EOS/single_test_optimization.py

#!/usr/bin/env python3

import time

import numpy as np

from akkudoktoreos.config import get_working_dir, load_config
from akkudoktoreos.optimization.genetic import (
    OptimizationParameters,
    optimization_problem,
)

start_hour = 0

# PV Forecast (in W)
pv_forecast = np.zeros(48)
pv_forecast[12] = 5000
# [
#     0,
#     0,
#     0,
#     0,
#     0,
#     0,
#     0,
#     8.05,
#     352.91,
#     728.51,
#     930.28,
#     1043.25,
#     1106.74,
#     1161.69,
#     1018.82,
#     1519.07,
#     1969.88,
#     1017.96,
#     1043.07,
#     1007.17,
#     319.67,
#     7.88,
#     0,
#     0,
#     0,
#     0,
#     0,
#     0,
#     0,
#     0,
#     0,
#     5.04,
#     335.59,
#     705.32,
#     1121.12,
#     1604.79,
#     2157.38,
#     1433.25,
#     5718.49,
#     4553.96,
#     3027.55,
#     2574.46,
#     1720.4,
#     963.4,
#     383.3,
#     0,
#     0,
#     0,
# ]

# 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
# [
#     0.0000384,
#     0.0000318,
#     0.0000284,
#     0.0008283,
#     0.0008289,
#     0.0008334,
#     0.0008290,
#     0.0003302,
#     0.0003042,
#     0.0002430,
#     0.0002280,
#     0.0002212,
#     0.0002093,
#     0.0001879,
#     0.0001838,
#     0.0002004,
#     0.0002198,
#     0.0002270,
#     0.0002997,
#     0.0003195,
#     0.0003081,
#     0.0002969,
#     0.0002921,
#     0.0002780,
#     0.0003384,
#     0.0003318,
#     0.0003284,
#     0.0003283,
#     0.0003289,
#     0.0003334,
#     0.0003290,
#     0.0003302,
#     0.0003042,
#     0.0002430,
#     0.0002280,
#     0.0002212,
#     0.0002093,
#     0.0001879,
#     0.0001838,
#     0.0002004,
#     0.0002198,
#     0.0002270,
#     0.0002997,
#     0.0003195,
#     0.0003081,
#     0.0002969,
#     0.0002921,
#     0.0002780,
# ]

# 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
parameters = OptimizationParameters(
    **{
        "ems": {
            # Value of energy in battery (per Wh)
            "preis_euro_pro_wh_akku": 0e-05,
            # Feed-in tariff for exporting electricity (per Wh)
            "einspeiseverguetung_euro_pro_wh": 7e-05,
            # Overall load on the system
            "gesamtlast": gesamtlast,
            # PV generation forecast (48 hours)
            "pv_prognose_wh": pv_forecast,
            # Electricity price forecast (48 hours)
            "strompreis_euro_pro_wh": strompreis_euro_pro_wh,
        },
        "pv_akku": {
            # Battery capacity (in Wh)
            "kapazitaet_wh": 26400,
            # Initial state of charge (SOC) of PV battery (%)
            "start_soc_prozent": 15,
            # Minimum Soc PV Battery
            "min_soc_prozent": 15,
        },
        "eauto": {
            # Minimum SOC for electric car
            "min_soc_prozent": 50,
            # Electric car battery capacity (Wh)
            "kapazitaet_wh": 60000,
            # Charging efficiency of the electric car
            "lade_effizienz": 0.95,
            # Charging power of the electric car (W)
            "max_ladeleistung_w": 11040,
            # Current SOC of the electric car (%)
            "start_soc_prozent": 5,
        },
        # "dishwasher": {
        #     # Household appliance consumption (Wh)
        #     "consumption_wh": 5000,
        #     # Duration of appliance usage (hours)
        #     "duration_h": 0,
        # },
        # Temperature forecast (48 hours)
        "temperature_forecast": temperature_forecast,
        # Initial solution for the optimization
        "start_solution": start_solution,
    }
)

# Startzeit nehmen
start_time = time.time()

# Initialize the optimization problem using the default configuration
working_dir = get_working_dir()
config = load_config(working_dir)
opt_class = optimization_problem(config, verbose=True, fixed_seed=42)

# Perform the optimisation based on the provided parameters and start hour
ergebnis = opt_class.optimierung_ems(parameters=parameters, start_hour=start_hour)

# Endzeit nehmen
end_time = time.time()

# Berechnete Zeit ausgeben
elapsed_time = end_time - start_time
print(f"Elapsed time: {elapsed_time:.4f} seconds")


print(ergebnis.model_dump())