mirror of
https://github.com/Akkudoktor-EOS/EOS.git
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114 lines
4.3 KiB
Python
114 lines
4.3 KiB
Python
from types import SimpleNamespace
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from unittest.mock import patch
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import numpy as np
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import pytest
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from deap import creator
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from akkudoktoreos.config.config import ConfigEOS
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from akkudoktoreos.core.coreabc import get_ems
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from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
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from akkudoktoreos.utils.datetimeutil import to_datetime
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def _configure_hourly_grid(config_eos: ConfigEOS, *, start_hour: int = 0) -> None:
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 48},
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"optimization": {"horizon_hours": 48, "interval": 3600},
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}
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)
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get_ems(init=True).set_start_datetime(to_datetime().set(hour=start_hour, minute=0))
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def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = True
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opt.ev_possible_charge_values = [0.0, 1.0]
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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individual = creator.Individual([0] * opt.total_slots + [1] * opt.total_slots)
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first_result = {
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"Gesamtbilanz_Euro": 10.0,
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"Gesamt_Verluste": 0.0,
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"EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
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}
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repaired_result = {
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"Gesamtbilanz_Euro": 1.0,
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"Gesamt_Verluste": 0.0,
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"EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
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}
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parameters = SimpleNamespace(
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ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
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eauto=None,
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)
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with patch.object(opt, "evaluate_inner", side_effect=[first_result, repaired_result]) as evaluate:
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fitness = opt.evaluate(individual, parameters, start_hour=0, worst_case=False) # type: ignore[arg-type]
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assert evaluate.call_count == 2
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assert fitness == pytest.approx((1.0,))
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assert individual[opt.total_slots :] == [0] * opt.total_slots
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def test_mutated_warm_start_neighbors_keep_elapsed_slots(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos, start_hour=10)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=10)
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start_solution = [0] * opt.total_slots
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neighbors = opt._mutated_warm_start_neighbors(start_solution, count=5)
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assert len(neighbors) == 5
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assert len({tuple(neighbor) for neighbor in neighbors}) == 5
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assert all(neighbor[:10] == start_solution[:10] for neighbor in neighbors)
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assert all(neighbor != start_solution for neighbor in neighbors)
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def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.optimize_dc_charge = True
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opt.optimize_battery_grid_export = True
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opt.bat_possible_charge_values = [1.0]
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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slots = opt.total_slots
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opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
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opt.simulation.elect_revenue_per_hour_arr = np.linspace(0.00001, 0.0003, slots)
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opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
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opt.simulation.load_energy_array = np.full(slots, 500.0)
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guesses = opt._educated_guess_individuals()
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dc_allowed_state = 4
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export_state = 5
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assert len(guesses) >= 4
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assert all(len(guess) == slots for guess in guesses)
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assert any(guess[0] == dc_allowed_state for guess in guesses)
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assert any(guess[-1] == export_state for guess in guesses)
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def test_flat_feed_in_tariff_does_not_seed_direct_marketing(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.optimize_dc_charge = True
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opt.optimize_battery_grid_export = True
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opt.bat_possible_charge_values = [1.0]
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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slots = opt.total_slots
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opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
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opt.simulation.elect_revenue_per_hour_arr = np.full(slots, 0.00005)
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opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
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opt.simulation.load_energy_array = np.full(slots, 500.0)
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guesses = opt._educated_guess_individuals()
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export_state = 5
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assert all(export_state not in guess for guess in guesses)
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