Load Profile Simulations for Smart Charging of Electric Vehicles Considering Driving Behavior and Vehicle Performance

Abstract

Shifting from internal combustion vehicles (ICE) to electric vehicles (EVs) has a positive effect on global warming and emissions. However, the rise of EVs can create intermittent, high charging demands. For this reason, the penetration of EVs affects the power system in terms of congestion and rapid changes in power demand. This study aims to develop a Monte Carlo simulation-based algorithm to simulate the driving and charging behavior of EVs for estimating charging power demand based on the data from surveys and research reports. In addition, smart charging control algorithms, also known as direct charging control, are proposed based on previous charging duration and state of charge. The study results show load profile forecasting for some certain percentages of EV adoption in an area of interest and the ability of the developed smart scheduling algorithms to mitigate the impact of extremely high demand during charging congestion periods.