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Charging Stations Selection Using a Graph Convolutional Network from Geographic Grid

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  • Jianxin Qin

    (Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China
    School of Geographic Sciences, Hunan Normal University, Changsha 410081, China
    These authors contributed equally to this work.)

  • Jing Qiu

    (Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China
    School of Geographic Sciences, Hunan Normal University, Changsha 410081, China
    These authors contributed equally to this work.)

  • Yating Chen

    (State Grid Information & Communication Company of Hunan Electric Power Corporation, Changsha 410004, China)

  • Tao Wu

    (Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China
    School of Geographic Sciences, Hunan Normal University, Changsha 410081, China)

  • Longgang Xiang

    (State Key Laboratory of LIESMARS, Wuhan University, Wuhan 430079, China)

Abstract

Electric vehicles (EVs) have attracted considerable attention because of their clean and high-energy efficiency. Reasonably planning a charging station network has become a vital issue for the popularization of EVs. Current research on optimizing charging station networks focuses on the role of stations in a local scope. However, spatial features between charging stations are not considered. This paper proposes a charging station selection method based on the graph convolutional network (GCN) and establishes a charging station selection method considering traffic information and investment cost. The method uses the GCN to extract charging stations. The charging demand of each candidate station is calculated through the traffic flow information to optimize the location of charging stations. Finally, the cost of the charging station network is evaluated. A case study on charging station selection shows that the method can solve the EV charging station location problem.

Suggested Citation

  • Jianxin Qin & Jing Qiu & Yating Chen & Tao Wu & Longgang Xiang, 2022. "Charging Stations Selection Using a Graph Convolutional Network from Geographic Grid," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16797-:d:1003647
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    References listed on IDEAS

    as
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