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# -*- coding: utf-8 -*-
"""
Created on Thu Apr 18 20:02:10 2019
@author: ogpg0_000
"""
import numpy as np
import random
import os
#%%
def Prob_Make_up(y):
while (float(format(sum(y),'.8f')) > 1.00000001) or (float(format(sum(y),'.8f')) < 0.99999999):
if sum(y) < 1:
ajust = (1-sum(y))/len(y)
y = y + ajust*np.ones(len(y))
else:
ajust = (sum(y)-1)/len(y)
y = y - ajust*np.ones(len(y))
return y
def car_selection_day_1(total_cars, av_cars_day_1): #RANDOM SELECTION OF CARS WITH THE RESPECTIVE AV PER DAY
import pandas as pd
av_cars_sel = random.sample(set(range(total_cars)),av_cars_day_1) #select randomlya number of cars depending on the probability
av_cars_list = total_cars*[0] # 0 is not available
if len(av_cars_sel) > 0:
for i in range(len(av_cars_sel)):
av_cars_list[av_cars_sel[i]] = 1 # 1 is available
return(av_cars_list)
#This function convert a csv into a numpy vector
def csv_numpy( csv_name, nombre_columna ):
import pandas as pd
dataframe = pd.read_csv( csv_name )
dato_real = dataframe[ nombre_columna ]
my_data = dato_real.to_numpy()
return my_data
def Random_Events(total_cars, events_ev_disp, power_val, kwh_val, fixed_day_prob, fixed_day):
dir_path = os.path.dirname(os.path.realpath(__file__))
print( "dir_path = ", dir_path )
dato1 = csv_numpy( dir_path + "/Probabilidades/dato1.csv", "Valor")
dato2 = csv_numpy( dir_path + "/Probabilidades/dato2.csv", "Valor")
dato3 = csv_numpy( dir_path + "/Probabilidades/dato3.csv", "Valor")
dato4 = csv_numpy( dir_path + "/Probabilidades/dato4.csv", "Valor")
dato5 = csv_numpy( dir_path + "/Probabilidades/dato5.csv", "Valor")
dato6 = csv_numpy( dir_path + "/Probabilidades/dato6.csv", "Valor")
dato7 = csv_numpy( dir_path + "/Probabilidades/dato7.csv", "Valor")
dato8 = csv_numpy( dir_path + "/Probabilidades/dato8.csv", "Valor")
dato9 = csv_numpy( dir_path + "/Probabilidades/dato9.csv", "Valor")
dato9 = dato9[0]
dato11 = csv_numpy( dir_path + "/Probabilidades/dato11.csv", "Valor")
dato11 = dato11[0]
"""
Original
dato1 = weekday_dict['time']['gmm']['valores'][2]
dato2 = weekend_dict['time']['gmm']['valores'][1]
dato3 = weekday_dict['SoC']['gmm']['valores'][2]
dato4 = weekend_dict['SoC']['gmm']['valores'][2]
dato5 = weekday_dict['duration']['gmm']['valores'][1]
dato6 = weekend_dict['duration']['gmm']['valores'][2]
dato7 = general_dict['charge_demand']['histogram'][0]
dato8 = general_dict['battery_cap']['histogram'][0]
dato9 = weekday_dict['duration']['histogram'][1][0]
dato11 = weekend_dict['duration']['histogram'][1][0]
"""
#Availability start time probabilities
wd_prob_av_start_1 = Prob_Make_up( dato1 )
we_prob_av_start_1 = Prob_Make_up( dato2 )
#Availability initial SOC probabilities
wd_prob_SOC_1 = Prob_Make_up( dato3 )
we_prob_SOC_1 = Prob_Make_up( dato4 )
#Availability duration probabilities
wd_prob_duration_1 = Prob_Make_up( dato5 )
we_prob_duration_1 = Prob_Make_up( dato6 )
#Availability duration probabilities
w_prob_power_1 = Prob_Make_up( dato7 ) #POR MIENTRAS SE CONSIGUEN LOS DATOS
w_prob_kwh_1 = Prob_Make_up( dato8 ) #POR MIENTRAS SE CONSIGUEN LOS DATOS
########################################################################################################################
#Here we go:
prev_av_random_events = {}
# day of the week: 0 if it is monday and 6 if it is sunday
if fixed_day is False:
day_1 = np.random.choice(range(0,7))
else:
day_1 = fixed_day
# PROBABILITY DEFINITION PER DAY
prob_day_1 = fixed_day_prob
if day_1 < 5:
av_cars_day_1 = int(np.round(total_cars*prob_day_1*10/100))
av_car_list_day_1 = car_selection_day_1(total_cars,av_cars_day_1) # Random selection of cars
else:
av_cars_day_1 = int(np.round(total_cars*prob_day_1*10/100))
av_car_list_day_1 = car_selection_day_1(total_cars,av_cars_day_1) # Random selection of cars
for i in range(1,total_cars+1):
prev_av_random_events[i] = {'reps_per_day':[],'av_start':[],'SOC':[],'duration':[], 'charge_demand':[], 'battery_cap':[]}
day_before_const = total_cars*[0] #
#Number of availability events per day
for i in range(total_cars):
###################################################### day 1 ######################################################
if av_car_list_day_1[i] == 1:
if day_1 < 5:
prev_av_random_events[i+1]['reps_per_day'] = 1
prev_av_random_events[i+1]['duration'].append(int(np.random.choice(range( dato9, dato9 + len(wd_prob_duration_1)), p = wd_prob_duration_1)/15))
