax[0].plot(time_plot, demand_profile_upstream[:n_plot] * 1e-3, label="Upstream demand")
+ax[0].plot(time_plot, demand_profile[:n_plot] * 1e-3, label="Original demand")
+ax[0].axhline(
+    demand_profile_peak_cutoff * 1e-3, label="Demand peak cutoff", color="k", linestyle="--"
+)
+ax[0].axhline(
+    demand_profile_upstream_peak_cutoff * 1e-3,
+    label="Upstream demand peak cutoff",
+    color="k",
+    linestyle=":",
+)
+ax[0].set(ylabel="Power (MW)", ylim=[-2, 2])
+ax[0].legend(frameon=True, ncol=3)
+
+ax[1].plot(time_plot, demand_profile[:n_plot] * 1e-3, label="Original demand (MW)")
+ax[1].plot(
+    time_plot,
+    model.prob.get_val("fuel_cell.electricity_out", units="MW")[:n_plot],
+    label="fuel_cell dispatch",
+)
+ax[1].set(ylabel="Power (MW)", ylim=[-2, 2])
+ax[1].legend(frameon=False, ncol=2)
+
+ax[2].plot(time_plot, demand_profile[:n_plot] * 1e-3, label="Original demand (MW)")
+ax[2].plot(
+    time_plot,
+    model.prob.get_val("electrical_load_demand.unmet_electricity_demand_out", units="MW")[:n_plot],
+    label="New demand profile",
+)
+ax[2].plot(time_plot, grid_output[:n_plot], label="Grid purchase (MW)", linestyle=":")
+
+ax[2].set(ylabel="Power (MW)", ylim=[-2, 2])
+ax[2].legend(frameon=False, ncol=3)
+ax[2].tick_params(axis="x", labelrotation=90)
+
+for axis in ax:
+    axis.minorticks_on()
+    axis.grid(True, which="major", alpha=0.45, linewidth=0.8)
+    axis.grid(True, which="minor", alpha=0.2, linewidth=0.5)
+
+plt.tight_layout()
+plt.savefig("example_peak_load_dispatch.png", transparent=False)
diff --git a/examples/33_peak_load_management_heuristics/plm_converter/tech_config.yaml b/examples/33_peak_load_management_heuristics/plm_converter/tech_config.yaml
new file mode 100644
index 000000000..0df99c0d6
--- /dev/null
+++ b/examples/33_peak_load_management_heuristics/plm_converter/tech_config.yaml
@@ -0,0 +1,66 @@
+name: technology_config
+description: This plant dispatches a fuel cell to reduce peak demand
+technologies:
+  hydrogen_feedstock:
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: hydrogen
+        commodity_rate_units: kg/h
+      performance_parameters:
+        rated_capacity: 50.0  # kg of H2/hour
+      cost_parameters:
+        cost_year: 2022
+        price: 5.0  # USD/kg H2
+        annual_cost: 0.
+        start_up_cost: 0.0
+  fuel_cell:
+    performance_model:
+      model: LinearH2FuelCellPerformanceModel
+    cost_model:
+      model: H2FuelCellCostModel
+    control_strategy:
+      model: PeakLoadManagementHeuristicOpenLoopConverterController
+    model_inputs:
+      shared_parameters:
+        system_capacity_kw: 1000.0
+      performance_parameters:
+        fuel_cell_efficiency_hhv: 0.50
+        uptime_hours_until_eol: 80000
+      cost_parameters:
+        capex_per_kw: 200.0
+        fixed_opex_per_kw_per_year: 10.0
+        variable_opex_per_kwh: 0.0
+        cost_year: 2018
+      control_parameters:
+        commodity: electricity
+        commodity_rate_units: kW
+        demand_profile: !include demand_profiles/demand_profile.yaml  # found in library/demand_profiles
+        demand_profile_upstream: !include demand_profiles/demand_profile_upstream.yaml  # found in library/demand_profiles
+        demand_profile_peak_cutoff: 400.0
+        demand_profile_upstream_peak_cutoff: 1000.0
+  electrical_load_demand:
+    performance_model:
+      model: GenericDemandComponent
+    model_inputs:
+      performance_parameters:
+        commodity: electricity
+        commodity_rate_units: kW
+        demand_profile: !include demand_profiles/demand_profile.yaml  # found in library/demand_profiles
+  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/33_peak_load_management_heuristics/plm_storage/33_plm_storage_heuristic.yaml b/examples/33_peak_load_management_heuristics/plm_storage/33_plm_storage_heuristic.yaml
new file mode 100644
index 000000000..3b0321e48
--- /dev/null
+++ b/examples/33_peak_load_management_heuristics/plm_storage/33_plm_storage_heuristic.yaml
@@ -0,0 +1,5 @@
+name: H2Integrate_config
+system_summary: Peak load management dispatch
+driver_config: driver_config.yaml
+technology_config: tech_config.yaml
+plant_config: plant_config.yaml
diff --git a/examples/33_peak_load_management_heuristics/plm_storage/driver_config.yaml b/examples/33_peak_load_management_heuristics/plm_storage/driver_config.yaml
new file mode 100644
index 000000000..50e625472
--- /dev/null
+++ b/examples/33_peak_load_management_heuristics/plm_storage/driver_config.yaml
@@ -0,0 +1,5 @@
+name: driver_config
+description: Driver configuration for peak load management storage heuristic control
+general:
+  folder_output: outputs
+  create_om_reports: false
diff --git a/examples/33_peak_load_management/plant_config.yaml b/examples/33_peak_load_management_heuristics/plm_storage/plant_config.yaml
similarity index 86%
rename from examples/33_peak_load_management/plant_config.yaml
rename to examples/33_peak_load_management_heuristics/plm_storage/plant_config.yaml
index 93be1cecf..e9f5e65db 100644
--- a/examples/33_peak_load_management/plant_config.yaml
+++ b/examples/33_peak_load_management_heuristics/plm_storage/plant_config.yaml
@@ -1,5 +1,5 @@
 name: plant_config
-description: Demonstrates multivariable streams with a gas combiner
+description: Plant configuration for peak load management storage heuristic control
 plant:
   plant_life: 30
   simulation:
diff --git a/examples/33_peak_load_management/run_peak_load_management.py b/examples/33_peak_load_management_heuristics/plm_storage/run_peak_load_management.py
similarity index 98%
rename from examples/33_peak_load_management/run_peak_load_management.py
rename to examples/33_peak_load_management_heuristics/plm_storage/run_peak_load_management.py
index 075ec6c61..596540006 100644
--- a/examples/33_peak_load_management/run_peak_load_management.py
+++ b/examples/33_peak_load_management_heuristics/plm_storage/run_peak_load_management.py
@@ -1,5 +1,5 @@
 """
-Example 33: Peak load management dispatch
+Example 33: Peak load management storage heuristic dispatch
 
