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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

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    1. Xiang, Yue & Liu, Junyong & Li, Ran & Li, Furong & Gu, Chenghong & Tang, Shuoya, 2016. "Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates," Applied Energy, Elsevier, vol. 178(C), pages 647-659.
    2. Tran, Cong Quoc & Keyvan-Ekbatani, Mehdi & Ngoduy, Dong & Watling, David, 2021. "Stochasticity and environmental cost inclusion for electric vehicles fast-charging facility deployment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    3. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2019. "A four-step method for electric-vehicle charging facility deployment in a dense city: An empirical study in Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 224-237.
    4. Quddus, Md Abdul & Kabli, Mohannad & Marufuzzaman, Mohammad, 2019. "Modeling electric vehicle charging station expansion with an integration of renewable energy and Vehicle-to-Grid sources," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 251-279.
    5. Wang, Hua & Zhao, De & Cai, Yutong & Meng, Qiang & Ong, Ghim Ping, 2021. "Taxi trajectory data based fast-charging facility planning for urban electric taxi systems," Applied Energy, Elsevier, vol. 286(C).
    6. Andrenacci, N. & Ragona, R. & Valenti, G., 2016. "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas," Applied Energy, Elsevier, vol. 182(C), pages 39-46.
    7. Xu, Min & Meng, Qiang, 2020. "Optimal deployment of charging stations considering path deviation and nonlinear elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 120-142.
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    Cited by:

    1. Jiang, Ziyue & Han, Jingzuo & Li, Yetong & Chen, Xinyu & Peng, Tianduo & Xiong, Jianliang & Shu, Zhan, 2023. "Charging station layout planning for electric vehicles based on power system flexibility requirements," Energy, Elsevier, vol. 283(C).

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