IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p8957-d611899.html
   My bibliography  Save this article

Location and Expansion of Electric Bus Charging Stations Based on Gridded Affinity Propagation Clustering and a Sequential Expansion Rule

Author

Listed:
  • Yajun Zhang

    (School of Business Administration, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Jie Deng

    (Intellectual Property Institute of Chongqing, Chongqing University of Technology, Chongqing 400054, China)

  • Kangkang Zhu

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China)

  • Yongqiang Tao

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China)

  • Xiaolin Liu

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China)

  • Ligang Cui

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract

With the escalating contradiction between the growing demand for electric buses and limited supporting resources of cities to deploy electric charging infrastructure, it is a great challenge for decision-makers to synthetically plan the location and decide on the expansion sequence of electric charging stations. In light of the location decisions of electric charging stations having long-term impacts on the deployment of electric buses and the layout of city traffic networks, a comprehensive framework for planning the locations and deciding on the expansion of electric bus charging stations should be developed simultaneously. In practice, construction or renovation of a new charging station is limited by various factors, such as land resources, capital investment, and power grid load. Thus, it is necessary to develop an evaluation structure that combines these factors to provide integrated decision support for the location of bus charging stations. Under this background, this paper develops a gridded affinity propagation (AP) clustering algorithm that combines the superiorities of the AP clustering algorithm and the map gridding rule to find the optimal candidate locations for electric bus charging stations by considering multiple impacting factors such as land cost, traffic conditions, and so on. Based on the location results of the candidate stations, the expansion sequence of these candidate stations is proposed. In particular, a sequential expansion rule for planning the charging stations is proposed that considers the development trends of the charging demand. To verify the performance of the gridded AP clustering and the effectiveness of the proposed sequential expansion rule, an empirical investigation of Guiyang City, the capital of Guizhou province in China, is conducted. The results of the empirical investigation demonstrate that the proposed framework that helps find optimal locations for electric bus charging stations and the expansion sequence of these locations are decided with less capital investment pressure. This research shows that the combination of gridded AP clustering and the proposed sequential expansion rule can systematically solve the problem of finding the optimal locations and deciding on the best expansion sequence for electric bus charging stations, which denotes that the proposed structure is pretty pragmatic and would benefit the government for long-term investment in electric bus station deployment.

