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Update test.py

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- removed unused/duplicate imports
- organized parameters
- input values got rounded!
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NormannK 2024-09-19 22:00:09 +02:00 committed by Daniel Molkentin
parent 226608dc9e
commit 709211784b
1 changed files with 79 additions and 319 deletions
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test.py
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@ -1,335 +1,95 @@
from flask import Flask, jsonify, request
#!/usr/bin/env python3
import numpy as np
from datetime import datetime
from modules.class_optimize import *
# from modules.class_load import *
# from modules.class_ems import *
# from modules.class_pv_forecast import *
# from modules.class_akku import *
# from modules.class_strompreis import *
# from modules.class_heatpump import *
# from modules.class_load_container import *
# from modules.class_eauto import *
from modules.class_optimize import *
from pprint import pprint
import matplotlib.pyplot as plt
from modules.visualize import *
from deap import base, creator, tools, algorithms
import numpy as np
import random
import os
# Import necessary modules from the project
from modules.class_optimize import optimization_problem
from modules.visualize import *
start_hour = 10
pv_forecast= [
0,
0,
0,
0,
0,
0,
0,
8.05499380056326,
352.906710152794,
728.510230116837,
930.282113186742,
1043.25445504815,
1106.74498341506,
1161.69140358941,
6018.82237954771,
5519.06508185542,
3969.87633262384,
3017.96293205546,
1943.06957539177,
1007.17065928121,
319.672404988219,
7.87634136648885,
0,
0,
0,
0,
0,
0,
0,
0,
0,
5.04340865393592,
335.585179721385,
705.32093965119,
1121.11845108965,
1604.78796905453,
2157.38417470292,
1433.25331647539,
5718.48693381975,
4553.95522042393,
3027.5471975751,
2574.46499468404,
1720.39712914078,
963.402827741714,
383.299960578605,
0,
0,
0
]
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,
18.6,
19.2,
19.1,
18.7,
18.5,
17.7,
16.2,
14.6,
13.6,
13,
12.6,
12.2,
11.7,
11.6,
11.3,
11,
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,
17.4
]
# 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 °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 = [
0.00033840228,
0.00033180228,
0.00032840228,
0.00032830228,
0.00032890228,
0.00033340228,
0.00032900228,
0.00033020228,
0.00030420228,
0.00024300228,
0.00022800228,
0.00022120228,
0.00020930228,
0.00018790228,
0.00018380228,
0.00020040228,
0.00021980228,
0.00022700228,
0.00029970228,
0.00031950228,
0.00030810228,
0.00029690228,
0.00029210228,
0.00027800228,
0.00033840228,
0.00033180228,
0.00032840228,
0.00032830228,
0.00032890228,
0.00033340228,
0.00032900228,
0.00033020228,
0.00030420228,
0.00024300228,
0.00022800228,
0.00022120228,
0.00020930228,
0.00018790228,
0.00018380228,
0.00020040228,
0.00021980228,
0.00022700228,
0.00029970228,
0.00031950228,
0.00030810228,
0.00029690228,
0.00029210228,
0.00027800228
]
gesamtlast= [
676.712691350422,
876.187995931743,
527.13496018672,
468.8832716908,
531.379343927472,
517.948592590007,
483.146247717859,
472.284832630916,
1011.67951144825,
995.004317471209,
1053.06955100748,
1063.9080395892,
1320.56143113193,
1132.02504127723,
1163.67246837107,
1176.81613875329,
1216.21914051274,
1103.77675478374,
1129.12158352941,
1178.70748410006,
1050.97894301995,
988.55813665172,
912.383030600675,
704.613809064162,
516.371536532904,
868.049462163551,
694.342395302237,
608.791374542592,
556.310160150771,
488.88509383088,
506.910948217211,
804.891484351704,
1141.97850300923,
1056.97012155463,
992.46421110044,
1155.98941936038,
827.012550864246,
1257.97979633947,
1232.66876472966,
871.261677859026,
860.884647456424,
1158.02879027548,
1222.71811626233,
1221.03860924522,
949.989048056282,
987.007654562746,
733.993140774617,
592.972573276025
]
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
]
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
]
parameter= {"preis_euro_pro_wh_akku": 10e-05,'pv_soc': 80, 'pv_akku_cap': 26400, 'year_energy': 4100000, 'einspeiseverguetung_euro_pro_wh': 7e-05, 'max_heizleistung': 1000,"gesamtlast":gesamtlast, 'pv_forecast': pv_forecast, "temperature_forecast":temperature_forecast, "strompreis_euro_pro_wh":strompreis_euro_pro_wh, 'eauto_min_soc': 0, 'eauto_cap': 60000, 'eauto_charge_efficiency': 0.95, 'eauto_charge_power': 11040, 'eauto_soc': 54, 'pvpowernow': 211.137503624, 'start_solution': start_solution, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0}
# 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)
}
opt_class = optimization_problem(prediction_hours=48, strafe=10,optimization_hours=24)
# 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(ergebnis)
#print(jsonify(ergebnis))
# Print or visualize the result
pprint(ergebnis)