IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i8p3334-d1376632.html
   My bibliography  Save this article

Enhancing Electric Bus Charging Scheduling: An Energy-Integrated Dynamic Bus Replacement Strategy with Time-of-Use Pricing

Author

Listed:
  • Yang Liu

    (Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, China)

  • Bing Zeng

    (Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, China)

  • Kejun Long

    (Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, China)

  • Wei Wu

    (School of Transportation Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract

Existing studies on electric bus (EB) scheduling mainly focus on the arrangement of bus charging at the bus terminals, which may lead to inflexible charging plans, high scheduling costs, and low utilization of electricity energy. To address these challenges, this paper proposes a dynamic bus replacement strategy. When the power of an in-service EB is insufficient, a standby EB stationed at nearby charging stations is dispatched in advance to replace this in-service EB at a designated bus stop. Passengers then transfer to the standby bus to complete their journey. The replaced bus proceeds to the charging station and transitions into a “standby bus” status after recharging. A mixed-integer nonlinear programming (MINLP) model is established to determine the dispatching plan for both standby and in-service EBs while also designing optimal charging schemes (i.e., the charging time, location, and the amount of charged power) for electric bus systems. Additionally, this study also incorporates the strategy of time-of-use electricity prices to mitigate the adverse impact on the power grid. The proposed model is linearized to the mixed-integer linear programming (MILP) model and efficiently solved by commercial solvers (e.g., GUROBI). The case study demonstrates that EBs with different energy levels can be dynamically assigned to different bus lines using bus replacement strategies, resulting in reduced electricity costs for EB systems without compromising on scheduling efficiency.

Suggested Citation

  • Yang Liu & Bing Zeng & Kejun Long & Wei Wu, 2024. "Enhancing Electric Bus Charging Scheduling: An Energy-Integrated Dynamic Bus Replacement Strategy with Time-of-Use Pricing," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3334-:d:1376632
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/8/3334/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/8/3334/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    2. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    3. Petit, Antoine & Ouyang, Yanfeng & Lei, Chao, 2018. "Dynamic bus substitution strategy for bunching intervention," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 1-16.
    4. 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.
    5. Li, Lu & Lo, Hong K. & Huang, Wei & Xiao, Feng, 2021. "Mixed bus fleet location-routing-scheduling under range uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 155-179.
    6. Petit, Antoine & Lei, Chao & Ouyang, Yanfeng, 2019. "Multiline Bus Bunching Control via Vehicle Substitution," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 68-86.
    7. Bi, Zicheng & Keoleian, Gregory A. & Ersal, Tulga, 2018. "Wireless charger deployment for an electric bus network: A multi-objective life cycle optimization," Applied Energy, Elsevier, vol. 225(C), pages 1090-1101.
    Full references (including those not matched with items on IDEAS)

    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. Zhou, Chang & Tian, Qiong & Wang, David Z.W., 2022. "A novel control strategy in mitigating bus bunching: Utilizing real-time information," Transport Policy, Elsevier, vol. 123(C), pages 1-13.
    2. 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.
    3. Bian, Bomin & Zhu, Ning & Meng, Qiang, 2023. "Real-time cruising speed design approach for multiline bus systems," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 1-24.
    4. Wu, Weitiao & Lin, Yue & Liu, Ronghui & Jin, Wenzhou, 2022. "The multi-depot electric vehicle scheduling problem with power grid characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 322-347.
    5. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    6. Lizana, Jesus & Friedrich, Daniel & Renaldi, Renaldi & Chacartegui, Ricardo, 2018. "Energy flexible building through smart demand-side management and latent heat storage," Applied Energy, Elsevier, vol. 230(C), pages 471-485.
    7. Ritter, Andreas & Widmer, Fabio & Duhr, Pol & Onder, Christopher H., 2022. "Long-term stochastic model predictive control for the energy management of hybrid electric vehicles using Pontryagin’s minimum principle and scenario-based optimization," Applied Energy, Elsevier, vol. 322(C).
    8. Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(C).
    9. Dai, Zhuang & Han, Ke, 2023. "Exploring the drive-by sensing power of bus fleet through active scheduling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    10. Loris Di Natale & Luca Funk & Martin Rüdisüli & Bratislav Svetozarevic & Giacomo Pareschi & Philipp Heer & Giovanni Sansavini, 2021. "The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland," Energies, MDPI, vol. 14(16), pages 1-24, August.
    11. Jia, Jun-Jun & Ni, Jinlan & Wei, Chu, 2023. "Residential responses to service-specific electricity demand: Case of China," China Economic Review, Elsevier, vol. 78(C).
    12. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    13. Li, Yanxue & Zhang, Xiaoyi & Gao, Weijun & Xu, Wenya & Wang, Zixuan, 2022. "Operational performance and grid-support assessment of distributed flexibility practices among residential prosumers under high PV penetration," Energy, Elsevier, vol. 238(PB).
    14. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    15. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    16. Guo, Hongqiang & Hou, Daizheng & Du, Shangye & Zhao, Ling & Wu, Jian & Yan, Ning, 2020. "A driving pattern recognition-based energy management for plug-in hybrid electric bus to counter the noise of stochastic vehicle mass," Energy, Elsevier, vol. 198(C).
    17. Ju, Fei & Zhuang, Weichao & Wang, Liangmo & Zhang, Zhe, 2020. "Comparison of four-wheel-drive hybrid powertrain configurations," Energy, Elsevier, vol. 209(C).
    18. Fatemeh Enayatollahi & Ahmed Osman Idris & M. A. Amiri Atashgah, 2019. "Modelling bus bunching under variable transit demand using cellular automata," Public Transport, Springer, vol. 11(2), pages 269-298, August.
    19. Woo, Hyeon & Son, Yongju & Cho, Jintae & Kim, Sung-Yul & Choi, Sungyun, 2023. "Optimal expansion planning of electric vehicle fast charging stations," Applied Energy, Elsevier, vol. 342(C).
    20. Dingyi Lu & Yunqian Lu & Kexin Zhang & Chuyuan Zhang & Shao-Chao Ma, 2023. "An Application Designed for Guiding the Coordinated Charging of Electric Vehicles," Sustainability, MDPI, vol. 15(14), pages 1-16, July.

    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:16:y:2024:i:8:p:3334-:d:1376632. 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.