 This example demonstrates:
 1. Peak load management dispatch open loop control with two demand profiles of interest
@@ -32,7 +32,7 @@
 
 
 # Create, setup, and run the H2Integrate model
-model = H2IntegrateModel("33_peak_load_management.yaml")
+model = H2IntegrateModel("33_plm_storage_heuristic.yaml")
 
 model.setup()
 model.run()
diff --git a/examples/33_peak_load_management/tech_config.yaml b/examples/33_peak_load_management_heuristics/plm_storage/tech_config.yaml
similarity index 91%
rename from examples/33_peak_load_management/tech_config.yaml
rename to examples/33_peak_load_management_heuristics/plm_storage/tech_config.yaml
index e1a9ff88f..7acb20a8b 100644
--- a/examples/33_peak_load_management/tech_config.yaml
+++ b/examples/33_peak_load_management_heuristics/plm_storage/tech_config.yaml
@@ -1,5 +1,5 @@
 name: technology_config
-description: This plant charges a battery from the grid to reduce peak demand
+description: This plant dispatches a battery to reduce peak demand
 technologies:
   battery:
     performance_model:
@@ -21,9 +21,9 @@ technologies:
         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
+        demand_profile: !include demand_profiles/demand_profile.yaml  # found in library/demand_profiles
       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.
+        demand_profile_upstream: !include demand_profiles/demand_profile_upstream.yaml  # found in library/demand_profiles. 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
@@ -52,7 +52,7 @@ technologies:
       performance_parameters:
         commodity: electricity
         commodity_rate_units: kW
-        demand_profile: !include demand_profiles/demand_profile.yaml
+        demand_profile: !include demand_profiles/demand_profile.yaml  # found in library/demand_profiles
   grid_buy:
     performance_model:
       model: GridPerformanceModel
diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml
index 0f0a2df68..bfeee34b8 100644
--- a/examples/34_plm_optimized_dispatch/tech_config.yaml
+++ b/examples/34_plm_optimized_dispatch/tech_config.yaml
@@ -23,7 +23,7 @@ technologies:
         demand_profile: 0.0
       control_parameters:
         system_commodity_interface_limit: 1.0e9
-        supervisory_signal: !include demand_profiles/supervisory_signal.yaml
+        supervisory_signal: !include demand_profiles/supervisory_signal.yaml  # found in library/demand_profiles
         peak_window:
           start: 12:00:00
           end: 20:00:00
diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py
index 025e7719a..23e448f2c 100644
--- a/examples/test/test_all_examples.py
+++ b/examples/test/test_all_examples.py
@@ -2961,12 +2961,13 @@ def test_cmu_eaf_dri_example(subtests, temp_copy_of_example):
 
 @pytest.mark.integration
 @pytest.mark.parametrize(
-    "example_folder,resource_example_folder", [("33_peak_load_management", None)]
+    "example_folder,resource_example_folder",
+    [("33_peak_load_management_heuristics/plm_storage", 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 = H2IntegrateModel(example_folder / "33_plm_storage_heuristic.yaml")
     model.setup()
     model.run()
 
@@ -3005,6 +3006,43 @@ def test_peak_load_management_example(subtests, temp_copy_of_example):
         assert battery_unmet_demand.sum() == pytest.approx(grid_purchase.sum(), rel=1e-3)
 
