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Charging Network Planning for Electric Bus Cities: A Case Study of Shenzhen, China

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  • Yuping Lin

    (Center of Environmental Science & New Energy Technology, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
    Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720, USA)

  • Kai Zhang

    (Center of Environmental Science & New Energy Technology, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
    Division of Logistics and Transportation, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Zuo-Jun Max Shen

    (Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720, USA
    Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA)

  • Lixin Miao

    (Center of Environmental Science & New Energy Technology, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
    Division of Logistics and Transportation, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

Abstract

In 2017, Shenzhen replaced all its buses with battery e-buses (electric buses) and has become the first all-e-bus city in the world. Systematic planning of the supporting charging infrastructure for the electrified bus transportation system is required. Considering the number of city e-buses and the land scarcity, large-scale bus charging stations were preferred and adopted by the city. Compared with other EVs (electric vehicles), e-buses have operational tasks and different charging behavior. Since large-scale electricity-consuming stations will result in an intense burden on the power grid, it is necessary to consider both the transportation network and the power grid when planning the charging infrastructure. A cost-minimization model to jointly determine the deployment of bus charging stations and a grid connection scheme was put forward, which is essentially a three-fold assignment model. The problem was formulated as a mixed-integer second-order cone programming model, and a “No R” algorithm was proposed to improve the computational speed further. Computational studies, including a case study of Shenzhen, were implemented and the impacts of EV technology advancements on the cost and the infrastructure layout were also investigated.

Suggested Citation

  • Yuping Lin & Kai Zhang & Zuo-Jun Max Shen & Lixin Miao, 2019. "Charging Network Planning for Electric Bus Cities: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 11(17), pages 1-27, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4713-:d:262125
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    References listed on IDEAS

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    Cited by:

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    5. Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    6. Tal, Gil & Benoliel, Peter K & Garcia Sanchez, Juan Carlos & Hernandez Rios, Kevin, 2023. "Exploring Tools for Maximizing the Potential for Electrified Transit Buses in Mexico," Institute of Transportation Studies, Working Paper Series qt5nv441q2, Institute of Transportation Studies, UC Davis.
    7. Foda, Ahmed & Abdelaty, Hatem & Mohamed, Moataz & El-Saadany, Ehab, 2023. "A generic cost-utility-emission optimization for electric bus transit infrastructure planning and charging scheduling," Energy, Elsevier, vol. 277(C).
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