Optimization of pricing policy of electric vehicle charging station based on big data

Author(s): Xu Tiana, Yuan Xub, Xiangcheng Zhanga, Xue Maa, Su Zhangb
aEconomic & Technological Research Institute of State Grid Qinghai Power Co, 810000 XiNing, China
bState Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, 430074 WuHan, China
International Journal of Smart Grid and Clean Energy, vol. 9, no. 3, May 2020: pp. 596-606
ISSN: 2315-4462 (Print)
ISSN: 2373-3594 (Online)
Digital Object Identifier: 10.12720/sgce.9.3.596-606

Abstract: The guideless charging behaviour of electric vehicles(EVs) will lead to load fluctuation and increase the difficulty of power system control. By formulating a reasonable pricing strategy, the orderly charging behaviour of EVs can be fully encouraged, which is conducive to improving the stability of the system and the economic benefits of charging stations. Compared with the current pricing strategy optimization method, the pricing strategy based on big data will provide a research methodology to capture the relationship between charging load and pricing strategy. Firstly, by analysing EV user’s behaviour with big data, a charging load predicting model is proposed. Secondly, a causality model is built to study the relationship between pricing strategy and EV load. Thirdly, the optimization model about pricing strategy of charging station is established under three angles of minimum loss in power system, highest user satisfaction and maximum benefit of charging station. Then, the optimization problem solved by using particle swarm optimization(PSO). Finally, by taking the IEEE9 as an example, the validity of the EV charging load predicting model and the feasibility of the charging station pricing strategy optimization model are analysed and verified.

Keywords: Electric vehicle, load forecasting, big data, charging station pricing policy
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