Suggested Citation

  • Yajun Zhang & Jie Deng & Kangkang Zhu & Yongqiang Tao & Xiaolin Liu & Ligang Cui, 2021. "Location and Expansion of Electric Bus Charging Stations Based on Gridded Affinity Propagation Clustering and a Sequential Expansion Rule," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:8957-:d:611899
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/8957/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/16/8957/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kabli, Mohannad & Quddus, Md Abdul & Nurre, Sarah G. & Marufuzzaman, Mohammad & Usher, John M., 2020. "A stochastic programming approach for electric vehicle charging station expansion plans," International Journal of Production Economics, Elsevier, vol. 220(C).
    2. Geng, Zhiqiang & Zeng, Rongfu & Han, Yongming & Zhong, Yanhua & Fu, Hua, 2019. "Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries," Energy, Elsevier, vol. 179(C), pages 863-875.
    3. He, Fang & Wu, Di & Yin, Yafeng & Guan, Yongpei, 2013. "Optimal deployment of public charging stations for plug-in hybrid electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 87-101.
    4. Wang, Yusheng & Huang, Yongxi & Xu, Jiuping & Barclay, Nicole, 2017. "Optimal recharging scheduling for urban electric buses: A case study in Davis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 115-132.
    5. Gao, Shangce & Wang, Yirui & Cheng, Jiujun & Inazumi, Yasuhiro & Tang, Zheng, 2016. "Ant colony optimization with clustering for solving the dynamic location routing problem," Applied Mathematics and Computation, Elsevier, vol. 285(C), pages 149-173.
    6. Yue Xiang & Wei Yang & Junyong Liu & Furong Li, 2016. "Multi-Objective Distribution Network Expansion Incorporating Electric Vehicle Charging Stations," Energies, MDPI, vol. 9(11), pages 1-17, November.
    7. Liu, Tao & Ceder, Avishai (Avi), 2015. "Analysis of a new public-transport-service concept: Customized bus in China," Transport Policy, Elsevier, vol. 39(C), pages 63-76.
    8. Mahmoud, Moataz & Garnett, Ryan & Ferguson, Mark & Kanaroglou, Pavlos, 2016. "Electric buses: A review of alternative powertrains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 673-684.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Renata Żochowska & Marcin Jacek Kłos & Piotr Soczówka & Marcin Pilch, 2022. "Assessment of Accessibility of Public Transport by Using Temporal and Spatial Analysis," Sustainability, MDPI, vol. 14(23), pages 1-29, December.
    2. Yu Feng & Xiaochun Lu, 2021. "Construction Planning and Operation of Battery Swapping Stations for Electric Vehicles: A Literature Review," Energies, MDPI, vol. 14(24), pages 1-19, December.
    3. Wang, Ning & Tian, Hangqi & Wu, Huahua & Liu, Qiaoqian & Luan, Jie & Li, Yuan, 2023. "Cost-oriented optimization of the location and capacity of charging stations for the electric Robotaxi fleet," Energy, Elsevier, vol. 263(PC).
    4. Adrian Barchański & Renata Żochowska & Marcin Jacek Kłos, 2022. "A Method for the Identification of Critical Interstop Sections in Terms of Introducing Electric Buses in Public Transport," Energies, MDPI, vol. 15(20), pages 1-37, October.
    5. Kłos, Marcin Jacek & Sierpiński, Grzegorz, 2023. "Siting of electric vehicle charging stations method addressing area potential and increasing their accessibility," Journal of Transport Geography, Elsevier, vol. 109(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Xiaolei & Miao, Ran & Wu, Xinkai & Liu, Xianglong, 2021. "Examining influential factors on the energy consumption of electric and diesel buses: A data-driven analysis of large-scale public transit network in Beijing," Energy, Elsevier, vol. 216(C).
    2. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    3. Wei, Ran & Liu, Xiaoyue & Ou, Yi & Kiavash Fayyaz, S., 2018. "Optimizing the spatio-temporal deployment of battery electric bus system," Journal of Transport Geography, Elsevier, vol. 68(C), pages 160-168.
    4. Boud Verbrugge & Mohammed Mahedi Hasan & Haaris Rasool & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2021. "Smart Integration of Electric Buses in Cities: A Technological Review," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    5. Jean-Michel Clairand & Paulo Guerra-Terán & Xavier Serrano-Guerrero & Mario González-Rodríguez & Guillermo Escrivá-Escrivá, 2019. "Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies," Energies, MDPI, vol. 12(16), pages 1-22, August.
    6. He, Yi & Liu, Zhaocai & Song, Ziqi, 2020. "Optimal charging scheduling and management for a fast-charging battery electric bus system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    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).
    8. Kayhan Alamatsaz & Sadam Hussain & Chunyan Lai & Ursula Eicker, 2022. "Electric Bus Scheduling and Timetabling, Fast Charging Infrastructure Planning, and Their Impact on the Grid: A Review," Energies, MDPI, vol. 15(21), pages 1-39, October.
    9. Masmoudi, Mohamed Amine & Hosny, Manar & Demir, Emrah & Genikomsakis, Konstantinos N. & Cheikhrouhou, Naoufel, 2018. "The dial-a-ride problem with electric vehicles and battery swapping stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 392-420.
    10. Hatem Abdelaty & Ahmed Foda & Moataz Mohamed, 2023. "The Robustness of Battery Electric Bus Transit Networks under Charging Infrastructure Disruptions," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    11. Feifeng Zheng & Zhixin Wang & Zhaojie Wang & Ming Liu, 2023. "Daytime and Overnight Joint Charging Scheduling for Battery Electric Buses Considering Time-Varying Charging Power," Sustainability, MDPI, vol. 15(13), pages 1-19, July.
    12. Foda, Ahmed & Mohamed, Moataz, 2024. "The impacts of optimization approaches on BEB system configuration in transit," Transport Policy, Elsevier, vol. 151(C), pages 12-23.
    13. He, Yi & Liu, Zhaocai & Zhang, Yiming & Song, Ziqi, 2023. "Time-dependent electric bus and charging station deployment problem," Energy, Elsevier, vol. 282(C).
    14. Scarinci, Riccardo & Zanarini, Alessandro & Bierlaire, Michel, 2019. "Electrification of urban mobility: The case of catenary-free buses," Transport Policy, Elsevier, vol. 80(C), pages 39-48.
    15. Rinaldi, Marco & Picarelli, Erika & D'Ariano, Andrea & Viti, Francesco, 2020. "Mixed-fleet single-terminal bus scheduling problem: Modelling, solution scheme and potential applications," Omega, Elsevier, vol. 96(C).
    16. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    17. Dennis Dreier & Björn Rudin & Mark Howells, 2020. "Comparison of management strategies for the charging schedule and all-electric operation of a plug-in hybrid-electric bi-articulated bus fleet," Public Transport, Springer, vol. 12(2), pages 363-404, June.
    18. Wang, Jing & Kang, Lixia & Liu, Yongzhong, 2020. "Optimal scheduling for electric bus fleets based on dynamic programming approach by considering battery capacity fade," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    19. Gong, Manlin & Hu, Yucong & Chen, Zhiwei & Li, Xiaopeng, 2021. "Transfer-based customized modular bus system design with passenger-route assignment optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    20. Zhou, Yu & Meng, Qiang & Ong, Ghim Ping, 2022. "Electric Bus Charging Scheduling for a Single Public Transport Route Considering Nonlinear Charging Profile and Battery Degradation Effect," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 49-75.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:8957-:d:611899. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.