1.State Key Laboratory of Reliability and Intelligence of Electrical Equipment (Hebei University of Technology), Tianjin 300131, China;2.Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211130, China;3.Key Laboratory of Ministry of Education on Smart Power Grids (Tianjin University), Tianjin 300072, China

As the market share of electric vehicles continues to grow annually, the proportion of electric vehicle charging load limited by the capacity of power battery in the region to the total electricity load continues to increase. To this end, this paper proposes a medium- and long-term charging load forecasting method for electric vehicles considering battery aging. First, a total capacity forecasting model for electric vehicle batteries in the rigion considering battery aging is constructed to forecast the total capacity attenuation caused by battery aging, as well as the total capacity increase caused by the replacement with new batteries and the growth in the number of the vehicles. Then, an estimation model for the aging characteristics of power batteries considering temperature changes is constructed to estimate the maximum capacity, rechargeable capacity and driving range of the battery after each charging cycle. When the maximum capacity of the battery is insufficient to ensure the safe operation of the vehicle, it is replaced with a new one. Finally, a vehicle behavior simulation model considering charging costs is constructed to simulate both vehicle travel and charging processes. By using the fuzzy C-means method to partition different kinds of charging loads of electric vehicles, the medium- and long-term loads of electric vehicles in the region are determined through Monte Carlo simulation and linear weighting. The simulation results show that as the vehicle usage time increases, the fluctuation degree of the annual load curve continues to increase. Compared with new vehicles, after battery aging, the peak time of weekly electricity load of single vehicle will be earlier, and the peak-to-valley differences of loads will also increase.