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1abdd345c47b3db5b59b7989491ee1f43fd0fa49
EOS/tests/single_test_genetic0optimization.py
T
Bobby NoelteGitHubdr-dimitriNormann
 1abdd345c4 fix: unify mypy environments for local checks and CI (#1291) 
The isolated pre-commit mypy hook previously omitted runtime type information that
make mypy used, hiding errors involving dependencies such as Pydantic and Pendulum.
Makefile, pre-commit and CI now run the same full-project typing policy in the
development environment defined by uv.lock.

- Use uv run --locked --exact --extra dev and the same mypy arguments for Makefile
  and the local hook. Check all of src and tests, including on configuration-only
  changes.
- Pin Python 3.13 for local development and the pre-commit CI job, and install the
  locked pre-commit version in CI.
- Disable incremental analysis because existing Pendulum cache state changes mypy 2.3.1
  diagnostics. Document the policy, the performance tradeoff and the existing typing debt.
- Add a regression test that exercises Makefile, the hook and the CI command in a
  temporary project, accepting valid dependency types and detecting deliberate
  Pydantic/Pendulum assignment errors.

Resolve the newly detected mypy diagnostics.

- Enable the numpydantic and Pydantic mypy plugins, retaining strict Pydantic
  constructor typing with init_typed = true. Validate raw/coercible payloads through model_validate.
- Propagate concrete record, provider and time-window types through generic collections,
  factories and lookup methods. Preserve runtime field inspection and generated time-window
  documentation.
- Align Pendulum annotations with actual factory/arithmetic results while retaining Pydantic
  validation adapters at runtime. Correct optional values, array boundaries, REST handlers
  and plotting interfaces.
- Add pinned scipy-stubs and types-psutil, update uv.lock, and supply the plugins' dependencies.
- Add runtime regression coverage for validated path defaults, normalized time-series metadata,
  generic field inspection, invalid timestamps and unsupported provider imports.

Runtime and compatibility details:

- Validate path defaults as Path objects while retaining raw string defaults needed by
  migration serialization with exclude_defaults.
- Normalize feed-in tariff lists and default charge rates to NumPy arrays; reject missing
  timestamps/uninitialized values explicitly. Importing into a provider without import support
  returns HTTP 400.
- Public JSON schemas and OpenAPI structure match main (excluding the generated version).

Signed-off-by: dr-dimitry

Signed-off-by: dr-dimitry
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: dr-dimitri <87113560+dr-dimitri@users.noreply.github.com>
Co-authored-by: Normann <github@koldrack.com>
2026-09-10 23:20:35 +02:00

