Volume 01: Proceedings of Applied Energy Symposium: MIT A+B, United States, 2019

Simulating the Daily Profile of EV Charging Load based on User’s Travel Mode ZHANG Jing, LIU Yongqian, YAN Jie, LV Guoliang, YI Wenjing


Rapid development of electric vehicles (EVs) has a negative impact on the operating and planning of the power systems, and it is important to accurately simulate the EV charging load when large amount of EV charging demands are connected to the grid. In this paper, an EV charging load simulation method is proposed based on the users’ daily travel mode. Different from most of the previous works, this paper adds additional consideration of the charging preference, location type, day type and power consumption rate to improve the simulation accuracy. First, probabilistic distribution models for many defined spatialtemporal variables are established under refined conditions to improve the modelling accuracy, and the models include Burr Type XII, lognormal, generalized extreme, Weibull and Stable distribution, etc. Second, considering additional influential factors, daily profile of EV charging load is simulated based on the established distribution models and Monte Carlo algorithm. To take the public data from the US National Household Travel Survey (NHTS) as example, the proposed method is validated. The results show that the proposed method can provide more realistic daily curve of the charging load with no requirements on the historical charging data. And, the consideration of refined modelling conditions and additional influential factors can improve the accuracy of distribution models and the load simulation.

Keywords electric vehicle, charging load simulation, travel mode, charging preference, power consumption rate, Monte Carlo

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