mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2026-08-31 12:46:38 +00:00
AndreasandClaude Opus 4.8
67cf6f7d8a
Replace the single hourly "dishwasher" home appliance with a list of flexible consumers (home_appliances). Each consumer defines its load either as an explicit power profile (energy-preservingly resampled onto the optimization slot grid, incl. 15-min and non-integer interval ratios) or the flat consumption_wh/duration_h fallback, and runs ONCE or DAILY within its time windows and the optimization horizon. - ConsumerScheduleMode + shared load-definition validation (XOR of profile/fallback, reject negative/NaN/inf, unique device_id) - ApplianceGeneLayout: variable appliance gene block (index into allowed_start_slots), ONCE/DAILY calendar-day based, no snapping - per-device output: result.home_appliance_energy_wh, appliance_starts (absolute local times), per-device solution columns and DDBC RUN/OFF instructions on state transitions only - deprecate dishwasher/washingstart/Home_appliance_wh_per_hour with backward-compatible mapping and explicit conflict rejection - max_home_appliances is now an upper bound only; no demo appliance and no on/off behaviour - docs, openapi.json, CHANGELOG and optimize_result_2* fixtures updated; new tests/test_homeappliance.py covers the mandatory test matrix Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
508 lines
15 KiB
Python
508 lines
15 KiB
Python
from unittest.mock import Mock
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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.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(
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device_id="inverter1", max_power_wh=10000, battery_id=akku.parameters.device_id
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),
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battery=akku,
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)
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# Flexible consumer (fixed start at slot 2 for this deterministic test)
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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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home_appliance.build_load_curve([2])
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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_appliances=[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.bat_grid_export_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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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 (
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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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assert (
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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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assert (
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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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assert (
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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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assert (
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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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# Verify specific values in the 'Last_Wh_pro_Stunde' array
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assert (
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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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assert (
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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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assert (
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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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# 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 (
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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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# Verify the total balance
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assert (
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abs(result["Gesamtbilanz_Euro"] - 7.025236588371921) < 1e-5
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), "Total balance should reflect the shared per-slot battery power limit."
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# Check total revenue and total costs
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assert (
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abs(result["Gesamteinnahmen_Euro"] - 2.3247787887715) < 1e-5
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), "Total revenue should respect the shared per-slot battery power limit."
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assert (
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abs(result["Gesamtkosten_Euro"] - 9.350015377143421) < 1e-5
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), "Total costs should respect the shared per-slot battery power limit."
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# Check the losses
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assert abs(result["Gesamt_Verluste"] - 1620.0) < 1e-5, "Total losses should be 1620.0 ."
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# Check the values in 'akku_soc_pro_stunde'
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assert (
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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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assert (
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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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# Check home appliances
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assert (
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sum(simulation.home_appliances[0].get_load_curve()) == 2000
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), "The sum of 'simulation.home_appliances[0].get_load_curve()' should be 2000."