 
+@pytest.mark.integration
+@pytest.mark.parametrize(
+    "example_folder,resource_example_folder",
+    [("33_peak_load_management_heuristics/plm_converter", None)],
+)
+def test_plm_converter_heuristic_example(subtests, temp_copy_of_example):
+    example_folder = temp_copy_of_example
+
+    model = H2IntegrateModel(example_folder / "33_plm_converter_heuristic.yaml")
+    model.setup()
+    model.run()
+
+    command = model.prob.get_val("fuel_cell.electricity_command_value", units="kW")
+    electricity_out = model.prob.get_val("fuel_cell.electricity_out", units="kW")
+    unmet = 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")
+    rated = model.prob.get_val("fuel_cell.rated_electricity_production", units="kW")
+
+    with subtests.test("Fuel-cell command sum"):
+        assert command.sum() == pytest.approx(778276.0, rel=1e-6)
+
+    with subtests.test("Fuel-cell output matches command"):
+        np.testing.assert_allclose(electricity_out, command, rtol=1e-9)
+
+    with subtests.test("Fuel-cell command max"):
+        assert command.max() == pytest.approx(519.0, rel=1e-6)
+
+    with subtests.test("Unmet demand equals grid purchase"):
+        assert unmet.sum() == pytest.approx(grid_purchase.sum(), rel=1e-9)
+
+    with subtests.test("Rated fuel-cell production"):
+        assert rated[0] == pytest.approx(1000.0, rel=1e-9)
+
+    with subtests.test("Nonzero dispatch timesteps"):
+        assert np.sum(command > 0) == 3648
+
+
 @pytest.mark.integration
 @pytest.mark.parametrize(
     "example_folder,resource_example_folder", [("34_plm_optimized_dispatch", None)]
diff --git a/h2integrate/control/control_strategies/converters/__init__.py b/h2integrate/control/control_strategies/converters/__init__.py
index e69de29bb..88f398739 100644
--- a/h2integrate/control/control_strategies/converters/__init__.py
+++ b/h2integrate/control/control_strategies/converters/__init__.py
@@ -0,0 +1,3 @@
+from h2integrate.control.control_strategies.converters.plm_openloop_converter_controller import (
+    PeakLoadManagementHeuristicOpenLoopConverterController,
+)
diff --git a/h2integrate/control/control_strategies/converters/plm_openloop_converter_controller.py b/h2integrate/control/control_strategies/converters/plm_openloop_converter_controller.py
new file mode 100644
index 000000000..7667a1006
--- /dev/null
+++ b/h2integrate/control/control_strategies/converters/plm_openloop_converter_controller.py
@@ -0,0 +1,189 @@
+import numpy as np
+from attrs import field, define
+
+from h2integrate.core.utilities import merge_shared_inputs
+from h2integrate.core.validators import contains
+from h2integrate.control.control_strategies.storage.openloop_storage_control_base import (
+    StorageOpenLoopControlBase,
+    StorageOpenLoopControlBaseConfig,
+)
+
+
+@define(kw_only=True)
+class PeakLoadManagementHeuristicOpenLoopConverterControllerConfig(
+    StorageOpenLoopControlBaseConfig
+):
+    """
+    Configuration class for the PeakLoadManagementHeuristicOpenLoopConverterController.
+
+    Defines the peak-cutoff heuristics used to compute an open-loop converter
+    command that shaves demand peaks from one or two demand profiles.
+
+    Attributes:
+        demand_profile_peak_cutoff (int | float): Primary set-point threshold used to
+            trigger demand curtailment. Dispatch is only considered when
+            ``<commodity>_set_point`` exceeds this value.
+        demand_profile_upstream (int | float | list | None): Secondary upstream profile
+            used to trigger or shape dispatch decisions. For
+            ``demand_profile_upstream_kind='electricity'`` this is typically an
+            upstream demand signal in commodity amount units. For
+            ``demand_profile_upstream_kind='price'`` this is a price time series.
+        demand_profile_upstream_peak_cutoff (int | float | None): Threshold applied to
+            ``demand_profile_upstream``. Units depend on
+            ``demand_profile_upstream_kind``.
+        demand_profile_upstream_kind (str): Interpretation mode for
+            ``demand_profile_upstream``. One of ``"electricity"`` or ``"price"``.
+            Defaults to ``"electricity"``.
+
+    """
+
+    demand_profile_peak_cutoff: int | float = field()
+    demand_profile_upstream: int | float | list | None = field()
+    demand_profile_upstream_peak_cutoff: int | float | None = field()
+    demand_profile_upstream_kind: str = field(
+        default="electricity", validator=contains(["electricity", "price"])
+    )
+
+    def __attrs_post_init__(self):
+        super().__attrs_post_init__()
+
+
+class PeakLoadManagementHeuristicOpenLoopConverterController(StorageOpenLoopControlBase):
+    """Open-loop peak-load management controller for converter technologies.
+
+    This controller computes a timestep-wise converter command that limits
+    dispatch based on:
+
+    1. A primary set-point peak cutoff
+    2. An optional upstream signal cutoff (electricity demand or price)
+    3. A converter capacity ceiling
+
+    The resulting command profile is written to ``<commodity>_command_value`` and
+    can be consumed by converter performance models.
+    """
+
+    _time_step_bounds = (
+        1e-12,
+        np.inf,
+    )
+
+    # This controller reads the performance model's ``rated_<commodity>_production``
+    # output as its command ceiling, which creates a controller<->performance data
+    # cycle within the technology group. h2integrate_model.py::_process_model() uses
+    # this flag to add a nonlinear solver so the cycle converges.
+    _reads_performance_outputs = True
+
+    def setup(self):
+        """Initialize configuration and register converter-specific OpenMDAO inputs.
+
+        During setup:
+        1. Loads controller configuration from tech_config model inputs
+        2. Registers a rated-production input (auto-connected to the technology's
+           performance-model ``rated_<commodity>_production`` output via promotion)
+        3. Registers an upstream cutoff input with units based on
+           demand_profile_upstream_kind
+        4. Stores the simulation horizon length for use in compute()
+        """
+        self.config = PeakLoadManagementHeuristicOpenLoopConverterControllerConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control"),
+            strict=False,
+            additional_cls_name=self.__class__.__name__,
+        )
+        super().setup()
+
+        self.add_input(
+            f"rated_{self.config.commodity}_production",
+            val=0.0,
+            units=f"{self.config.commodity_rate_units}",
+            desc="Rated production of the technology, used as the converter command ceiling",
+        )
+
+        if self.config.demand_profile_upstream_kind == "price":
+            peak_cutoff_units = f"USD/({self.config.commodity_amount_units})"
+        else:
+            peak_cutoff_units = self.config.commodity_rate_units
+        self.add_input(
+            "demand_profile_upstream_peak_cutoff",
+            val=self.config.demand_profile_upstream_peak_cutoff,
+            units=peak_cutoff_units,
+            desc="demand_profile_upstream_peak_cutoff",
+        )
+
+    def compute(self, inputs, outputs):
+        """Compute converter command profile using configured peak-cutoff heuristics.
+
+        Dispatch logic per timestep:
+
+        - If the primary set-point exceeds demand_profile_peak_cutoff,
+          dispatch may be activated.
+        - For ``demand_profile_upstream_kind='electricity'``, the command tracks
+          the larger of primary and upstream exceedances, while respecting demand
+          and capacity limits.
+        - For ``demand_profile_upstream_kind='price'``, dispatch is only enabled
+          when upstream price exceeds demand_profile_upstream_peak_cutoff.
+
+        The command is clipped to remain between zero and both the instantaneous
+        demand and converter capacity.
+
+        Args:
+            inputs: OpenMDAO input vector containing set-point, upstream cutoff,
+                and rated-production values.
+            outputs: OpenMDAO output vector populated with
+                ``<commodity>_command_value``.
+
+        Raises:
+            ValueError: If demand_profile_upstream_kind is neither
+                ``"electricity"`` nor ``"price"``.
+        """
+        commodity = self.config.commodity
+        demand_profile = inputs[f"{commodity}_set_point"]
+        rated_production = inputs[f"rated_{commodity}_production"][0]
+        demand_profile_peak_cutoff = self.config.demand_profile_peak_cutoff
+        demand_profile_upstream = self.config.demand_profile_upstream
+        demand_profile_upstream_peak_cutoff = inputs["demand_profile_upstream_peak_cutoff"][0]
+        self.command_value = np.zeros(self.n_timesteps)
+
+        if demand_profile_upstream is None:
+            demand_profile_upstream = np.zeros_like(demand_profile)
+        else:
+            demand_profile_upstream = np.asarray(demand_profile_upstream)
+            if demand_profile_upstream.ndim == 0:
+                demand_profile_upstream = np.full_like(
+                    demand_profile, float(demand_profile_upstream)
+                )
+
+        for idx, val in enumerate(demand_profile):
+            val_upstream = demand_profile_upstream[idx]
+            if (
+                val > demand_profile_peak_cutoff
+                or val_upstream > demand_profile_upstream_peak_cutoff
+            ):
+                desired_dispatch = val - demand_profile_peak_cutoff
+
+                if self.config.demand_profile_upstream_kind == "electricity":
+                    desired_dispatch_upstream = val_upstream - demand_profile_upstream_peak_cutoff
+
+                    self.command_value[idx] = min(
+                        max(
+                            max(desired_dispatch, 0),
+                            max(desired_dispatch_upstream, 0),
+                        ),
+                        val,
+                        rated_production,
+                    )
+                elif self.config.demand_profile_upstream_kind == "price":
+                    if val_upstream > demand_profile_upstream_peak_cutoff:
+                        self.command_value[idx] = min(
+                            max(desired_dispatch, 0),
+                            val,
+                            rated_production,
+                        )
+                else:
+                    raise (
+                        ValueError(
+                            f"Invalid demand_profile_upstream_kind \
+                            '{self.config.demand_profile_upstream_kind}'"
+                        )
+                    )
+
+        outputs[f"{commodity}_command_value"] = self.command_value
diff --git a/h2integrate/converters/hydrogen/h2_fuel_cell.py b/h2integrate/converters/hydrogen/h2_fuel_cell.py
index f6288386d..cbbd210ee 100644
--- a/h2integrate/converters/hydrogen/h2_fuel_cell.py
+++ b/h2integrate/converters/hydrogen/h2_fuel_cell.py
@@ -2,7 +2,7 @@
 from attrs import field, define
 
