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EOS/tests/test_geneticsimulation2.py
T
AndreasandClaude Opus 4.8 67cf6f7d8a feat(optimization): schedule any number of flexible consumers 
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>
2026-07-15 14:19:46 +02:00

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import numpy as np
import pytest
from akkudoktoreos.config.configabc import TimeWindow, TimeWindowSequence
from akkudoktoreos.devices.genetic.battery import Battery
from akkudoktoreos.devices.genetic.homeappliance import HomeAppliance
from akkudoktoreos.devices.genetic.inverter import Inverter
from akkudoktoreos.optimization.genetic.genetic import GeneticSimulation
from akkudoktoreos.optimization.genetic.geneticdevices import (
ElectricVehicleParameters,
HomeApplianceParameters,
InverterParameters,
SolarPanelBatteryParameters,
)
from akkudoktoreos.optimization.genetic.geneticparams import (
GeneticEnergyManagementParameters,
GeneticOptimizationParameters,
)
from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSimulationResult
from akkudoktoreos.utils.datetimeutil import to_duration, to_time
start_hour = 0
# Example initialization of necessary components
@pytest.fixture
def genetic_simulation_2(config_eos) -> GeneticSimulation:
"""Fixture to create an EnergyManagement instance with given test parameters."""
# Assure configuration holds the correct values
config_eos.merge_settings_from_dict(
{"prediction": {"hours": 48}, "optimization": {"hours": 24}}
)
assert config_eos.prediction.hours == 48
assert config_eos.optimization.horizon_hours == 24
# Initialize the battery and the inverter
akku = Battery(
SolarPanelBatteryParameters(
device_id="battery1",
capacity_wh=5000,
initial_soc_percentage=80,
min_soc_percentage=10,
),
prediction_hours = config_eos.prediction.hours,
)
akku.reset()
inverter = Inverter(
InverterParameters(device_id="inverter1", max_power_wh=10000, battery_id=akku.parameters.device_id),
battery = akku,
)
# Flexible consumer (fixed start at slot 2 for this deterministic test)
home_appliance = HomeAppliance(
HomeApplianceParameters(
device_id="dishwasher1",
consumption_wh=2000,
duration_h=2,
time_windows=None,
),
optimization_hours = config_eos.optimization.horizon_hours,
prediction_hours = config_eos.prediction.hours,
)
home_appliance.build_load_curve([2])
# Example initialization of electric car battery
eauto = Battery(
ElectricVehicleParameters(
device_id="ev1", capacity_wh=26400, initial_soc_percentage=10, min_soc_percentage=10
),
prediction_hours = config_eos.prediction.hours,
)
# Parameters based on previous example data
pv_prognose_wh = [0.0] * config_eos.prediction.hours
pv_prognose_wh[10] = 5000.0
pv_prognose_wh[11] = 5000.0
strompreis_euro_pro_wh = [0.001] * config_eos.prediction.hours
strompreis_euro_pro_wh[0:10] = [0.00001] * 10
strompreis_euro_pro_wh[11:15] = [0.00005] * 4
strompreis_euro_pro_wh[20] = 0.00001
einspeiseverguetung_euro_pro_wh = [0.00007] * len(strompreis_euro_pro_wh)
preis_euro_pro_wh_akku = 0.0001
gesamtlast = [
676.71,
876.19,
527.13,
468.88,
531.38,
517.95,
483.15,
472.28,
1011.68,
995.00,
1053.07,
1063.91,
1320.56,
1132.03,
1163.67,
1176.82,
1216.22,
1103.78,
1129.12,
1178.71,
1050.98,
988.56,
912.38,
704.61,
516.37,
868.05,
694.34,
608.79,
556.31,
488.89,
506.91,
804.89,
1141.98,
1056.97,
992.46,
1155.99,
827.01,
1257.98,
1232.67,
871.26,
860.88,
1158.03,
1222.72,
1221.04,
949.99,
987.01,
733.99,
592.97,
]
# Initialize the energy management system with the respective parameters
simulation = GeneticSimulation()
simulation.prepare(
GeneticEnergyManagementParameters(
pv_prognose_wh=pv_prognose_wh,
strompreis_euro_pro_wh=strompreis_euro_pro_wh,
einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh,
preis_euro_pro_wh_akku=preis_euro_pro_wh_akku,
gesamtlast=gesamtlast,
),
optimization_hours = config_eos.optimization.horizon_hours,
prediction_hours = config_eos.prediction.hours,
inverter=inverter,
ev=eauto,
home_appliances=[home_appliance],
)
ac = np.full(config_eos.prediction.hours, 0.0)
ac[20] = 1
simulation.ac_charge_hours = ac
dc = np.full(config_eos.prediction.hours, 0.0)
dc[11] = 1
simulation.dc_charge_hours = dc
return simulation
def test_simulation(genetic_simulation_2):
"""Test the EnergyManagement simulation method."""
