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EOS/tests/test_genetic0simulation.py
T
AndreasGitHubBobby Noelter0b2g1t
 8224c64654 feat: integrate device and runtime configuration foundation on main (#1328) 
* feat: adapt configuration for multi optimization algorithms

Decouple configuration from optimization algorithm parameters. Add to_[algorithm]_param() methods
to the configuration that derive optimization algorithm specific parameters from the configuration.
Add x-scope tags to the configuration options that describe for which specific algorithms the
configuration option is for.

The whole device settings are restructured. There are now general settings for the device classes
with the afore mentioned to_[algorithm]_param() methods. The general device settings got their own
directory `devices/settings`. By this the parameter class also does not have to be a pydantic model
which can be used for future optimization/ simulations speed up.

Also the parameter class for a device is now part of the device module. This better decouples and
also is the natural place for parameters of a device.

Besides this feature there are also fixes and improvements:

* feat: extend home appliance time window settings and simulation

  Home appliance can now be configured for multiple runs with per-cycle allowed time windows. The
  number of remaining cycles to plan is determined at runtime by reading the
  ``cycles_completed_measurement_key`` from the measurement store.

* feat: specialiced CycleTimeWindowSequence for time window sequences

  Sequence of time windows associated to cycles.

  This model specializes ``ValueTimeWindowSequence`` so that the ``value``
  field of each ``ValueTimeWindow`` encodes the **cycle index** (0-based
  integer) the window belongs to.

  Typical use: an appliance that must run ``n`` times per day, each run
  constrained to a distinct time window.  Assign ``value=0`` to windows
  for the first cycle, ``value=1`` for the second, and so on.  Multiple
  windows may share the same cycle index (their allowed regions are unioned).
  Windows with ``value=None`` are silently ignored by all cycle-aware methods.

* fix: Make test_configmigrate also regard the _ANY_SENTENIEL in key values

* chore: Make devices configurations a map instead of a list

  This makes config paths stable regardless of declaration order and lets each device settings
  class build its own config path from ``self.device_id`` without needing an external index.
  Tests are adapted likewise.

  Devices configurations are automatically migrated from lists to maps.

* chore: rename levelized_cost_of_storage_kwh to levelized_cost_of_storage_amt kwh

  This better fits in the naming scheme and also makes clear the costs are money.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

* fix: runtime config update ignored by config file

Runtime settings were handed back to pydantic-settings as init settings,
which rank below the config file and the environment. Any key already
present in EOS.config.json or in the environment silently discarded the
update, so a bulk PUT /v1/config returned 200 without applying anything,
while the granular PUT /v1/config/{path} endpoint kept working.

Add a dedicated runtime settings source ranked directly below the command
line arguments and record granular updates there as well, so both
endpoints share one store that survives re-evaluation of the settings
sources. Environment variables keep precedence over the config file for
all keys that were not set at runtime.

Also repairs revert_settings() and update(), which passed their data
through the same init settings.

Closes #1303

* fix: env vars ignored on first config build

ConfigEOS.__init__ passed self as first positional argument to _setup,
which forwards it to pydantic_settings.BaseSettings.__init__. Its first
positional parameter is _case_sensitive, so the environment source
matched the upper case variable names against the lower case field names
and returned nothing. Environment settings only took effect after the
next configuration setup.

* docs: changelog for config priority fixes

* fix(config): preserve device identities and storage costs during migration

* fix(devices): preserve charge-rate typing and public import compatibility

* ruff format fix

* fix(config): satisfy typed device conversion and migration contracts

* docs(config): refresh validated configuration prerequisite schemas

---------

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: r0b2g1t <r0b2g1t@users.noreply.github.com>
2026-09-17 17:51:40 +02:00

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import numpy as np
import pytest
from akkudoktoreos.config.configabc import TimeWindow, TimeWindowSequence
from akkudoktoreos.devices.genetic0.genetic0battery import (
Genetic0Battery,
Genetic0ElectricVehicleParameters,
Genetic0SolarPanelBatteryParameters,
)
from akkudoktoreos.devices.genetic0.genetic0homeappliance import (
Genetic0HomeAppliance,
Genetic0HomeApplianceParameters,
)
from akkudoktoreos.devices.genetic0.genetic0inverter import (
Genetic0Inverter,
Genetic0InverterParameters,
)
from akkudoktoreos.optimization.genetic0.genetic0 import (
Genetic0EnergyManagementParameters,
Genetic0Simulation,
Genetic0SimulationResult,
)
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.model_validate(dict(
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.model_validate(dict(
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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