 from h2integrate.core.utilities import BaseConfig, merge_shared_inputs
-from h2integrate.core.validators import gte_zero, range_val
+from h2integrate.core.validators import gt_zero, gte_zero, range_val
 from h2integrate.tools.constants import HHV_H2_MJ_PER_KG
 from h2integrate.core.model_baseclasses import (
     CostModelBaseClass,
@@ -19,10 +19,13 @@ class LinearH2FuelCellPerformanceConfig(BaseConfig):
         system_capacity_kw (float): The capacity of the fuel cell system in kilowatts (kW).
         fuel_cell_efficiency_hhv (float): The higher heating value efficiency of the
             fuel cell (0 <= efficiency <= 1).
+        uptime_hours_until_eol (int): Number of "on" hours until the fuel cell reaches
+            end-of-life (EOL).
     """
 
     system_capacity_kw: float = field(validator=gte_zero)
     fuel_cell_efficiency_hhv: float = field(validator=range_val(0, 1))
+    uptime_hours_until_eol: int = field(validator=gt_zero)
 
 
 class LinearH2FuelCellPerformanceModel(PerformanceModelBaseClass):
@@ -108,7 +111,7 @@ def compute(self, inputs, outputs):
             inputs: OpenMDAO inputs object containing hydrogen_in, fuel cell
                 HHV efficiency, electricity_command_value, and system_capacity.
             outputs: OpenMDAO outputs object for electricity_out,
-                hydrogen_consumed.
+                hydrogen_consumed, and replacement_schedule.
         """
 
         # calculate max input and output
@@ -146,6 +149,45 @@ def compute(self, inputs, outputs):
         # calculate electricity output in kW
         electricity_out_kw = hydrogen_in / kw_to_kgh_h2
 
+        # calculate replacement schedule based on cumulative "on" hours, where "on" hours
+        # are timesteps where electricity output is greater than zero
+        n_hours_eol = self.config.uptime_hours_until_eol
+        on_off_status = np.where(electricity_out_kw > 0, 1, 0)
+
+        # Calculate cumulative hours and find all crossings of n_hours_eol multiples
+        cumsum_hours = np.cumsum(np.tile(on_off_status, self.plant_life))
+
+        # Find all replacement thresholds (n_hours_eol, 2*n_hours_eol, 3*n_hours_eol, ...)
+        max_hours = np.max(cumsum_hours)
+        n_replacements = int(max_hours / n_hours_eol)
+
+        # For each replacement threshold, find the first index where cumsum >= threshold
+        i_replace = []
+        for k in range(1, n_replacements + 1):
+            threshold = k * n_hours_eol
+            # Find first index where cumsum >= threshold
+            idx = np.searchsorted(cumsum_hours, threshold, side="left")
+            if idx < len(cumsum_hours):
+                i_replace.append(idx)
+
+        i_replace = np.array(i_replace) if i_replace else np.array([], dtype=int)
+
+        # Convert indices to years and count replacements per year
+        refurb_schedule = np.zeros(self.plant_life)
+        for idx in i_replace:
+            year = int(np.floor(idx / self.n_timesteps))
+            if 0 <= year < self.plant_life:
+                refurb_schedule[year] += 1
+
+        # The replacement_schedule is the number of refurbishments that occur in each year (0, 1, or
+        # more if several thresholds are crossed within a single year). The portion of capex of the
+        # total capacity that is replaced per refurbishment is given by the users in the tech_config
+        # as replacement_cost_percent.
+        # The replacement_schedule may be used in the finance model if the replacement_cost_percent
+        # is specified in the tech_config under
+        # ['model_inputs']['finance_parameters']['capital_items']['replacement_cost_percent']
+        outputs["replacement_schedule"] = refurb_schedule
+
         # clip the electricity output to the system capacity
         outputs["electricity_out"] = np.minimum(electricity_out_kw, system_capacity)
         outputs["total_electricity_produced"] = np.sum(outputs["electricity_out"]) * (
@@ -165,13 +207,19 @@ def compute(self, inputs, outputs):
 class H2FuelCellCostConfig(CostModelBaseConfig):
     """Configuration class for the hydrogen fuel cell cost model.
 
-    Fields include `system_capacity_kw`, `capex_per_kw`, and `fixed_opex_per_kw_per_year`.
-    The `cost_year` field is inherited from `CostModelBaseConfig`.
+    Attributes:
+        system_capacity_kw (float): The capacity of the fuel cell system in kilowatts (kW).
+        capex_per_kw (float): Capital cost per unit of capacity in USD/kW.
+        fixed_opex_per_kw_per_year (float): Fixed operating expenses per unit of capacity per year
+            in USD/(kW*year).
+        variable_opex_per_kwh (float): Variable operating expenses per unit of electricity
+            produced in USD/(kW*h).
     """
 
     system_capacity_kw: float = field(validator=gte_zero)
     capex_per_kw: float = field(validator=gte_zero)
     fixed_opex_per_kw_per_year: float = field(validator=gte_zero)
+    variable_opex_per_kwh: float = field(validator=gte_zero)
 
 
 class H2FuelCellCostModel(CostModelBaseClass):
@@ -216,13 +264,28 @@ def setup(self):
             desc="Fixed operating expenses per unit capacity per year",
         )
 
+        self.add_input(
+            "unit_varopex",
+            val=self.config.variable_opex_per_kwh,
+            units="USD/(kW*h)",
+            desc="Variable operating expenses per unit of electricity produced",
+        )
+
+        self.add_input(
+            "annual_electricity_produced",
+            val=0.0,
+            shape=self.plant_life,
+            units="(kW*h)/year",
+        )
+
     def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
         """
-        Compute capital and fixed operating costs for the fuel cell system.
+        Compute capital, fixed operating, and variable operating costs for the fuel cell system.
 
