feat: improve config backup and update and revert (#737)

Improve the backup of the EOS configuration on configuration migration
from another version. Backup files now get a backup id based on date
and time.

Add the configuration backup listing and the revert to the backup to
the EOS api.

Add revert to backup to the EOSdash admin tab.

Improve documentation about install, update and revert of EOS versions.

Add EOS execution profiling to make commands and to test description in
the development guideline.

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

tests/single_test_optimization.py

@@ -0,0 +1,526 @@
+#!/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.config.config import get_config
+from akkudoktoreos.core.ems import get_ems
+from akkudoktoreos.core.emsettings import EnergyManagementMode
+from akkudoktoreos.optimization.genetic.geneticparams import (
+    GeneticOptimizationParameters,
+)
+from akkudoktoreos.prediction.prediction import get_prediction
+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()