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04420e66ab1ba3b009e89c5c24adbb5f91daa34a
EOS/tests/single_test_optimization.py
Christopher Nadler 04420e66ab
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fix: Improve provider update error handling and add VRM provider settings validation (#887) 
* fix: improve error handling for provider updates

Distinguishes failures of active providers from inactive ones.
Propagates errors only for enabled providers, allowing execution
to continue if a non-active provider fails, which avoids unnecessary
interruptions and improves robustness.

* fix: add provider settings validation for forecast requests

Prevents potential runtime errors by checking if provider settings are configured
before accessing forecast credentials.

Raises a clear error when settings are missing to help with debugging misconfigurations.

* refactor(load): move provider settings to top-level fields

Transitions load provider settings from a nested "provider_settings" object with provider-specific keys to dedicated top-level fields.\n\nRemoves the legacy "provider_settings" mapping and updates migration logic to ensure backward compatibility with existing configurations.

* docs: update version numbers and documantation

---------

Co-authored-by: Normann <github@koldrack.com>
2026-02-26 18:31:47 +01:00

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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.genetic.geneticparams import (
GeneticOptimizationParameters,
)
from akkudoktoreos.utils.datetimeutil import to_datetime
config_eos = get_config()
prediction_eos = get_prediction()
ems_eos = get_ems()
def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
"""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": {
"horizon_hours": 24,
"interval": 3600,
"genetic": {
"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}"
)
prediction_eos.update_data()
# PV Forecast (in W)
pv_forecast = 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 = 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 (in Euro per Wh)
strompreis_euro_pro_wh = prediction_eos.key_to_array(
key="elecprice_marketprice_wh",
start_datetime=prediction_eos.ems_start_datetime,
end_datetime=prediction_eos.end_datetime,
)
print(f"strompreis_euro_pro_wh: {strompreis_euro_pro_wh}")
# Overall System Load (in W)
gesamtlast = prediction_eos.key_to_array(
key="load_mean",
start_datetime=prediction_eos.ems_start_datetime,
end_datetime=prediction_eos.end_datetime,
)
print(f"gesamtlast: {gesamtlast}")
# Start Solution (binary)
start_solution = None
print(f"start_solution: {start_solution}")
# Define parameters for the optimization problem
return GeneticOptimizationParameters(
**{
"ems": {
"preis_euro_pro_wh_akku": 0e-05,
"einspeiseverguetung_euro_pro_wh": 7e-05,
"gesamtlast": gesamtlast,
"pv_prognose_wh": pv_forecast,
"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
},
"pv_akku": {
"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",
},
"eauto": {
"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() -> GeneticOptimizationParameters:
"""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,
"genetic": {
"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 (in Euro per Wh)
strompreis_euro_pro_wh = np.full(48, 0.001)
strompreis_euro_pro_wh[0:10] = 0.00001
strompreis_euro_pro_wh[11:15] = 0.00005
strompreis_euro_pro_wh[20] = 0.00001
# Overall System Load (in W)
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,
]
# Start Solution (binary)
start_solution = None
# Define parameters for the optimization problem
return GeneticOptimizationParameters(
**{
"ems": {
"preis_euro_pro_wh_akku": 0e-05,
"einspeiseverguetung_euro_pro_wh": 7e-05,
"gesamtlast": gesamtlast,
"pv_prognose_wh": pv_forecast,
"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
},
"pv_akku": {
"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",
},
"eauto": {
"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 = GeneticOptimizationParameters(**json.load(f))
elif real_world:
parameters = 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,
genetic_parameters=parameters,
genetic_individuals=ngen,
genetic_seed=seed,
)
)
solution = ems_eos.genetic_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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