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#!/usr/bin/env python3
import argparse
import asyncio
import cProfile
import json
import pstats
import sys
import time
from typing import Any
import numpy as np
from loguru import logger
from akkudoktoreos.core.coreabc import get_config, get_ems, get_prediction
from akkudoktoreos.core.emsettings import EnergyManagementMode
from akkudoktoreos.optimization.genetic0.genetic0params import (
Genetic0OptimizationParameters,
)
from akkudoktoreos.optimization.optimization import OptimizationAlgorithm
from akkudoktoreos.utils.datetimeutil import to_datetime
config_eos = get_config()
prediction_eos = get_prediction()
ems_eos = get_ems()
async def prepare_optimization_real_parameters() -> Genetic0OptimizationParameters:
"""Prepare and return optimization parameters with real world data.
Returns:
GeneticOptimizationParameters: Configured optimization parameters
"""
# Make a config
settings = {
"general": {
"latitude": 52.52,
"longitude": 13.405,
},
"prediction": {
"hours": 48,
"historic_hours": 24,
},
"optimization": {
"algorithm": "GENETIC0",
"genetic0": {
"horizon_hours": 24,
"individuals": 300,
"generations": 400,
"seed": None,
"penalties": {
"ev_soc_miss": 10,
},
},
},
# PV Forecast
"pvforecast": {
"provider": "PVForecastAkkudoktor",
"planes": [
{
"peakpower": 5.0,
"surface_azimuth": -10,
"surface_tilt": 7,
"userhorizon": [20, 27, 22, 20],
"inverter_paco": 10000,
},
{
"peakpower": 4.8,
"surface_azimuth": -90,
"surface_tilt": 7,
"userhorizon": [30, 30, 30, 50],
"inverter_paco": 10000,
},
{
"peakpower": 1.4,
"surface_azimuth": -40,
"surface_tilt": 60,
"userhorizon": [60, 30, 0, 30],
"inverter_paco": 2000,
},
{
"peakpower": 1.6,
"surface_azimuth": 5,
"surface_tilt": 45,
"userhorizon": [45, 25, 30, 60],
"inverter_paco": 1400,
},
],
},
# Weather Forecast
"weather": {
"provider": "ClearOutside",
},
# Electricity Price Forecast
"elecprice": {
"provider": "ElecPriceAkkudoktor",
},
# Load Forecast
"load": {
"provider": "LoadAkkudoktor",
"loadakkudoktor": {
"loadakkudoktor_year_energy_kwh": 5000, # Energy consumption per year in kWh
},
},
# -- Simulations --
# Assure we have charge rates for the EV
"devices": {
"max_electric_vehicles": 1,
"electric_vehicles": [
{
"charge_rates": [
0.0,
6.0 / 16.0,
8.0 / 16.0,
10.0 / 16.0,
12.0 / 16.0,
14.0 / 16.0,
1.0,
],
},
],
},
}
# Update/ set configuration
config_eos.merge_settings_from_dict(settings)
# Get current prediction data for optimization run
ems_eos.set_start_datetime()
print(
f"Real data prediction from {prediction_eos.ems_start_datetime} to {prediction_eos.end_datetime}"
)
await prediction_eos.update_data()
# PV Forecast (in W)
pv_forecast = await prediction_eos.key_to_array(
key="pvforecast_ac_power",
start_datetime=prediction_eos.ems_start_datetime,
end_datetime=prediction_eos.end_datetime,
)
print(f"pv_forecast: {pv_forecast}")
# Temperature Forecast (in degree C)
temperature_forecast = await prediction_eos.key_to_array(
key="weather_temp_air",
start_datetime=prediction_eos.ems_start_datetime,
end_datetime=prediction_eos.end_datetime,
)
print(f"temperature_forecast: {temperature_forecast}")
# Electricity price (per Wh)
electricity_price_per_wh = await prediction_eos.key_to_array(
key="elecprice_marketprice_wh",
start_datetime=prediction_eos.ems_start_datetime,
end_datetime=prediction_eos.end_datetime,
fill_method="ffill",
)
print(f"electricity_price_per_wh: {electricity_price_per_wh}")
# Overall System Load (in W)
total_load = await prediction_eos.key_to_array(
key="load_mean",
start_datetime=prediction_eos.ems_start_datetime,
end_datetime=prediction_eos.end_datetime,
)
print(f"total_load: {total_load}")
# Start Solution (binary)
start_solution = None
print(f"start_solution: {start_solution}")
# Define parameters for the optimization problem
return Genetic0OptimizationParameters.model_validate({
"ems": {
"price_per_wh_battery": 0e-05,
"feed_in_tariff_per_wh": 7e-05,
"total_load": total_load,
"pv_forecast_wh": pv_forecast,
"electricity_price_per_wh": electricity_price_per_wh,
},
"pv_battery": {
"device_id": "battery 1",
"capacity_wh": 26400,
"initial_soc_percentage": 15,
"min_soc_percentage": 15,
},
"inverter": {
"device_id": "inverter 1",
"max_power_wh": 10000,
"battery_id": "battery 1",
},
"ev": {
"device_id": "electric vehicle 1",
"min_soc_percentage": 50,
"capacity_wh": 60000,
"charging_efficiency": 0.95,
"max_charge_power_w": 11040,
"initial_soc_percentage": 5,
},
"temperature_forecast": temperature_forecast,
"start_solution": start_solution,
})
def prepare_optimization_parameters() -> Genetic0OptimizationParameters:
"""Prepare and return optimization parameters with predefined data.
Returns:
GeneticOptimizationParameters: Configured optimization parameters
"""
# Initialize the optimization problem using the default configuration
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {
"horizon_hours": 48,
"interval": 3600,
"algorithm": "GENETIC0",
"genetic0": {
"individuals": 300,
"generations": 400,
"seed": None,
"penalties": {
"ev_soc_miss": 10,
},
},
},