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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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def test_direct_marketing_curtails_negative_feed_in(config_eos, monkeypatch):
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config_eos.merge_settings_from_dict(
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{"prediction": {"hours": 2}, "optimization": {"horizon_hours": 2}}
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)
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inverter = Inverter(InverterParameters(device_id="inverter1", max_power_wh=1000.0))
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monkeypatch.setattr(
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inverter.self_consumption_predictor,
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"calculate_expected_direct_consumption",
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Mock(side_effect=min),
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)
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simulation = GeneticSimulation()
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simulation.prepare(
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GeneticEnergyManagementParameters(
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pv_prognose_wh=[500.0, 500.0],
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strompreis_euro_pro_wh=[-0.0001, -0.0001],
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einspeiseverguetung_euro_pro_wh=[-0.0001, -0.0001],
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preis_euro_pro_wh_akku=0.0,
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gesamtlast=[0.0, 0.0],
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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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direct_marketing_enabled=True,
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)
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result = simulation.simulate(start_hour=0)
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assert result["Netzeinspeisung_Wh_pro_Stunde"][0] == 0.0
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assert result["Einnahmen_Euro_pro_Stunde"][0] == 0.0
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assert result["Verluste_Pro_Stunde"][0] == pytest.approx(500.0)
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def _direct_marketing_battery_export_simulation(
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config_eos,
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levelized_cost_of_storage_kwh: float = 0.0,
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dc_to_ac_efficiency: float = 1.0,
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) -> GeneticSimulation:
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config_eos.merge_settings_from_dict(
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{"prediction": {"hours": 2}, "optimization": {"horizon_hours": 2}}
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)
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battery = Battery(
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SolarPanelBatteryParameters(
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device_id="battery1",
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capacity_wh=1000,
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initial_soc_percentage=100,
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min_soc_percentage=0,
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charging_efficiency=1.0,
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discharging_efficiency=1.0,
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levelized_cost_of_storage_kwh=levelized_cost_of_storage_kwh,
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max_charge_power_w=500,
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),
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prediction_hours=config_eos.prediction.hours,
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)
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inverter = Inverter(
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InverterParameters(
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device_id="inverter1",
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max_power_wh=500.0,
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battery_id=battery.parameters.device_id,
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dc_to_ac_efficiency=dc_to_ac_efficiency,
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),
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battery=battery,
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)
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simulation = GeneticSimulation()
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simulation.prepare(
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GeneticEnergyManagementParameters(
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pv_prognose_wh=[0.0, 0.0],
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strompreis_euro_pro_wh=[0.0, 0.0],
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einspeiseverguetung_euro_pro_wh=[0.0002, 0.0002],
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preis_euro_pro_wh_akku=0.0,
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gesamtlast=[0.0, 0.0],
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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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direct_marketing_enabled=True,
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)
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return simulation
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def test_direct_marketing_discharge_allowed_does_not_export_battery(config_eos):
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simulation = _direct_marketing_battery_export_simulation(config_eos)
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assert simulation.bat_discharge_hours is not None
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simulation.bat_discharge_hours[0] = 1
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result = simulation.simulate(start_hour=0)
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assert result["Netzeinspeisung_Wh_pro_Stunde"][0] == 0.0
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assert simulation.battery is not None
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assert simulation.battery.current_soc_percentage() == 100.0
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def test_direct_marketing_battery_grid_export_uses_separate_signal(config_eos):
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simulation = _direct_marketing_battery_export_simulation(config_eos)
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assert simulation.bat_grid_export_hours is not None
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simulation.bat_grid_export_hours[0] = 1
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result = simulation.simulate(start_hour=0)
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assert result["Netzeinspeisung_Wh_pro_Stunde"][0] == pytest.approx(500.0)
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assert result["Einnahmen_Euro_pro_Stunde"][0] == pytest.approx(0.1)
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|
assert simulation.battery is not None
|
|
assert simulation.battery.current_soc_percentage() == 50.0
|
|
|
|
|
|
def test_battery_lcos_is_charged_once_on_delivered_energy(config_eos):
|
|
simulation = _direct_marketing_battery_export_simulation(
|
|
config_eos,
|
|
levelized_cost_of_storage_kwh=0.12,
|
|
dc_to_ac_efficiency=0.8,
|
|
)
|
|
assert simulation.bat_grid_export_hours is not None
|
|
simulation.bat_grid_export_hours[0] = 1
|
|
|
|
result = simulation.simulate(start_hour=0)
|
|
|
|
# The battery delivers 500 Wh DC, so LCOS is 0.5 kWh * 0.12 EUR/kWh
|
|
# = 0.06 EUR exactly once. After the 80% inverter, 400 Wh AC reaches
|
|
# the grid and earns 400 Wh * 0.0002 EUR/Wh = 0.08 EUR.
|
|
assert result["Kosten_Euro_pro_Stunde"][0] == pytest.approx(0.06)
|
|
assert result["Gesamtkosten_Euro"] == pytest.approx(0.06)
|
|
assert result["Einnahmen_Euro_pro_Stunde"][0] == pytest.approx(0.08)
|
|
assert result["Gesamtbilanz_Euro"] == pytest.approx(-0.02)
|