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Research on electric vehicle charging load prediction and charging mode optimization

Zhang Zhiyan, Shi Hang, Zhu Ruihong, Zhao Hongfei, Zhu Yingjie

Archives of Electrical Engineering

2021

Vol. 70, nr 2

399--414

To reduce the influence of the disorderly charging of electric vehicles (EVs) on the grid load, the EV charging load and charging mode are studied in this paper. First, the distribution of EV charging capacity and state of charge (SOC) feature quantity are analyzed, and their probability density function is solved. It is verified that both EV charging capacity and SOC obey the skew-normal distribution. Second, considering the space-time distribution characteristics of the EV charging load, a method for charging load prediction based on a wavelet neural network is proposed, and compared with the traditional BP neural network, the prediction results show that the error of the wavelet neural network is smaller, and the effectiveness of the wavelet neural network prediction is verified. The optimization objective function with the lowest user costs is established, and the constraint conditions are determined, so the orderly charging behavior is simulated by the Monte Carlo method. Finally, the influence of charging mode optimization on power grid operation is analyzed, and the result shows that the effectiveness of the charging optimization model is verified.