simulation = genetic_simulation_2
# Simulate starting from hour 0 (this value can be adjusted)
result = simulation.simulate(start_hour=start_hour)
# --- Pls do not remove! ---
# visualisiere_ergebnisse(
# simulation.gesamtlast,
# simulation.pv_prognose_wh,
# simulation.strompreis_euro_pro_wh,
# result,
# simulation.akku.discharge_array+simulation.akku.charge_array,
# None,
# simulation.pv_prognose_wh,
# start_hour,
# 48,
# np.full(48, 0.0),
# filename="visualization_results.pdf",
# extra_data=None,
# )
# Assertions to validate results
assert result is not None, "Result should not be None"
assert isinstance(result, dict), "Result should be a dictionary"
assert GeneticSimulationResult(**result) is not None
assert "Last_Wh_pro_Stunde" in result, "Result should contain 'Last_Wh_pro_Stunde'"
"""
Check the result of the simulation based on expected values.
"""
# Example result returned from the simulation (used for assertions)
assert result is not None, "Result should not be None."
# Check that the result is a dictionary
assert isinstance(result, dict), "Result should be a dictionary."
# Verify that the expected keys are present in the result
expected_keys = [
"Last_Wh_pro_Stunde",
"Netzeinspeisung_Wh_pro_Stunde",
"Netzbezug_Wh_pro_Stunde",
"Kosten_Euro_pro_Stunde",
"akku_soc_pro_stunde",
"Einnahmen_Euro_pro_Stunde",
"Gesamtbilanz_Euro",
"EAuto_SoC_pro_Stunde",
"Gesamteinnahmen_Euro",
"Gesamtkosten_Euro",
"Verluste_Pro_Stunde",
"Gesamt_Verluste",
"Home_appliance_wh_per_hour",
]
for key in expected_keys:
assert key in result, f"The key '{key}' should be present in the result."
# Check the length of the main arrays
assert len(result["Last_Wh_pro_Stunde"]) == 48, (
"The length of 'Last_Wh_pro_Stunde' should be 48."
)
assert len(result["Netzeinspeisung_Wh_pro_Stunde"]) == 48, (
"The length of 'Netzeinspeisung_Wh_pro_Stunde' should be 48."
)
assert len(result["Netzbezug_Wh_pro_Stunde"]) == 48, (
"The length of 'Netzbezug_Wh_pro_Stunde' should be 48."
)
assert len(result["Kosten_Euro_pro_Stunde"]) == 48, (
"The length of 'Kosten_Euro_pro_Stunde' should be 48."
)
assert len(result["akku_soc_pro_stunde"]) == 48, (
"The length of 'akku_soc_pro_stunde' should be 48."
)
# Verfify DC and AC Charge Bins
assert abs(result["akku_soc_pro_stunde"][2] - 80.0) < 1e-5, (
"'akku_soc_pro_stunde[2]' should be 80.0."
)
assert abs(result["akku_soc_pro_stunde"][10] - 80.0) < 1e-5, (
"'akku_soc_pro_stunde[10]' should be 80."
)
assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][10] - 3946.93) < 1e-3, (
"'Netzeinspeisung_Wh_pro_Stunde[11]' should be 3946.93."
)
assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][11] - 2799.7263636361786) < 1e-3, (
"'Netzeinspeisung_Wh_pro_Stunde[11]' should be 2799.7263636361786."
)
assert abs(result["akku_soc_pro_stunde"][20] - 100) < 1e-5, (
"'akku_soc_pro_stunde[20]' should be 100."
)
assert abs(result["Last_Wh_pro_Stunde"][20] - 1050.98) < 1e-3, (
"'Last_Wh_pro_Stunde[20]' should be 1050.98."
)
print("All tests passed successfully.")
def test_set_parameters(genetic_simulation_2):
"""Test the set_parameters method of EnergyManagement."""
simulation = genetic_simulation_2
# Check if parameters are set correctly
assert simulation.load_energy_array is not None, "load_energy_array should not be None"
assert simulation.pv_prediction_wh is not None, "pv_prediction_wh should not be None"
assert simulation.elect_price_hourly is not None, "elect_price_hourly should not be None"
assert simulation.elect_revenue_per_hour_arr is not None, (
"elect_revenue_per_hour_arr should not be None"
)
def test_reset(genetic_simulation_2):
"""Test the reset method of EnergyManagement."""
simulation = genetic_simulation_2
simulation.reset()
assert simulation.ev.current_soc_percentage() == simulation.ev.parameters.initial_soc_percentage, "EV SOC should be reset to initial value"
assert simulation.battery.current_soc_percentage() == simulation.battery.parameters.initial_soc_percentage, (
"Battery SOC should be reset to initial value"
)
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