arrival.clear()
depart.clear()
distance.clear()
demand.clear()
charge_power=np.empty([1,1])
installed_chargers.clear()
installed_cost.clear()
TFC=np.empty([1,1])
EV_samples.clear()
list_data.clear()
#create a scenario data
arrival, depart, distance, demand, charge_power,installed_chargers,\
installed_cost,TFC, EV_samples = dataFile(number_of_EVs,
number_of_timeslot,
Charger_Type,
charger_cost,
slot)
# arrival,
# depart,
# distance,
# demand,
# charge_power,
# installed_chargers,
# installed_cost,
# TFC,
# EV_samples)
"""
Calling the model creator function based on generated data
number_of_scenarios = 100
number_of_timeslot = 24 * slot
Charger_Type = [4, 8, 19] #type of chargers to install
charger_cost = [1000, 1500, 2200] #cost of installation
"""
Initializing required data for creating scenario files
"""
#Find the max number of requiredd chargers as CS capacity
count = np.zeros(len(Charger_Type), dtype=np.int8)
for i in range(500):
installed_chargers = dataFile(number_of_EVs, number_of_timeslot,
Charger_Type, charger_cost, slot)
for i in range(len(Charger_Type)):
max_i = installed_chargers.count(Charger_Type[i])
if count[i] < max_i:
count[i] = max_i
#using next two variables for creating scenarios data files
number_of_Chargers = sum(count)
chargers_cost = []
installed_chargers = []
for i in range(len(Charger_Type)):
for j in range(count[i]):
chargers_cost.append(charger_cost[i])
installed_chargers.append(Charger_Type[i])