# Assure we have charge rates for the EV
"devices": {
"max_electric_vehicles": 1,
"electric_vehicles": [
{
"device_id": "Default EV",
"charge_rates": [
0.0,
6.0 / 16.0,
8.0 / 16.0,
10.0 / 16.0,
12.0 / 16.0,
14.0 / 16.0,
1.0,
],
},
],
},
}
)
# PV Forecast (in W)
pv_forecast = np.zeros(48)
pv_forecast[12] = 5000
# Temperature Forecast (in degree C)
temperature_forecast = [
18.3,
17.8,
16.9,
16.2,
15.6,
15.1,
14.6,
14.2,
14.3,
14.8,
15.7,
16.7,
17.4,
18.0,
18.6,
19.2,
19.1,
18.7,
18.5,
17.7,
16.2,
14.6,
13.6,
13.0,
12.6,
12.2,
11.7,
11.6,
11.3,
11.0,
10.7,
10.2,
11.4,
14.4,
16.4,
18.3,
19.5,
20.7,
21.9,
22.7,
23.1,
23.1,
22.8,
21.8,
20.2,
19.1,
18.0,
17.4,
]
# Electricity price (per Wh)
electricity_price_per_wh = np.full(48, 0.001)
electricity_price_per_wh[0:10] = 0.00001
electricity_price_per_wh[11:15] = 0.00005
electricity_price_per_wh[20] = 0.00001
# Overall System Load (in W)
total_load = [
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,
]
# Start Solution (binary)
start_solution = None
# Define parameters for the optimization problem
return Genetic0OptimizationParameters.model_validate({
"ems": {
"price_per_wh_battery": 0e-05,
"feed_in_tariff_per_wh": 7e-05,
"total_load": total_load,
"pv_forecast_wh": pv_forecast,
"electricity_price_per_wh": electricity_price_per_wh,
},
"pv_battery": {
"device_id": "battery 1",
"capacity_wh": 26400,
"initial_soc_percentage": 15,
"min_soc_percentage": 15,
},
"inverter": {
"device_id": "inverter 1",
"max_power_wh": 10000,
"battery_id": "battery 1",
},
"ev": {
"device_id": "electric vehicle 1",
"min_soc_percentage": 50,
"capacity_wh": 60000,
"charging_efficiency": 0.95,
"max_charge_power_w": 11040,
"initial_soc_percentage": 5,
},
"temperature_forecast": temperature_forecast,
"start_solution": start_solution,
})
def run_optimization(
real_world: bool, start_hour: int, verbose: bool, seed: int, parameters_file: str, ngen: int
) -> Any:
"""Run the optimization problem.
Args:
start_hour (int, optional): Starting hour for optimization. Defaults to 0.
verbose (bool, optional): Whether to print verbose output. Defaults to False.
Returns:
dict: Optimization result as a dictionary
"""
# Prepare parameters
if parameters_file:
with open(parameters_file, "r") as f:
parameters = Genetic0OptimizationParameters(**json.load(f))
elif real_world:
parameters = asyncio.run(prepare_optimization_real_parameters())
else:
parameters = prepare_optimization_parameters()
logger.info("Optimization Parameters:")
logger.info(parameters.model_dump_json(indent=4))
if start_hour is None:
start_datetime = None
else:
start_datetime = to_datetime().set(hour=start_hour)
asyncio.run(
ems_eos.run(
start_datetime=start_datetime,
mode=EnergyManagementMode.OPTIMIZATION,
algorithm=OptimizationAlgorithm.GENETIC0,
genetic0_parameters=parameters,
genetic0_generations=ngen,
genetic0_seed=seed,
)
)
solution = ems_eos.genetic0_solution()
if solution is None:
return None
return solution.model_dump_json()
def main():
"""Main function to run the optimization script with optional profiling."""
parser = argparse.ArgumentParser(description="Run Energy Optimization Simulation")
parser.add_argument("--profile", action="store_true", help="Enable performance profiling")
parser.add_argument(
"--verbose", action="store_true", help="Enable verbose output during optimization"
)
parser.add_argument(
"--real-world", action="store_true", help="Use real world data for predictions"
)
parser.add_argument(
"--start-hour", type=int, default=0, help="Starting hour for optimization (default: 0)"
)
parser.add_argument(
"--parameters-file",
type=str,
default="",
help="Load optimization parameters from json file (default: unset)",
)
parser.add_argument("--seed", type=int, default=42, help="Use fixed random seed (default: 42)")
parser.add_argument(
"--ngen",
type=int,
default=400,
help="Number of generations during optimization process (default: 400)",
)
args = parser.parse_args()
if args.profile:
# Run with profiling
profiler = cProfile.Profile()
try:
result = profiler.runcall(
run_optimization,
real_world=args.real_world,
start_hour=args.start_hour,
verbose=args.verbose,
seed=args.seed,
parameters_file=args.parameters_file,
ngen=args.ngen,
)
# Print profiling statistics
stats = pstats.Stats(profiler)
stats.strip_dirs().sort_stats("cumulative").print_stats(200)
# Print result
if args.verbose:
print("\nOptimization Result:")
print(result)
except Exception as e:
print(f"Error during optimization: {e}", file=sys.stderr)
sys.exit(1)
else:
# Run without profiling
try:
start_time = time.time()
result = run_optimization(
real_world=args.real_world,
start_hour=args.start_hour,
verbose=args.verbose,
seed=args.seed,
parameters_file=args.parameters_file,
ngen=args.ngen,
)
end_time = time.time()
elapsed_time = end_time - start_time
if args.verbose:
print(f"\nElapsed time: {elapsed_time:.4f} seconds.")
print("\nOptimization Result:")
print(result)
except Exception as e:
print(f"Error during optimization: {e}", file=sys.stderr)
sys.exit(1)
if __name__ == "__main__":
main()
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