         Args:
-            inputs: OpenMDAO inputs object containing system_capacity.
-            outputs: OpenMDAO outputs object for capital_cost and fixed_operating_cost_per_year.
+            inputs: OpenMDAO inputs object containing system_capacity, unit_capex,
+                fixed_opex_per_year, unit_varopex, and annual_electricity_produced.
+            outputs: OpenMDAO outputs object for CapEx, OpEx, and VarOpEx.
         """
 
         system_capacity_kw = inputs["system_capacity"]
@@ -232,3 +295,6 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
 
         # Calculate fixed operating cost per year
         outputs["OpEx"] = system_capacity_kw * inputs["fixed_opex_per_year"]
+
+        # Calculate variable operating cost per year
+        outputs["VarOpEx"] = inputs["annual_electricity_produced"] * inputs["unit_varopex"]
diff --git a/h2integrate/converters/hydrogen/test/test_h2_fuel_cell.py b/h2integrate/converters/hydrogen/test/test_h2_fuel_cell.py
index cfce4935d..e03f03c78 100644
--- a/h2integrate/converters/hydrogen/test/test_h2_fuel_cell.py
+++ b/h2integrate/converters/hydrogen/test/test_h2_fuel_cell.py
@@ -1,3 +1,5 @@
+import copy
+
 import numpy as np
 import pytest
 import openmdao.api as om
@@ -30,6 +32,7 @@ def tech_config():
             "performance_parameters": {
                 "system_capacity_kw": 1000.0,
                 "fuel_cell_efficiency_hhv": 0.50,
+                "uptime_hours_until_eol": (8760 * 7.0),
             }
         }
     }
@@ -44,6 +47,7 @@ def cost_config():
                 "system_capacity_kw": 1000.0,
                 "capex_per_kw": 200.0,
                 "fixed_opex_per_kw_per_year": 10.0,
+                "variable_opex_per_kwh": 5.0,
                 "cost_year": 2018,
             }
         }
@@ -121,6 +125,79 @@ def test_fuel_cell_performance(tech_config, plant_config, subtests):
             == 175230.7542647681
         )
 
+    with subtests.test("refurbishment_schedule"):
+        np.testing.assert_allclose(
+            prob.get_val("fuel_cell.replacement_schedule", units="unitless"),
+            np.tile([0, 0, 0, 0, 0, 0, 1], 15)[:30],
+            atol=1e-6,
+        )
+
+    with subtests.test("refurbishment_schedule_non_int_multiple"):
+        n_timesteps = int(plant_config["plant"]["simulation"]["n_timesteps"])
+        tech_config_copy = copy.deepcopy(tech_config)
+        tech_config_copy["model_inputs"]["performance_parameters"]["uptime_hours_until_eol"] = (
+            8760 * 1.5
+        )
+        prob = om.Problem()
+
+        fuel_cell_2 = LinearH2FuelCellPerformanceModel(
+            plant_config=plant_config, tech_config=tech_config_copy, driver_config={}
+        )
+
+        prob.model.add_subsystem("fuel_cell", fuel_cell_2, promotes=["*"])
+
+        prob.setup()
+
+        hydrogen_input = np.ones(n_timesteps) * 20.0  # kg/h
+        hydrogen_input[0] = (
+            500000000.0  # test case of extreme hydrogen input to check system capacity limit
+        )
+
+        prob.set_val("fuel_cell.hydrogen_in", hydrogen_input, units="kg/h")
+
+        prob.run_model()
+
+        # Check that electricity output is less than or equal to system capacity
+        electricity_output = prob.get_val("fuel_cell.electricity_out", units="kW")
+        np.testing.assert_allclose(
+            prob.get_val("fuel_cell.replacement_schedule", units="unitless"),
+            np.tile([0, 1, 1], 10),
+            atol=1e-6,
+        )
+
+    with subtests.test("refurbishment_schedule_multiple_replacements_in_some_years"):
+        n_timesteps = int(plant_config["plant"]["simulation"]["n_timesteps"])
+        tech_config_copy = tech_config
+        tech_config_copy["model_inputs"]["performance_parameters"]["uptime_hours_until_eol"] = (
+            8760 * 0.75
+        )
+        prob = om.Problem()
+
+        fuel_cell_2 = LinearH2FuelCellPerformanceModel(
+            plant_config=plant_config, tech_config=tech_config_copy, driver_config={}
+        )
+
+        prob.model.add_subsystem("fuel_cell", fuel_cell_2, promotes=["*"])
+
+        prob.setup()
+
+        hydrogen_input = np.ones(n_timesteps) * 20.0  # kg/h
+        hydrogen_input[0] = (
+            500000000.0  # test case of extreme hydrogen input to check system capacity limit
+        )
+
+        prob.set_val("fuel_cell.hydrogen_in", hydrogen_input, units="kg/h")
+
+        prob.run_model()
+
+        # Check that electricity output is less than or equal to system capacity
+        electricity_output = prob.get_val("fuel_cell.electricity_out", units="kW")
+        np.testing.assert_allclose(
+            prob.get_val("fuel_cell.replacement_schedule", units="unitless"),
+            np.tile([1, 1, 2], 10),
+            atol=1e-6,
+        )
+
 
 @pytest.mark.unit
 def test_fuel_cell_demand(tech_config, plant_config, subtests):
@@ -176,15 +253,22 @@ def test_fuel_cell_demand(tech_config, plant_config, subtests):
         )
 
 
-@pytest.mark.regression
+@pytest.mark.unit
 def test_fuel_cell_cost(cost_config, plant_config, subtests):
     int(plant_config["plant"]["simulation"]["n_timesteps"])
 
     prob = om.Problem()
 
+    annual_production = 45  # kwh/year
+
     fuel_cell_cost = H2FuelCellCostModel(
         plant_config=plant_config, tech_config=cost_config, driver_config={}
     )
+    annual_prod_comp = om.IndepVarComp(
+        name="annual_electricity_produced", val=np.full(30, annual_production), units="(kW*h)/yr"
+    )
+
+    prob.model.add_subsystem("IVC1", annual_prod_comp, promotes=["*"])
 
     prob.model.add_subsystem("fuel_cell_cost", fuel_cell_cost, promotes=["*"])
 
