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EOS/tests/test_genetic0simulation.py
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Bobby NoelteandGitHub e23bb7b497
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chore: prepare for update of genetic algorithm (#1190) 
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>
2026-07-29 12:56:08 +02:00

655 lines
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import numpy as np
import pytest
from akkudoktoreos.config.configabc import TimeWindow, TimeWindowSequence
from akkudoktoreos.devices.genetic0.genetic0battery import Genetic0Battery
from akkudoktoreos.devices.genetic0.genetic0homeappliance import Genetic0HomeAppliance
from akkudoktoreos.devices.genetic0.genetic0inverter import Genetic0Inverter
from akkudoktoreos.optimization.genetic0.genetic0 import (
Genetic0Simulation,
Genetic0SimulationResult,
)
from akkudoktoreos.optimization.genetic0.genetic0devices import (
Genetic0ElectricVehicleParameters,
Genetic0HomeApplianceParameters,
Genetic0InverterParameters,
Genetic0SolarPanelBatteryParameters,
)
from akkudoktoreos.optimization.genetic0.genetic0params import (
Genetic0EnergyManagementParameters,
)
from akkudoktoreos.utils.datetimeutil import to_duration, to_time
START_HOUR = 0
# Example initialization of necessary components
@pytest.fixture
def genetic0_simulation(config_eos) -> Genetic0Simulation:
"""Fixture to create an GENETIC2 simualtion 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.genetic0.horizon_hours == 24
# Initialize the battery and the inverter
akku = Genetic0Battery(
Genetic0SolarPanelBatteryParameters(
device_id="battery1",
capacity_wh=5000,
initial_soc_percentage=80,
min_soc_percentage=10,
),
prediction_hours = config_eos.prediction.hours,
)
akku.reset()
inverter = Genetic0Inverter(
Genetic0InverterParameters(device_id="inverter1", max_power_wh=10000, battery_id=akku.parameters.device_id),
battery = akku,
)
# Household device (currently not used, set to None)
home_appliance = Genetic0HomeAppliance(
Genetic0HomeApplianceParameters(
device_id="dishwasher1",
consumption_wh=2000,
duration_h=2,
time_windows=None,
),
optimization_hours = config_eos.optimization.genetic0.horizon_hours,
prediction_hours = config_eos.prediction.hours,
)
# Example initialization of electric car battery
eauto = Genetic0Battery(
Genetic0ElectricVehicleParameters(
device_id="ev1", capacity_wh=26400, initial_soc_percentage=10, min_soc_percentage=10
),
prediction_hours = config_eos.prediction.hours,
)
eauto.set_charge_per_hour(np.full(config_eos.prediction.hours, 1))
# Parameters based on previous example data
pv_prognose_wh = [
0,
0,
0,
0,
0,
0,
0,
8.05,
352.91,
728.51,
930.28,
1043.25,
1106.74,
1161.69,
6018.82,
5519.07,
3969.88,
3017.96,
1943.07,
1007.17,
319.67,
7.88,
0,
0,
0,
0,
0,
0,
0,
0,
0,
5.04,
335.59,
705.32,
1121.12,
1604.79,
2157.38,
1433.25,
5718.49,
4553.96,
3027.55,
2574.46,
1720.4,
963.4,
383.3,
0,
0,
0,
]
strompreis_euro_pro_wh = [
0.0003384,
0.0003318,
0.0003284,
0.0003283,
0.0003289,
0.0003334,
0.0003290,
0.0003302,
0.0003042,
0.0002430,
0.0002280,
0.0002212,
0.0002093,
0.0001879,
0.0001838,
0.0002004,
0.0002198,
0.0002270,
0.0002997,
0.0003195,
0.0003081,
0.0002969,
0.0002921,
0.0002780,
0.0003384,
0.0003318,
0.0003284,
0.0003283,
0.0003289,
0.0003334,
0.0003290,
0.0003302,
0.0003042,
0.0002430,
0.0002280,
0.0002212,
0.0002093,
0.0001879,
0.0001838,
0.0002004,
0.0002198,
0.0002270,
0.0002997,
0.0003195,
0.0003081,
0.0002969,
0.0002921,
0.0002780,
]
einspeiseverguetung_euro_pro_wh = 0.00007
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
genetic0_simulation = Genetic0Simulation()
genetic0_simulation.prepare(
Genetic0EnergyManagementParameters(
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.genetic0.horizon_hours,
prediction_hours = config_eos.prediction.hours,
inverter=inverter,
ev=eauto,
home_appliance=home_appliance,
)
# Init for test
assert genetic0_simulation.ac_charge_hours is not None
assert genetic0_simulation.dc_charge_hours is not None
assert genetic0_simulation.bat_discharge_hours is not None
assert genetic0_simulation.ev_charge_hours is not None
genetic0_simulation.ac_charge_hours[START_HOUR] = 1.0
genetic0_simulation.dc_charge_hours[START_HOUR] = 1.0
genetic0_simulation.bat_discharge_hours[START_HOUR] = 1.0
genetic0_simulation.ev_charge_hours[START_HOUR] = 1.0
genetic0_simulation.home_appliance_start_hour = 2
return genetic0_simulation
