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Electric Vehicle Participation in Regional Grid Demand Response: Potential Analysis Model and Architecture Planning

Author

Listed:
  • Qian Wang

    (School of Economics and Management, Jilin Institute of Chemical Technology, Jilin City 132022, China)

  • Xiaolong Yang

    (School of Economics and Management, Northeast Electric Power University, Jilin City 132012, China)

  • Xiaoyu Yu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Jingwen Yun

    (School of Economics and Management, Northeast Electric Power University, Jilin City 132012, China)

  • Jinbo Zhang

    (School of Economics and Management, Northeast Electric Power University, Jilin City 132012, China)

Abstract

When a large-scale random charging load is connected to the regional power grid, it can negatively affect the safe and stable operation of the power grid. Therefore, we need to study its charging load and response potential in advance so that electric vehicles can interact well with the grid after being connected to the regional grid. Firstly, after analyzing the influencing factors of regional electric vehicle ownership, an electric vehicle ownership prediction model based on the sparrow search algorithm-improved BP neural network (SSA-BPNN) is established. On this basis, an electric vehicle charging load prediction model is established based on the sparrow search algorithm-improved BP neural network and Monte Carlo algorithm (SSA-BPNN-MC). Secondly, the charging behavior of different types of electric vehicles is analyzed and modeled, and the data from a certain area are taken as an example for the prediction. Then, according to the load forecasting results, the potential of electric vehicles participating in demand response in the region in the future is deeply analyzed using the scenario analysis method. Finally, with the aim of resolving the problems of massive multi-source heterogeneous data processing and the management of electric vehicles participating in the regional power grid demand response, a basic framework of electric vehicles participating in the regional power grid demand response is developed, which provides effective support for promoting electric vehicles to participate in regional grid demand response.

Suggested Citation

  • Qian Wang & Xiaolong Yang & Xiaoyu Yu & Jingwen Yun & Jinbo Zhang, 2023. "Electric Vehicle Participation in Regional Grid Demand Response: Potential Analysis Model and Architecture Planning," Sustainability, MDPI, vol. 15(3), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2763-:d:1056326
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    1. Minan Tang & Changyou Wang & Jiandong Qiu & Hanting Li & Xi Guo & Wenxin Sheng, 2024. "Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models," Energies, MDPI, vol. 17(12), pages 1-19, June.
    2. Haihong Bian & Quance Ren & Zhengyang Guo & Chengang Zhou & Zhiyuan Zhang & Ximeng Wang, 2024. "Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation," Energies, MDPI, vol. 17(11), pages 1-23, May.

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