@@ -197,3 +281,6 @@ def test_fuel_cell_cost(cost_config, plant_config, subtests):
 
     with subtests.test("opex value"):
         assert prob.get_val("fuel_cell_cost.OpEx", units="USD/year") == 10000.0
+
+    with subtests.test("varopex"):
+        assert prob.get_val("fuel_cell_cost.VarOpEx", units="USD/year")[0] == 45 * 5.0
diff --git a/h2integrate/core/h2integrate_model.py b/h2integrate/core/h2integrate_model.py
index 2a3b49a85..a1d1bfb77 100644
--- a/h2integrate/core/h2integrate_model.py
+++ b/h2integrate/core/h2integrate_model.py
@@ -1066,6 +1066,17 @@ def _process_model(self, model_type, individual_tech_config, tech_group):
             promotes=["*"],
         )
 
+        # Some controllers read performance-model outputs (e.g.
+        # ``rated_<commodity>_production``) as inputs, which creates a
+        # controller<->performance data cycle within the technology group. Add a
+        # nonlinear solver so this coupling is resolved instead of using stale
+        # (default) values on a single execution pass.
+        if model_type == "control_strategy" and getattr(
+            model_object, "_reads_performance_outputs", False
+        ):
+            tech_group.nonlinear_solver = om.NonlinearBlockGS()
+            tech_group.linear_solver = om.DirectSolver()
+
         return om_model_object
 