@pytest.fixture
def genetic0_simulation_2(config_eos) -> Genetic0Simulation:
"""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.genetic0.horizon_hours == 24
# Initialize the battery and the inverter
akku = Genetic0Battery(
Genetic0SolarPanelBatteryParameters(
device_id="battery1",
capacity_wh=5000,
initial_soc_percentage=80,
min_soc_percentage=10,
),
prediction_hours = config_eos.prediction.hours,
)
akku.reset()
inverter = Genetic0Inverter(
Genetic0InverterParameters(device_id="inverter1", max_power_wh=10000, battery_id=akku.parameters.device_id),
battery = akku,
)
# Household device (currently not used, set to None)
home_appliance = Genetic0HomeAppliance(
Genetic0HomeApplianceParameters(
device_id="dishwasher1",
consumption_wh=2000,
duration_h=2,
time_windows=None,
),
optimization_hours = config_eos.optimization.genetic0.horizon_hours,
prediction_hours = config_eos.prediction.hours,
)
# Example initialization of electric car battery
eauto = Genetic0Battery(
Genetic0ElectricVehicleParameters(
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 = Genetic0Simulation()
simulation.prepare(
Genetic0EnergyManagementParameters(
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.genetic0.horizon_hours,
prediction_hours = config_eos.prediction.hours,
inverter=inverter,
ev=eauto,
home_appliance=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
simulation.home_appliance_start_hour = 2
return simulation
def test_genetic0simulation(genetic0_simulation):
"""Test the EnergyManagement simulation method."""
simulation = genetic0_simulation
# Simulate starting from hour 1 (this value can be adjusted)
result = simulation.simulate(start_hour=START_HOUR)
# 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 "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."
assert Genetic0SimulationResult(**result) is not None
# 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."
)
# Verify specific values in the 'Last_Wh_pro_Stunde' array
assert result["Last_Wh_pro_Stunde"][1] == 876.19, (
"The value at index 1 of 'Last_Wh_pro_Stunde' should be 876.19."
)
assert result["Last_Wh_pro_Stunde"][2] == 1527.13, (
"The value at index 2 of 'Last_Wh_pro_Stunde' should be 1527.13."
)
assert result["Last_Wh_pro_Stunde"][12] == 1320.56, (
"The value at index 12 of 'Last_Wh_pro_Stunde' should be 1320.56."
)
# Verify that the value at index 0 is 'None'
# Check that 'Netzeinspeisung_Wh_pro_Stunde' and 'Netzbezug_Wh_pro_Stunde' are consistent
assert result["Netzbezug_Wh_pro_Stunde"][1] == 876.19, (
"The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 876.19."
)
# Verify the total balance
assert abs(result["Gesamtbilanz_Euro"] - 6.883546477556756) < 1e-5, (
"Total balance should be 6.883546477556756."
)
# Check total revenue and total costs
assert abs(result["Gesamteinnahmen_Euro"] - 1.964301131937134) < 1e-5, (
"Total revenue should be 1.964301131937134."
)
assert abs(result["Gesamtkosten_Euro"] - 8.84784760949389) < 1e-5, (
"Total costs should be 8.84784760949389."
)
# Check the losses
assert abs(result["Gesamt_Verluste"] - 1620.0) < 1e-5, (
"Total losses should be 1620.0 ."
)
# Check the values in 'akku_soc_pro_stunde'
assert result["akku_soc_pro_stunde"][-1] == 98.0, (
"The value at index -1 of 'akku_soc_pro_stunde' should be 98.0."
)
assert result["akku_soc_pro_stunde"][1] == 98.0, (
"The value at index 1 of 'akku_soc_pro_stunde' should be 98.0."
)
# Check home appliances
assert sum(simulation.home_appliance.get_load_curve()) == 2000, (
"The sum of 'simulation.home_appliance.get_load_curve()' should be 2000."
)
assert (
np.nansum(
np.where(
result["Home_appliance_wh_per_hour"] is None,
np.nan,
np.array(result["Home_appliance_wh_per_hour"]),
)
)
== 2000
), "The sum of 'Home_appliance_wh_per_hour' should be 2000."
print("All tests passed successfully.")
def test_genetic0simulation_2(genetic0_simulation_2):
"""Test the EnergyManagement simulation method."""
simulation = genetic0_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 Genetic0SimulationResult(**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(genetic0_simulation_2):
"""Test the set_parameters method of EnergyManagement."""
simulation = genetic0_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(genetic0_simulation_2):
"""Test the reset method of EnergyManagement."""
simulation = genetic0_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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