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Dynamic Pricing Strategy of Charging Station Based on Traffic Assignment Simulation

Author

Listed:
  • Jiyuan Tan

    (Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China)

  • Fuyu Liu

    (Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China)

  • Na Xie

    (School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China)

  • Weiwei Guo

    (Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China)

  • Wenxiang Wu

    (Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China)

Abstract

The number of electric vehicles is increasing rapidly worldwide, leading to increasing demand for charging. This will negatively impact the grid. Therefore, it is essential to relieve the power grid operation pressure by changing the charging behaviour of users. In this paper, the charging behaviour of electric vehicles was guided by price instruments to maintain grid balance This paper uses travel simulation to establish the relationship between travel demand and electricity prices. The results were evaluated through the amount of grid voltage drop and network loss. Furthermore, we used the differential evolutionary algorithm to calculate the optimal operation status of the grid, which contains the minimal network loss and maximal voltage drop at different charging stations and the charging price. Finally, the effectiveness of the mechanism proposed in this paper was compared with other simulation examples. The results showed that the pricing strategy could guide users’ charging choices and maintain the grid load balancing. The simulation results show that the average bus voltage increases by 1.26% and 6.59%, respectively, under different requirements.

Suggested Citation

  • Jiyuan Tan & Fuyu Liu & Na Xie & Weiwei Guo & Wenxiang Wu, 2022. "Dynamic Pricing Strategy of Charging Station Based on Traffic Assignment Simulation," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14476-:d:963077
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    References listed on IDEAS

    as
    1. Rubino, Luigi & Capasso, Clemente & Veneri, Ottorino, 2017. "Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility," Applied Energy, Elsevier, vol. 207(C), pages 438-464.
    2. Dong, Xiaohong & Mu, Yunfei & Xu, Xiandong & Jia, Hongjie & Wu, Jianzhong & Yu, Xiaodan & Qi, Yan, 2018. "A charging pricing strategy of electric vehicle fast charging stations for the voltage control of electricity distribution networks," Applied Energy, Elsevier, vol. 225(C), pages 857-868.
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