     def _check_time_step(self, model_name, model_object):
diff --git a/h2integrate/core/model_baseclasses.py b/h2integrate/core/model_baseclasses.py
index 4f1160f53..8dceb7939 100644
--- a/h2integrate/core/model_baseclasses.py
+++ b/h2integrate/core/model_baseclasses.py
@@ -189,7 +189,6 @@ def setup(self):
         hours_per_year = 8760
         hours_simulated = (self.dt / 3600) * self.n_timesteps
         self.fraction_of_year_simulated = hours_simulated / hours_per_year
-
         # Define outputs: CapEx and OpEx costs
         self.add_output("CapEx", val=0.0, units="USD", desc="Capital expenditure")
         self.add_output("OpEx", val=0.0, units="USD/year", desc="Fixed operational expenditure")
diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py
index 51fa624bd..9fc57e81b 100644
--- a/h2integrate/core/supported_models.py
+++ b/h2integrate/core/supported_models.py
@@ -163,6 +163,7 @@ def copy(self):
         "OptimizedDispatchStorageController": "control.control_strategies.storage:OptimizedDispatchStorageController",
         "GenericDemandComponent": "demand:GenericDemandComponent",
         "FlexibleDemandComponent": "demand:FlexibleDemandComponent",
+        "PeakLoadManagementHeuristicOpenLoopConverterController": "control.control_strategies.converters:PeakLoadManagementHeuristicOpenLoopConverterController",
         # Dispatch
         "PyomoDispatchGenericConverter": "control.control_rules.converters:PyomoDispatchGenericConverter",
         "PyomoRuleStorageBaseclass": "control.control_rules.storage:PyomoRuleStorageBaseclass",
diff --git a/examples/33_peak_load_management/demand_profiles/demand_profile.yaml b/library/demand_profiles/demand_profile.yaml
similarity index 84%
rename from examples/33_peak_load_management/demand_profiles/demand_profile.yaml
rename to library/demand_profiles/demand_profile.yaml
index 655da28d6..c3a8378e3 100644
--- a/examples/33_peak_load_management/demand_profiles/demand_profile.yaml
+++ b/library/demand_profiles/demand_profile.yaml
@@ -1,8760 +1,8760 @@
-- 87
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+- 111
+- 93
diff --git a/examples/33_peak_load_management/demand_profiles/demand_profile_upstream.yaml b/library/demand_profiles/demand_profile_upstream.yaml
similarity index 86%
rename from examples/33_peak_load_management/demand_profiles/demand_profile_upstream.yaml
rename to library/demand_profiles/demand_profile_upstream.yaml
index a333bfce9..0af8d87cf 100644
--- a/examples/33_peak_load_management/demand_profiles/demand_profile_upstream.yaml
+++ b/library/demand_profiles/demand_profile_upstream.yaml
@@ -1,8760 +1,8760 @@
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-- 1160
-- 980
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-- 690
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-- 860
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-- 980
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-- 410
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-- 940
-- 760
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-- 690
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-- 760
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-- 1150
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-- 1180
-- 900
-- 410
-- 380
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-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
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-- 1080
-- 900
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-- 540
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-- 980
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-- 860
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-- 980
-- 760
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-- 410
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-- 1140
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-- 940
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-- 690
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-- 380
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-- 670
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-- 1080
-- 1420
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-- 1080
-- 900
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-- 540
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-- 980
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-- 980
-- 760
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-- 410
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-- 940
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-- 540
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-- 690
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-- 1180
-- 900
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-- 380
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-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
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-- 1080
-- 900
-- 740
-- 620
-- 540
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-- 860
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-- 980
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-- 860
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-- 1230
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-- 980
-- 760
-- 470
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-- 410
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-- 1140
-- 1360
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-- 940
-- 760
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-- 540
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-- 690
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-- 760
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-- 980
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-- 410
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-- 390
-- 540
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-- 660
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-- 1150
-- 1230
-- 1290
-- 1330
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-- 1180
-- 900
-- 410
-- 380
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-- 290
-- 360
-- 500
-- 670
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-- 1080
-- 1420
-- 1695
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-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
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-- 860
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-- 1490
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-- 1160
-- 980
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-- 690
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-- 860
-- 1040
-- 1230
-- 1310
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-- 980
-- 760
-- 470
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-- 390
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-- 410
-- 560
-- 740
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-- 1140
-- 1360
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-- 1180
-- 940
-- 760
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-- 540
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-- 690
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-- 670
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-- 1080
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-- 1080
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-- 540
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-- 980
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-- 980
-- 760
-- 470
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-- 410
-- 560
-- 740
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-- 1140
-- 1360
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-- 940
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-- 540
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-- 690
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-- 900
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-- 670
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-- 1080
-- 1420
-- 1695
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-- 1080
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-- 540
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-- 860
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-- 980
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-- 980
-- 760
-- 470
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-- 410
-- 560
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-- 1140
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-- 940
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-- 690
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-- 1150
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-- 1180
-- 900
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-- 380
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-- 360
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-- 670
-- 860
-- 1080
-- 1420
-- 1695
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-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
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-- 860
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-- 1490
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-- 1160
-- 980
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-- 690
-- 640
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-- 860
-- 1040
-- 1230
-- 1310
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-- 980
-- 760
-- 470
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-- 390
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-- 310
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
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-- 1180
-- 940
-- 760
-- 620
-- 540
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-- 430
-- 390
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-- 690
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-- 760
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-- 540
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-- 1150
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-- 1180
-- 900
-- 410
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-- 360
-- 500
-- 670
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-- 1080
-- 1420
-- 1695
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-- 1080
-- 900
-- 740
-- 620
-- 540
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-- 860
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-- 980
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-- 860
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-- 980
-- 760
-- 470
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-- 410
-- 560
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-- 1140
-- 1360
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-- 940
-- 760
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-- 540
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-- 690
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-- 540
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-- 1150
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-- 1180
-- 900
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-- 360
-- 500
-- 670
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-- 1080
-- 1420
-- 1695
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-- 1080
-- 900
-- 740
-- 620
-- 540
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-- 860
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-- 980
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-- 980
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-- 410
-- 560
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-- 1140
-- 1360
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-- 940
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-- 540
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-- 690
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-- 540
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-- 1150
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-- 1180
-- 900
-- 410
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-- 270
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-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
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-- 1080
-- 900
-- 740
-- 620
-- 540
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-- 430
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-- 860
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-- 1490
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-- 1160
-- 980
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-- 690
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-- 860
-- 1040
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-- 980
-- 760
-- 470
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-- 390
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
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-- 1180
-- 940
-- 760
-- 620
-- 540
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-- 390
-- 360
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-- 690
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-- 1785
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-- 1180
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-- 360
-- 500
-- 670
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-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
-- 480
-- 430
-- 620
-- 700
-- 860
-- 1020
-- 1260
-- 1450
-- 1525
-- 1490
-- 1320
-- 1160
-- 980
-- 860
-- 760
-- 690
-- 640
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-- 860
-- 1040
-- 1230
-- 1310
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-- 980
-- 760
-- 470
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-- 390
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
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-- 1440
-- 1180
-- 940
-- 760
-- 620
-- 540
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-- 430
-- 390
-- 360
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-- 520
-- 690
-- 860
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-- 1480
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-- 1110
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-- 760
-- 640
-- 560
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-- 380
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-- 420
-- 650
-- 980
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-- 980
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-- 410
-- 370
-- 330
-- 290
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-- 390
-- 540
-- 710
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-- 1650
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-- 660
-- 560
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-- 290
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-- 820
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-- 1080
-- 1150
-- 1230
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-- 1330
-- 1360
-- 1180
-- 900
-- 410
-- 380
-- 340
-- 300
-- 270
-- 250
-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
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-- 430
-- 620
-- 700
-- 860
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-- 1450
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-- 1490
-- 1320
-- 1160
-- 980
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-- 760
-- 690
-- 640
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-- 860
-- 1040
-- 1230
-- 1310
-- 1200
-- 980
-- 760
-- 470
-- 430
-- 390
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-- 310
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
-- 1550
-- 1710
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-- 1180
-- 940
-- 760
-- 620
-- 540
-- 480
-- 430
-- 390
-- 360
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-- 520
-- 690
-- 860
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-- 1480
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-- 760
-- 640
-- 560
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-- 410
-- 370
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-- 290
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-- 390
-- 540
-- 710
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-- 660
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-- 1150
-- 1230
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-- 1330
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-- 1180
-- 900
-- 410
-- 380
-- 340
-- 300
-- 270
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-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
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-- 430
