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Bobby NoelteandGitHub
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Andreas will update the genetic algorithm for 15-minutes optimization intervals. Copy the current GENETIC optimization algorithm to GENETIC0 to enable to keep the algorithm with the current functionality. Also copy resources like the load interpolator to the GENETIC0 algorithm to keep them despite possible later changes to the interpolator. Make the deprecated legacy /optimize endpoint use the GENETIC0 optimization algorithm to in-fact behave the same way even if there will later be changes to the GENETIC algorithm by Andreas. Add a new REST endpoint to provide the unprocessed optimisation results of the GENETIC and GENETIC0 algorithm in case one wants to use them as done with the deprecated /optimize endpoint. Adapt the optimization configuration to have distinct configurations for the GENETIC and the GENETIC0 algorithm. Create a copy of the current tests for the GENETIC algorithm to be used for the GENETIC0 algorithm. This avoids the tests for the GENETIC0 algorithm to be influenced by later changes by Andreas. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
377 lines
10 KiB
Python
377 lines
10 KiB
Python
import numpy as np
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import pytest
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from akkudoktoreos.config.configabc import TimeWindow, TimeWindowSequence
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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 to_duration, to_time
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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.genetic.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.genetic.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.genetic.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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