A Systematic Data-driven Analysis of Electric Vehicle Electricity Consumption with Wind Power Integration

Abstract

Real-time charging data of Electric Vehicles (EVs) cannot be easily shared between service providers, making analysis of the energy profile is difficult of collective EVs. This paper uses a real-time dataset that analyzes real-world charging load profiles of EVs to the nearest 15 minutes for one day period. This dataset includes charging data from 21 EVs at different session times and different locations in a region. The data was systematically expanded to take advantage of the Wind Turbine (WT) generation power which is one of the Renewable Energy Sources (RES) in the charge energy consumption of collective EVs in modified bus-2 network of the Roy Billington Test System (RBTS). Instead of assuming that EVs were constantly charging at maximum power in creating a charge-load profile, collective charge-load profiles were simulated based on the actual charging at varying power. Simulation results show that EV charging peak loads can decrease with an onsite WT generation power. Thus, the load balancing was performed due to the wind energy conversion system instead of load shifting in the modeled power system.