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-- 860
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-- 1490
-- 1320
-- 1160
-- 980
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-- 690
-- 640
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-- 860
-- 1040
-- 1230
-- 1310
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-- 980
-- 760
-- 470
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-- 390
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-- 310
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
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-- 1180
-- 940
-- 760
-- 620
-- 540
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-- 430
-- 390
-- 360
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-- 690
-- 860
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-- 1480
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-- 1110
-- 900
-- 760
-- 640
-- 560
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-- 460
-- 380
-- 340
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-- 650
-- 980
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-- 410
-- 370
-- 330
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-- 390
-- 540
-- 710
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-- 1490
-- 1650
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-- 820
-- 660
-- 560
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-- 820
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-- 1080
-- 1150
-- 1230
-- 1290
-- 1330
-- 1360
-- 1180
-- 900
-- 410
-- 380
-- 340
-- 300
-- 270
-- 250
-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
-- 480
-- 430
-- 620
-- 700
-- 860
-- 1020
-- 1260
-- 1450
-- 1525
-- 1490
-- 1320
-- 1160
-- 980
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-- 760
-- 690
-- 640
-- 610
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-- 860
-- 1040
-- 1230
-- 1310
-- 1200
-- 980
-- 760
-- 470
-- 430
-- 390
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
-- 1550
-- 1710
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-- 1680
-- 1440
-- 1180
-- 940
-- 760
-- 620
-- 540
-- 480
-- 430
-- 390
-- 360
-- 320
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-- 300
-- 380
-- 520
-- 690
-- 860
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-- 1260
-- 1480
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-- 1110
-- 900
-- 760
-- 640
-- 560
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-- 460
-- 380
-- 340
-- 310
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-- 260
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-- 420
-- 650
-- 980
-- 1475
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-- 980
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-- 720
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-- 1030
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-- 410
-- 370
-- 330
-- 290
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-- 390
-- 540
-- 710
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-- 1490
-- 1650
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-- 1785
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-- 1320
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-- 820
-- 660
-- 560
-- 490
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-- 290
-- 360
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-- 520
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-- 820
-- 910
-- 980
-- 1020
-- 1080
-- 1150
-- 1230
-- 1290
-- 1330
-- 1360
-- 1180
-- 900
-- 410
-- 380
-- 340
-- 300
-- 270
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-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
-- 480
-- 430
-- 620
-- 700
-- 860
-- 1020
-- 1260
-- 1450
-- 1525
-- 1490
-- 1320
-- 1160
-- 980
-- 860
-- 760
-- 690
-- 640
-- 610
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-- 860
-- 1040
-- 1230
-- 1310
-- 1200
-- 980
-- 760
-- 470
-- 430
-- 390
-- 350
-- 310
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
-- 1550
-- 1710
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-- 1680
-- 1440
-- 1180
-- 940
-- 760
-- 620
-- 540
-- 480
-- 430
-- 390
-- 360
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-- 690
-- 860
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-- 1480
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-- 1110
-- 900
-- 760
-- 640
-- 560
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-- 380
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-- 420
-- 650
-- 980
-- 1475
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-- 980
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-- 410
-- 370
-- 330
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-- 390
-- 540
-- 710
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-- 1650
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-- 820
-- 660
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-- 820
-- 910
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-- 1020
-- 1080
-- 1150
-- 1230
-- 1290
-- 1330
-- 1360
-- 1180
-- 900
-- 410
-- 380
-- 340
-- 300
-- 270
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-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
-- 480
-- 430
-- 620
-- 700
-- 860
-- 1020
-- 1260
-- 1450
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-- 1490
-- 1320
-- 1160
-- 980
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-- 760
-- 690
-- 640
-- 610
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-- 860
-- 1040
-- 1230
-- 1310
-- 1200
-- 980
-- 760
-- 470
-- 430
-- 390
-- 350
-- 310
-- 280
-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
-- 1550
-- 1710
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-- 1680
-- 1440
-- 1180
-- 940
-- 760
-- 620
-- 540
-- 480
-- 430
-- 390
-- 360
-- 320
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-- 380
-- 520
-- 690
-- 860
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-- 1480
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-- 1110
-- 900
-- 760
-- 640
-- 560
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-- 460
-- 380
-- 340
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-- 420
-- 650
-- 980
-- 1475
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-- 980
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-- 410
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-- 390
-- 540
-- 710
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-- 1320
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-- 660
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-- 290
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-- 820
-- 910
-- 980
-- 1020
-- 1080
-- 1150
-- 1230
-- 1290
-- 1330
-- 1360
-- 1180
-- 900
-- 410
-- 380
-- 340
-- 300
-- 270
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-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
-- 480
-- 430
-- 620
-- 700
-- 860
-- 1020
-- 1260
-- 1450
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-- 1490
-- 1320
-- 1160
-- 980
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-- 690
-- 640
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-- 860
-- 1040
-- 1230
-- 1310
-- 1200
-- 980
-- 760
-- 470
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-- 390
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-- 310
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
-- 1550
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-- 1680
-- 1440
-- 1180
-- 940
-- 760
-- 620
-- 540
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-- 430
-- 390
-- 360
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-- 690
-- 860
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-- 760
-- 640
-- 560
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-- 380
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-- 420
-- 650
-- 980
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-- 410
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-- 390
-- 540
-- 710
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-- 1650
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-- 1785
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-- 1320
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-- 660
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-- 820
-- 910
-- 980
-- 1020
-- 1080
-- 1150
-- 1230
-- 1290
-- 1330
-- 1360
-- 1180
-- 900
-- 410
-- 380
-- 340
-- 300
-- 270
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-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
-- 480
-- 430
-- 620
-- 700
-- 860
-- 1020
-- 1260
-- 1450
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-- 1490
-- 1320
-- 1160
-- 980
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-- 760
-- 690
-- 640
-- 610
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-- 860
-- 1040
-- 1230
-- 1310
-- 1200
-- 980
-- 760
-- 470
-- 430
-- 390
-- 350
-- 310
-- 280
-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
-- 1550
-- 1710
-- 1800
-- 1680
-- 1440
-- 1180
-- 940
-- 760
-- 620
-- 540
-- 480
-- 430
-- 390
-- 360
-- 320
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-- 300
-- 380
-- 520
-- 690
-- 860
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-- 1480
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-- 1110
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-- 760
-- 640
-- 560
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-- 460
-- 380
-- 340
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-- 420
-- 650
-- 980
-- 1475
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-- 980
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-- 410
-- 370
-- 330
-- 290
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-- 390
-- 540
-- 710
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-- 1310
-- 1490
-- 1650
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-- 1785
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-- 1320
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-- 660
-- 560
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-- 290
-- 360
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-- 820
-- 910
-- 980
-- 1020
-- 1080
-- 1150
-- 1230
-- 1290
-- 1330
-- 1360
-- 1180
-- 900
-- 410
-- 380
-- 340
-- 300
-- 270
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-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
-- 480
-- 430
-- 620
-- 700
-- 860
-- 1020
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-- 1450
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-- 1490
-- 1320
-- 1160
-- 980
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-- 690
-- 640
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-- 860
-- 1040
-- 1230
-- 1310
-- 1200
-- 980
-- 760
-- 470
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-- 390
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-- 310
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
-- 1550
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-- 1180
-- 940
-- 760
-- 620
-- 540
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-- 430
-- 390
-- 360
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-- 380
-- 520
-- 690
-- 860
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-- 760
-- 640
-- 560
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-- 380
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-- 420
-- 650
-- 980
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-- 410
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-- 330
-- 290
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-- 390
-- 540
-- 710
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-- 1310
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-- 1650
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-- 1785
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-- 660
-- 560
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-- 1080
-- 1150
-- 1230
-- 1290
-- 1330
-- 1360
-- 1180
-- 900
-- 410
-- 380
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-- 300
-- 270
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-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
-- 480
-- 430
-- 620
-- 700
-- 860
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-- 1490
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-- 1160
-- 980
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-- 690
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-- 860
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-- 980
-- 760
-- 470
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-- 390
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-- 320
-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
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-- 1180
-- 940
-- 760
-- 620
-- 540
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-- 430
-- 390
-- 360
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-- 690
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-- 540
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-- 660
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-- 1080
-- 1150
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-- 1290
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-- 1180
-- 900
-- 410
-- 380
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-- 300
-- 270
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-- 290
-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
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-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
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-- 430
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-- 700
-- 860
-- 1020
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-- 1450
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-- 1490
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-- 1160
-- 980
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-- 690
-- 640
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-- 860
-- 1040
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-- 1310
-- 1200
-- 980
-- 760
-- 470
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-- 390
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-- 410
-- 560
-- 740
-- 930
-- 1140
-- 1360
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-- 1440
-- 1180
-- 940
-- 760
-- 620
-- 540
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-- 390
-- 360
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-- 690
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-- 540
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-- 1785
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-- 660
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-- 1080
-- 1150
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-- 1290
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-- 1180
-- 900
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-- 270
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-- 360
-- 500
-- 670
-- 860
-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
-- 740
-- 620
-- 540
-- 480
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-- 1080
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-- 980
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-- 940
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-- 1080
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-- 1080
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-- 670
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-- 1080
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-- 1695
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-- 1080
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-- 540
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-- 980
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-- 940
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-- 670
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-- 1080
-- 1420
-- 1695
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-- 1080
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-- 540
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-- 940
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-- 670
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-- 1080
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-- 1080
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-- 980
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-- 670
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-- 1080
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-- 980
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-- 670
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-- 1080
-- 1420
-- 1695
-- 1580
-- 1430
-- 1270
-- 1080
-- 900
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