EOS/test.py

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))