prev_av_random_events[i+1]['av_start'].append(np.random.choice(range(0,96), p = wd_prob_av_start_1))
prev_av_random_events[i+1]['SOC'].append(np.random.choice(range(0,101), p = wd_prob_SOC_1))
prev_av_random_events[i+1]['charge_demand'].append(np.random.choice(power_val, p = w_prob_power_1))
prev_av_random_events[i+1]['battery_cap'].append(np.random.choice(kwh_val, p = w_prob_kwh_1))
day_before_const[i] = (prev_av_random_events[i+1]['duration'][0] + prev_av_random_events[i+1]['av_start'][0])-96
##################################################### WEEKEND #######################################################
else:
prev_av_random_events[i+1]['reps_per_day'] = 1
prev_av_random_events[i+1]['duration'].append(int(np.random.choice(range( dato11, dato11 + len(we_prob_duration_1)), p = we_prob_duration_1)/15))
prev_av_random_events[i+1]['av_start'].append(np.random.choice(range(0,96), p = we_prob_av_start_1))
prev_av_random_events[i+1]['SOC'].append(np.random.choice(range(0,101), p = we_prob_SOC_1))
prev_av_random_events[i+1]['charge_demand'].append(np.random.choice(power_val, p = w_prob_power_1))
prev_av_random_events[i+1]['battery_cap'].append(np.random.choice(kwh_val, p = w_prob_kwh_1))
day_before_const[i] = (prev_av_random_events[i+1]['duration'][0] + prev_av_random_events[i+1]['av_start'][0])-96
else:
prev_av_random_events[i+1]['reps_per_day'] = 0
#################################### FIN DEL CICLO DEL DÍA 1 ####################################
def After_day_Events(prev_av_random_events,day_before_const):
import pandas as pd
av_random_events = {}
for i in range(1,len(day_before_const)+1):
av_random_events[i] = {'reps_per_day': [], 'av_start':[],'SOC':[],'duration':[], 'charge_demand':[]}
def SOC_at_24_calc(bef_av_start, SOC_ini, power): #SOC value calculation at 00:00
av_dur_bef_24 = 96 - bef_av_start
units = (power/4)*av_dur_bef_24 #Con base a una simulación cada 15 minutos
if SOC_ini+units > 100:
SOC_at_24 = 100
else:
SOC_at_24 = SOC_ini+units
return(SOC_at_24)
for i in range(len(day_before_const)):
av_random_events[i+1]['reps_per_day'] = prev_av_random_events[i+1]['reps_per_day']
av_random_events[i+1]['charge_demand'] = prev_av_random_events[i+1]['charge_demand']
av_random_events[i+1]['battery_cap'] = prev_av_random_events[i+1]['battery_cap']
if day_before_const[i] > 0:
#CASE 1
if day_before_const[i] < prev_av_random_events[i+1]['av_start'][0]: #if the two events don´t coincide
av_random_events[i+1]['av_start'].append(0)
av_random_events[i+1]['duration'].append(day_before_const[i])
SOC_at_24 = SOC_at_24_calc(prev_av_random_events[i+1]['av_start'][-1], prev_av_random_events[i+1]['SOC'][-1], prev_av_random_events[i+1]['charge_demand'][-1])
av_random_events[i+1]['SOC'].append(SOC_at_24)
for j in range(len(prev_av_random_events[i+1]['av_start'])):
av_random_events[i+1]['av_start'].append(prev_av_random_events[i+1]['av_start'][j])
av_random_events[i+1]['SOC'].append(prev_av_random_events[i+1]['SOC'][j])
if j == len(prev_av_random_events[i+1]['av_start'])-1: #If it's the last in the list
av_random_events[i+1]['duration'].append(95-prev_av_random_events[i+1]['av_start'][j])
else:
av_random_events[i+1]['duration'].append(int(prev_av_random_events[i+1]['duration'][j]/15))
#CASE 2
elif day_before_const[i] > prev_av_random_events[i+1]['av_start'][0]: #if the two events coincide
av_random_events[i+1]['av_start'].append(0)
av_random_events[i+1]['duration'].append(prev_av_random_events[i+1]['av_start'][0]-1) #1 minute before
SOC_at_24 = SOC_at_24_calc(prev_av_random_events[i+1]['av_start'][-1], prev_av_random_events[i+1]['SOC'][-1], prev_av_random_events[i+1]['charge_demand'][-1])
av_random_events[i+1]['SOC'].append(SOC_at_24)
for j in range(len(prev_av_random_events[i+1]['av_start'])):
av_random_events[i+1]['av_start'].append(prev_av_random_events[i+1]['av_start'][j])
av_random_events[i+1]['SOC'].append(prev_av_random_events[i+1]['SOC'][j])
if j == len(prev_av_random_events[i+1]['av_start'])-1: #If it's the last in the list
av_random_events[i+1]['duration'].append(95-prev_av_random_events[i+1]['av_start'][j])
else:
av_random_events[i+1]['duration'].append(int(prev_av_random_events[i+1]['duration'][j])/15)
else:
av_random_events[i+1] = prev_av_random_events[i+1]
return av_random_events
av_random_events = After_day_Events(prev_av_random_events,day_before_const)
#return(av_random_events, day_before_const, av_car_list_day_1, av_car_list_day_2, av_cars_day_1, av_cars_day_2)
return(av_random_events, prob_day_1, day_1)