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EOS/tests/single_test_optimization.py
Bobby Noelte 6498c7dc32 Add database support for measurements and historic prediction data. (#848) 
The database supports backend selection, compression, incremental data load,
automatic data saving to storage, automatic vaccum and compaction.

Make SQLite3 and LMDB database backends available.

Update tests for new interface conventions regarding data sequences,
data containers, data providers. This includes the measurements provider and
the prediction providers.

Add database documentation.

The fix includes several bug fixes that are not directly related to the database
implementation but are necessary to keep EOS running properly and to test and
document the changes.

* fix: config eos test setup

  Make the config_eos fixture generate a new instance of the config_eos singleton.
  Use correct env names to setup data folder path.

* fix: startup with no config

  Make cache and measurements complain about missing data path configuration but
  do not bail out.

* fix: soc data preparation and usage for genetic optimization.

  Search for soc measurments 48 hours around the optimization start time.
  Only clamp soc to maximum in battery device simulation.

* fix: dashboard bailout on zero value solution display

  Do not use zero values to calculate the chart values adjustment for display.

* fix: openapi generation script

  Make the script also replace data_folder_path and data_output_path to hide
  real (test) environment pathes.

* feat: add make repeated task function

  make_repeated_task allows to wrap a function to be repeated cyclically.

* chore: removed index based data sequence access

  Index based data sequence access does not make sense as the sequence can be backed
  by the database. The sequence is now purely time series data.

* chore: refactor eos startup to avoid module import startup

  Avoid module import initialisation expecially of the EOS configuration.
  Config mutation, singleton initialization, logging setup, argparse parsing,
  background task definitions depending on config and environment-dependent behavior
  is now done at function startup.

* chore: introduce retention manager

  A single long-running background task that owns the scheduling of all periodic
  server-maintenance jobs (cache cleanup, DB autosave, …)

* chore: canonicalize timezone name for UTC

  Timezone names that are semantically identical to UTC are canonicalized to UTC.

* chore: extend config file migration for default value handling

  Extend the config file migration handling values None or nonexisting values
  that will invoke a default value generation in the new config file. Also
  adapt test to handle this situation.

* chore: extend datetime util test cases

* chore: make version test check for untracked files

  Check for files that are not tracked by git. Version calculation will be
  wrong if these files will not be commited.

* chore: bump pandas to 3.0.0

  Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit)
  for the output dtype which may become datetime64[us] (before it was ns). Also
  numeric dtype detection is now more strict which needs a different detection for
  numerics.

* chore: bump pydantic-settings to 2.12.0

  pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests
  were adapted and a workaround was introduced. Also ConfigEOS was adapted
  to allow for fine grain initialization control to be able to switch
  off certain settings such as file settings during test.

* chore: remove sci learn kit from dependencies

  The sci learn kit is not strictly necessary as long as we have scipy.

* chore: add documentation mode guarding for sphinx autosummary

  Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc
  mode.

* chore: adapt docker-build CI workflow to stricter GitHub handling

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2026-02-22 14:12:42 +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",
"provider_settings": {
"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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