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single_test_optimization.py
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single_test_optimization.py
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#!/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,
OptimizeResponse,
optimization_problem,
)
from akkudoktoreos.utils import NumpyEncoder
from akkudoktoreos.visualize import visualisiere_ergebnisse
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")
ac_charge, dc_charge, discharge = (
ergebnis["ac_charge"],
ergebnis["dc_charge"],
ergebnis["discharge_allowed"],
)
visualisiere_ergebnisse(
parameters.ems.gesamtlast,
parameters.ems.pv_prognose_wh,
parameters.ems.strompreis_euro_pro_wh,
ergebnis["result"],
ac_charge,
dc_charge,
discharge,
parameters.temperature_forecast,
start_hour,
einspeiseverguetung_euro_pro_wh=np.full(
config.eos.feed_in_tariff_eur_per_wh, parameters.ems.einspeiseverguetung_euro_pro_wh
),
config=config,
)
json_data = NumpyEncoder.dumps(ergebnis)
print(json_data)
OptimizeResponse(**ergebnis)