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VirtualRealityPawn.cpp

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  • Forked from LuFG VR VIS / VR-Group / Unreal-Development / Plugins / RWTH VR Toolkit
    Source project has a limited visibility.
    runme.py 4.17 KiB
    """
    MIT License
    
    Copyright (c) 2023 RWTH Aachen University
    
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:
    
    The above copyright notice and this permission notice shall be included in
    all copies or substantial portions of the Software.
    
    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
    THE SOFTWARE.
    """
    
    from Model_Library.Prosumer.main import ProsumerMain
    from Model_Library.dynamics import TrivialDynamic
    from Tooling.input_profile_processor.input_profile_processor import (
        process_input_profiles,
    )
    
    import json
    import pandas as pd
    
    t_start = pd.Timestamp("2019-05-10 00:00:00")
    dynamic = TrivialDynamic([60 for i in range(24 * 60 - 1)])
    
    input_profile_dict = {
        "bus_1_demand": {"type": "elec_demand", "file": "bus_1_demand.csv"},
        "bus_2_demand": {"type": "elec_demand", "file": "bus_2_demand.csv"},
        "bus_1_recuperation": {"type": "elec_demand", "file": "bus_1_recuperation.csv"},
        "bus_2_recuperation": {"type": "elec_demand", "file": "bus_2_recuperation.csv"},
        "bus_1_connected": {"type": "elec_demand", "file": "bus_1_connected.csv"},
        "bus_2_connected": {"type": "elec_demand", "file": "bus_2_connected.csv"},
    }
    
    input_profiles = process_input_profiles(input_profile_dict, t_start, dynamic)
    
    prosumer_paths = {"LaTreille": "IMC.json"}
    
    prosumer_dict = dict()
    for prosumer_name, prosumer_path in prosumer_paths.items():
        with open(prosumer_path) as f:
            prosumer_json = json.load(f)
        prosumer_dict[prosumer_name] = prosumer_json
    
    prosumer_dict["LaTreille"]["components"]["bus_1_consumption"][
        "consumption"
    ] = input_profiles["bus_1_demand"]
    prosumer_dict["LaTreille"]["components"]["bus_2_consumption"][
        "consumption"
    ] = input_profiles["bus_2_demand"]
    prosumer_dict["LaTreille"]["components"]["bus_1_generation"][
        "generation"
    ] = input_profiles["bus_1_recuperation"]
    prosumer_dict["LaTreille"]["components"]["bus_2_generation"][
        "generation"
    ] = input_profiles["bus_2_recuperation"]
    prosumer_dict["LaTreille"]["additional_model_logic"]["bus_1_connected"][
        "enable"
    ] = input_profiles["bus_1_connected"].values
    prosumer_dict["LaTreille"]["additional_model_logic"]["bus_2_connected"][
        "enable"
    ] = input_profiles["bus_2_connected"].values
    max_driving_consumpion = max(
        max(input_profiles["bus_1_demand"].values),
        max(input_profiles["bus_1_demand"].values),
    )
    with open(
        prosumer_dict["LaTreille"]["components"]["bus_1_motor_inverter"]["model"]
    ) as f:
        inverter_1_efficiency = json.load(f)["efficiency"]
    bus_1_moter_inverter_capacity = max_driving_consumpion / inverter_1_efficiency
    prosumer_dict["LaTreille"]["components"]["bus_1_motor_inverter"][
        "min_capacity"
    ] = bus_1_moter_inverter_capacity
    prosumer_dict["LaTreille"]["components"]["bus_1_motor_inverter"][
        "max_capacity"
    ] = bus_1_moter_inverter_capacity
    with open(
        prosumer_dict["LaTreille"]["components"]["bus_2_motor_inverter"]["model"]
    ) as f:
        inverter_2_efficiency = json.load(f)["efficiency"]
    bus_2_moter_inverter_capacity = max_driving_consumpion / inverter_2_efficiency
    prosumer_dict["LaTreille"]["components"]["bus_2_motor_inverter"][
        "min_capacity"
    ] = bus_2_moter_inverter_capacity
    prosumer_dict["LaTreille"]["components"]["bus_2_motor_inverter"][
        "max_capacity"
    ] = bus_2_moter_inverter_capacity
    
    prosumer_main = ProsumerMain(prosumer_dict, dynamic)
    
    prosumer_sizing_strategy = ["annuity", "peak_power_costs"]
    prosumer_main.optimize_sizing("sized", prosumer_sizing_strategy)
    
    prosumer_main.save_results()