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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 json
# Import necessary modules from the project
from akkudoktoreos.class_optimize import optimization_problem
start_hour = 10
# PV Forecast (in W)
pv_forecast = [
0,
0,
0,
0,
0,
0,
0,
8.05,
352.91,
728.51,
930.28,
1043.25,
1106.74,
1161.69,
6018.82,
5519.07,
3969.88,
3017.96,
1943.07,
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 = [
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,
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
parameter = {
# Cost of storing energy in battery (per Wh)
"preis_euro_pro_wh_akku": 10e-05,
# Initial state of charge (SOC) of PV battery (%)
"pv_soc": 80,
# Battery capacity (in Wh)
"pv_akku_cap": 26400,
# Yearly energy consumption (in Wh)
"year_energy": 4100000,
# Feed-in tariff for exporting electricity (per Wh)
"einspeiseverguetung_euro_pro_wh": 7e-05,
# Maximum heating power (in W)
"max_heizleistung": 1000,
# Overall load on the system
"gesamtlast": gesamtlast,
# PV generation forecast (48 hours)
"pv_forecast": pv_forecast,
# Temperature forecast (48 hours)
"temperature_forecast": temperature_forecast,
# Electricity price forecast (48 hours)
"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
# Minimum SOC for electric car
"eauto_min_soc": 80,
# Electric car battery capacity (Wh)
"eauto_cap": 60000,
# Charging efficiency of the electric car
"eauto_charge_efficiency": 0.95,
# Charging power of the electric car (W)
"eauto_charge_power": 11040,
# Current SOC of the electric car (%)
"eauto_soc": 5,
# Current PV power generation (W)
"pvpowernow": 211.137503624,
# Initial solution for the optimization
"start_solution": start_solution,
# Household appliance consumption (Wh)
"haushaltsgeraet_wh": 5000,
# Duration of appliance usage (hours)
"haushaltsgeraet_dauer": 2,
# Minimum Soc PV Battery
"min_soc_prozent": 15,
}
# Initialize the optimization problem
opt_class = optimization_problem(
prediction_hours=48, strafe=10, optimization_hours=24, verbose=True, fixed_seed=42
)
# Perform the optimisation based on the provided parameters and start hour
ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour)
# Print or visualize the result
# pprint(ergebnis)
json_data = json.dumps(ergebnis)
print(json_data)