LAUSR

Abstract Travel pattern of an electric vehicle (EV) user and the accuracy of their probability distribution models are the key factors affecting the simulation and prediction of EV charging load. Most of the existing works utilized the travel data for all kinds of populations and ignored the influence of people social attributes on their travel pattern, which deteriorates the accuracy of the charging load model. This paper demonstrates that the daily EV charging load profiles vary with different demographic and social attributes by presenting a refined EV charging load simulation method considering people’s demographics and social characteristics, e.g. gender, age, education level. First, to improve the fitting accuracy of people travel pattern, new probabilistic models of many defined spatial–temporal variables are established under refined conditions (i.e. location, day type, etc.). Second, additional factors (i.e. charging preference, power consumption rate, etc.) are included to simulate the daily profile of EV charging load based on the refined probabilistic models and Monte Carlo algorithm. Data from the US National Household Travel Survey are used to validate the proposed method. The results show that the user's demographic and social attributes have a considerable effect on the magnitude and peak time of the EV charging load profile, particularly for workdays and workplace. The proposed probabilistic models can improve the accuracy of the data fitting and the charging load simulation.