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EOS/tests/single_test_optimization.py
Bobby Noelte eb9e966de9 fix: move data management to async (#1015) 
FAstAPI is an async framework. Data may be imported and exported, load and save, set and get
asynchronously. Prevent interleaving data operations to corrupt the data. In the previous design
sync and async data access was intermixed leading to data corruption.

The basic data classes DataSequence and DataContainer and the derived classes like Provider and
Measurement now are async. Data access is protected by several async locks.

To support the async design of the data classes the database interface became async.

The energy management is also adapted to the new async design. Optimization is still off-loaded
to another thread, but the prepration for the optimization and the post optimization actions now
follow the async design.

Adapter operations are now also protected by async locks.

Tests were adapted to the async design and new tests were created.

Besides this major fix several other improvements and fixes are included in this PR.

* fix: key_to_dict/list/array only regard data records with key value set.

  Before the exclusion of no value data records was only done if the dropna flag was set.

* fix: test for visual result pdf generation

  Due to updates in the library the generated charts text was a little bit different.
  Adapt the test to create the comaprison pdf in the test data durectory and
  update the reference pdf.

* chore: Remove MutableMapping from DataSequence and DataContainer.

  Mutable Mapping does not fit to the now async design.

* chore: Add NoDB database backend

  This backend implements the full database backend interface but performs
  no actual persistence. It is intended for configurations where database
  persistence is disabled (`provider=None`).

* chore: Improve measurement data import testing with real world scenarios.

  Added two new endpoints to support testing.

* chore: Add mermaid to supported documentation tools

* chore: Add documentation about async design

* chore: Add documentation about generic data handling

  Covers the basics of measurement and prediction time series data handling.

* chore: Add empty lines around markdown lists.

* chore: sync pre-commit config to updated package versions

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2026-07-15 16:38:53 +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.genetic.geneticparams import (
GeneticOptimizationParameters,
)
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() -> 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,
"algorithm": "GENETIC",
"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}"
)
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 (in Euro per Wh)
strompreis_euro_pro_wh = await 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 = await 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,
"algorithm": "GENETIC",
"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 = 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,
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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