mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-04-19 08:55:15 +00:00
96 lines
4.6 KiB
Python
96 lines
4.6 KiB
Python
#!/usr/bin/env python3
|
|
|
|
import numpy as np
|
|
from datetime import datetime
|
|
from pprint import pprint
|
|
import matplotlib.pyplot as plt
|
|
from deap import base, creator, tools, algorithms
|
|
|
|
# Import necessary modules from the project
|
|
from modules.class_optimize import optimization_problem
|
|
from modules.visualize import *
|
|
|
|
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 = [
|
|
1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
|
|
1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
|
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
|
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
|
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
|
|
]
|
|
|
|
# Define parameters for the optimization problem
|
|
parameter = {
|
|
"preis_euro_pro_wh_akku": 10e-05, # Cost of storing energy in battery (per Wh)
|
|
'pv_soc': 80, # Initial state of charge (SOC) of PV battery (%)
|
|
'pv_akku_cap': 26400, # Battery capacity (in Wh)
|
|
'year_energy': 4100000, # Yearly energy consumption (in Wh)
|
|
'einspeiseverguetung_euro_pro_wh': 7e-05, # Feed-in tariff for exporting electricity (per Wh)
|
|
'max_heizleistung': 1000, # Maximum heating power (in W)
|
|
"gesamtlast": gesamtlast, # Overall load on the system
|
|
'pv_forecast': pv_forecast, # PV generation forecast (48 hours)
|
|
"temperature_forecast": temperature_forecast, # Temperature forecast (48 hours)
|
|
"strompreis_euro_pro_wh": strompreis_euro_pro_wh, # Electricity price forecast (48 hours)
|
|
'eauto_min_soc': 0, # Minimum SOC for electric car
|
|
'eauto_cap': 60000, # Electric car battery capacity (Wh)
|
|
'eauto_charge_efficiency': 0.95, # Charging efficiency of the electric car
|
|
'eauto_charge_power': 11040, # Charging power of the electric car (W)
|
|
'eauto_soc': 54, # Current SOC of the electric car (%)
|
|
'pvpowernow': 211.137503624, # Current PV power generation (W)
|
|
'start_solution': start_solution, # Initial solution for the optimization
|
|
'haushaltsgeraet_wh': 937, # Household appliance consumption (Wh)
|
|
'haushaltsgeraet_dauer': 0 # Duration of appliance usage (hours)
|
|
}
|
|
|
|
# Initialize the optimization problem
|
|
opt_class = optimization_problem(prediction_hours=48, strafe=10, optimization_hours=24)
|
|
|
|
# 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)
|