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* chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
381 lines
10 KiB
Python
381 lines
10 KiB
Python
import numpy as np
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import pytest
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from akkudoktoreos.devices.genetic.battery import Battery
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from akkudoktoreos.devices.genetic.homeappliance import HomeAppliance
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from akkudoktoreos.devices.genetic.inverter import Inverter
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from akkudoktoreos.optimization.genetic.genetic import GeneticSimulation
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from akkudoktoreos.optimization.genetic.geneticdevices import (
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ElectricVehicleParameters,
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HomeApplianceParameters,
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InverterParameters,
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SolarPanelBatteryParameters,
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)
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from akkudoktoreos.optimization.genetic.geneticparams import (
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GeneticEnergyManagementParameters,
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GeneticOptimizationParameters,
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)
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from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSimulationResult
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from akkudoktoreos.utils.datetimeutil import (
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TimeWindow,
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TimeWindowSequence,
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to_duration,
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to_time,
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)
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start_hour = 1
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# Example initialization of necessary components
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@pytest.fixture
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def genetic_simulation(config_eos) -> GeneticSimulation:
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"""Fixture to create an EnergyManagement instance with given test parameters."""
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# Assure configuration holds the correct values
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config_eos.merge_settings_from_dict(
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{"prediction": {"hours": 48}, "optimization": {"hours": 24}}
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)
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assert config_eos.prediction.hours == 48
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assert config_eos.optimization.horizon_hours == 24
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# Initialize the battery and the inverter
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akku = Battery(
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SolarPanelBatteryParameters(
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device_id="battery1",
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capacity_wh=5000,
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initial_soc_percentage=80,
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min_soc_percentage=10,
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),
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prediction_hours = config_eos.prediction.hours,
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)
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akku.reset()
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inverter = Inverter(
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InverterParameters(device_id="inverter1", max_power_wh=10000, battery_id=akku.parameters.device_id),
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battery = akku,
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)
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# Household device (currently not used, set to None)
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home_appliance = HomeAppliance(
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HomeApplianceParameters(
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device_id="dishwasher1",
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consumption_wh=2000,
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duration_h=2,
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time_windows=None,
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),
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optimization_hours = config_eos.optimization.horizon_hours,
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prediction_hours = config_eos.prediction.hours,
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)
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# Example initialization of electric car battery
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eauto = Battery(
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ElectricVehicleParameters(
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device_id="ev1", capacity_wh=26400, initial_soc_percentage=10, min_soc_percentage=10
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),
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prediction_hours = config_eos.prediction.hours,
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)
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eauto.set_charge_per_hour(np.full(config_eos.prediction.hours, 1))
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# Parameters based on previous example data
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pv_prognose_wh = [
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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8.05,
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352.91,
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728.51,
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930.28,
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1043.25,
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1106.74,
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1161.69,
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6018.82,
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5519.07,
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3969.88,
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3017.96,
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1943.07,
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1007.17,
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319.67,
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7.88,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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5.04,
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335.59,
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705.32,
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1121.12,
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1604.79,
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2157.38,
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1433.25,
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5718.49,
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4553.96,
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3027.55,
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2574.46,
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1720.4,
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963.4,
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383.3,
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0,
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0,
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0,
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]
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strompreis_euro_pro_wh = [
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0.0003384,
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0.0003318,
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0.0003284,
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0.0003283,
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0.0003289,
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0.0003334,
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0.0003290,
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0.0003302,
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0.0003042,
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0.0002430,
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0.0002280,
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0.0002212,
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0.0002093,
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0.0001879,
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0.0001838,
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0.0002004,
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0.0002198,
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0.0002270,
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0.0002997,
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0.0003195,
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0.0003081,
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0.0002969,
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0.0002921,
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0.0002780,
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0.0003384,
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0.0003318,
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0.0003284,
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0.0003283,
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0.0003289,
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0.0003334,
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0.0003290,
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0.0003302,
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0.0003042,
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0.0002430,
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0.0002280,
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0.0002212,
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0.0002093,
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0.0001879,
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0.0001838,
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0.0002004,
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0.0002198,
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0.0002270,
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0.0002997,
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0.0003195,
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0.0003081,
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0.0002969,
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0.0002921,
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0.0002780,
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]
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einspeiseverguetung_euro_pro_wh = 0.00007
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preis_euro_pro_wh_akku = 0.0001
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gesamtlast = [
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676.71,
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876.19,
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527.13,
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468.88,
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531.38,
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517.95,
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483.15,
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472.28,
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1011.68,
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995.00,
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1053.07,
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1063.91,
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1320.56,
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1132.03,
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1163.67,
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1176.82,
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1216.22,
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1103.78,
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1129.12,
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1178.71,
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1050.98,
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988.56,
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912.38,
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704.61,
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516.37,
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868.05,
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694.34,
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608.79,
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556.31,
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488.89,
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506.91,
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804.89,
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1141.98,
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1056.97,
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992.46,
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1155.99,
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827.01,
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1257.98,
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1232.67,
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871.26,
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860.88,
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1158.03,
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1222.72,
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1221.04,
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949.99,
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987.01,
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733.99,
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592.97,
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]
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# Initialize the energy management system with the respective parameters
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simulation = GeneticSimulation()
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simulation.prepare(
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GeneticEnergyManagementParameters(
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pv_prognose_wh=pv_prognose_wh,
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strompreis_euro_pro_wh=strompreis_euro_pro_wh,
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einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh,
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preis_euro_pro_wh_akku=preis_euro_pro_wh_akku,
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gesamtlast=gesamtlast,
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),
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optimization_hours = config_eos.optimization.horizon_hours,
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prediction_hours = config_eos.prediction.hours,
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inverter=inverter,
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ev=eauto,
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home_appliance=home_appliance,
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)
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# Init for test
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assert simulation.ac_charge_hours is not None
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assert simulation.dc_charge_hours is not None
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assert simulation.bat_discharge_hours is not None
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assert simulation.ev_charge_hours is not None
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simulation.ac_charge_hours[start_hour] = 1.0
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simulation.dc_charge_hours[start_hour] = 1.0
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simulation.bat_discharge_hours[start_hour] = 1.0
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simulation.ev_charge_hours[start_hour] = 1.0
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simulation.home_appliance_start_hour = 2
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return simulation
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def test_simulation(genetic_simulation):
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"""Test the EnergyManagement simulation method."""
