from flask import Flask, jsonify, request 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 start_hour = 13 pv_forecast= [ 0, 0, 0, 0, 0, 0, 0, 35.4104640357043, 436.191574979506, 734.585613834398, 914.346108603927, 1019.5228214119, 1766.84136350058, 5980.60975052259, 6236.00681862336, 5893.38154543782, 4309.88538120413, 3338.29004915145, 2177.55660706753, 1091.00542545193, 437.819525591319, 44.2226537829726, 0, 0, 0, 0, -0.0269415125679914, 0, 0, 0, 0, 25.5745140893473, 494.188146846569, 943.821134036728, 1458.66413119635, 1819.46147983229, 2127.45430524539, 2267.78128099068, 5944.86706099518, 5337.1322153025, 4376.56125932204, 3020.00212091936, 2414.53994231359, 1373.626161377, 517.764497317134, 35.619750070296, 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 ] 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= [ 546.16318964697, 893.072526185525, 448.7325491406, 460.696954446666, 497.688171532182, 468.186120420737, 424.440426628658, 454.341890696582, 1070.45287392313, 1096.46234344204, 1199.71317588613, 1294.39989535284, 1459.42631059004, 1295.23757474948, 1304.65748778424, 1187.47511606455, 1309.49984671163, 1106.60773651081, 1098.98136451936, 2112.82264661039, 1143.37118921705, 858.863135790621, 787.018517493612, 693.683533270357, 545.860858342826, 892.702692835489, 448.372058076642, 460.284228901714, 498.457870099476, 469.01826331988, 424.293997897019, 454.660942633609, 1070.71990586461, 1096.44557410693, 1199.72027861112, 1294.75410706442, 1459.61174223338, 1295.77661554687, 1304.95591385395, 1188.62778631227, 1310.43099742786, 1108.58589249073, 1101.73849714744, 2114.05576978017, 1143.68031998738, 858.607350786608, 786.574111043611, 693.463415886943 ] start_solution= [ 0, 1, 0, 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": 30e-05,'pv_soc': 95.00, 'pv_akku_cap': 30000, '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': 7590, 'eauto_soc': 53, 'pvpowernow': 211.137503624, 'start_solution': start_solution, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0} opt_class = optimization_problem(prediction_hours=48, strafe=10,optimization_hours=24) ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour) print(ergebnis) print(jsonify(ergebnis))