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simulation = genetic_simulation
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# Simulate starting from hour 1 (this value can be adjusted)
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result = simulation.simulate(start_hour=start_hour)
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# visualisiere_ergebnisse(
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# simulation.gesamtlast,
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# simulation.pv_prognose_wh,
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# simulation.strompreis_euro_pro_wh,
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# result,
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# simulation.akku.discharge_array+simulation.akku.charge_array,
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# None,
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# simulation.pv_prognose_wh,
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# start_hour,
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# 48,
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# np.full(48, 0.0),
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# filename="visualization_results.pdf",
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# extra_data=None,
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# )
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# Assertions to validate results
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assert result is not None, "Result should not be None"
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assert isinstance(result, dict), "Result should be a dictionary"
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assert "Last_Wh_pro_Stunde" in result, "Result should contain 'Last_Wh_pro_Stunde'"
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"""
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Check the result of the simulation based on expected values.
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"""
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# Example result returned from the simulation (used for assertions)
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assert result is not None, "Result should not be None."
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# Check that the result is a dictionary
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assert isinstance(result, dict), "Result should be a dictionary."
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assert GeneticSimulationResult(**result) is not None
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# Check the length of the main arrays
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assert len(result["Last_Wh_pro_Stunde"]) == 47, (
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"The length of 'Last_Wh_pro_Stunde' should be 48."
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)
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assert len(result["Netzeinspeisung_Wh_pro_Stunde"]) == 47, (
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"The length of 'Netzeinspeisung_Wh_pro_Stunde' should be 48."
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)
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assert len(result["Netzbezug_Wh_pro_Stunde"]) == 47, (
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"The length of 'Netzbezug_Wh_pro_Stunde' should be 48."
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)
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assert len(result["Kosten_Euro_pro_Stunde"]) == 47, (
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"The length of 'Kosten_Euro_pro_Stunde' should be 48."
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)
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assert len(result["akku_soc_pro_stunde"]) == 47, (
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"The length of 'akku_soc_pro_stunde' should be 48."
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)
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# Verify specific values in the 'Last_Wh_pro_Stunde' array
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assert result["Last_Wh_pro_Stunde"][1] == 1527.13, (
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"The value at index 1 of 'Last_Wh_pro_Stunde' should be 1527.13."
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)
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assert result["Last_Wh_pro_Stunde"][2] == 1468.88, (
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"The value at index 2 of 'Last_Wh_pro_Stunde' should be 1468.88."
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)
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assert result["Last_Wh_pro_Stunde"][12] == 1132.03, (
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"The value at index 12 of 'Last_Wh_pro_Stunde' should be 1132.03."
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)
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# Verify that the value at index 0 is 'None'
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# Check that 'Netzeinspeisung_Wh_pro_Stunde' and 'Netzbezug_Wh_pro_Stunde' are consistent
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assert result["Netzbezug_Wh_pro_Stunde"][1] == 1527.13, (
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"The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 1527.13."
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)
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# Verify the total balance
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assert abs(result["Gesamtbilanz_Euro"] - 6.612835813556755) < 1e-5, (
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"Total balance should be 6.612835813556755."
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)
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# Check total revenue and total costs
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assert abs(result["Gesamteinnahmen_Euro"] - 1.964301131937134) < 1e-5, (
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"Total revenue should be 1.964301131937134."
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)
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assert abs(result["Gesamtkosten_Euro"] - 8.577136945493889) < 1e-5, (
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"Total costs should be 8.577136945493889 ."
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)
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# Check the losses
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assert abs(result["Gesamt_Verluste"] - 1620.0) < 1e-5, (
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"Total losses should be 1620.0 ."
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)
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# Check the values in 'akku_soc_pro_stunde'
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assert result["akku_soc_pro_stunde"][-1] == 98.0, (
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"The value at index -1 of 'akku_soc_pro_stunde' should be 98.0."
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)
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assert result["akku_soc_pro_stunde"][1] == 98.0, (
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"The value at index 1 of 'akku_soc_pro_stunde' should be 98.0."
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)
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# Check home appliances
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assert sum(simulation.home_appliance.get_load_curve()) == 2000, (
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"The sum of 'simulation.home_appliance.get_load_curve()' should be 2000."
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)
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assert (
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np.nansum(
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np.where(
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result["Home_appliance_wh_per_hour"] is None,
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np.nan,
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np.array(result["Home_appliance_wh_per_hour"]),
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)
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)
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== 2000
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), "The sum of 'Home_appliance_wh_per_hour' should be 2000."
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print("All tests passed successfully.")
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