IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v198y2025ics1366554525001528.html
   My bibliography  Save this article

Optimizing day-ahead EV scheduling across multiple charging stations with an interrupted-charging scheme

Author

Listed:
  • Kim, Hyojin
  • Lee, Jongheon
  • Lee, Siyoung

Abstract

Globally, the expansion of charging infrastructure has lagged behind the rapid adoption of electric vehicles (EVs), causing inconvenience to users due to limited charging station availability. To address this issue, this study proposes a day-ahead scheduling strategy for charging service providers (CSPs) managing multiple stations within their jurisdictions. The strategy optimally assigns charging demands to stations and establishes charging schedules while considering battery degradation and existing infrastructure through an interrupted-charging scheme. We formulate this problem as a mixed-integer programming model to minimize operational costs consisting of charging, shortage, and assignment costs. To enhance scalability, a decomposition method based on column generation is developed to obtain high-quality solutions efficiently. A case study inspired by the regional characteristics of South Korea validates the strategy’s effectiveness. Compared with benchmark strategies, the proposed approach achieves lower operational costs while maintaining high charging completion rates in both day-ahead scheduling and even in real-time operation where actual demand deviates from day-ahead estimates. Furthermore, the results demonstrated the effectiveness of the proposed decomposition method for large-scale instances.

Suggested Citation

  • Kim, Hyojin & Lee, Jongheon & Lee, Siyoung, 2025. "Optimizing day-ahead EV scheduling across multiple charging stations with an interrupted-charging scheme," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:transe:v:198:y:2025:i:c:s1366554525001528
    DOI: 10.1016/j.tre.2025.104111
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554525001528
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2025.104111?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Sirapa Shrestha & Bivek Baral & Malesh Shah & Sailesh Chitrakar & Bim P Shrestha, 2022. "Measures to resolve range anxiety in electric vehicle users [A new long term assessment of energy return on investment (EROI) for U.S. oil and gas discovery and production]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 1186-1206.
    2. Bagheri Tookanlou, Mahsa & Pourmousavi, S. Ali & Marzband, Mousa, 2023. "A three-layer joint distributionally robust chance-constrained framework for optimal day-ahead scheduling of e-mobility ecosystem," Applied Energy, Elsevier, vol. 331(C).
    3. Zhou, Kaile & Cheng, Lexin & Lu, Xinhui & Wen, Lulu, 2020. "Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices," Applied Energy, Elsevier, vol. 276(C).
    4. Liu, Jin-peng & Zhang, Teng-xi & Zhu, Jiang & Ma, Tian-nan, 2018. "Allocation optimization of electric vehicle charging station (EVCS) considering with charging satisfaction and distributed renewables integration," Energy, Elsevier, vol. 164(C), pages 560-574.
    5. Benjamin Schaden & Thomas Jatschka & Steffen Limmer & Günther Robert Raidl, 2021. "Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers," Energies, MDPI, vol. 14(22), pages 1-33, November.
    6. Xu, Maosheng & Gao, Shan & Zheng, Junyi & Huang, Xueliang & Wu, Chuanshen, 2024. "Day-ahead electric vehicle charging behavior forecasting and schedulable capacity calculation for electric vehicle parking lot," Energy, Elsevier, vol. 309(C).
    7. Matina L. Y. Chau & Diamanto Koutsompina & Konstantinos Gkiotsalitis, 2024. "The Electric Vehicle Scheduling Problem for Buses in Networks with Multi-Port Charging Stations," Sustainability, MDPI, vol. 16(3), pages 1-21, February.
    8. Jinil Han & Jongyoon Park & Kyungsik Lee, 2017. "Optimal Scheduling for Electric Vehicle Charging under Variable Maximum Charging Power," Energies, MDPI, vol. 10(7), pages 1-15, July.
    9. Manu Lahariya & Dries F. Benoit & Chris Develder, 2020. "Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data," Energies, MDPI, vol. 13(16), pages 1-18, August.
    10. Wu, Fei & Sioshansi, Ramteen, 2017. "A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 55-82.
    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, Sixun & Yan, Rujing & Zhang, Jing & He, Yu & Geng, Xianxian & Li, Yuanbo & Yu, Changkun, 2025. "Optimizing interaction in renewable-vehicle-microgrid systems: Balancing battery health, user satisfaction, and participation," Renewable Energy, Elsevier, vol. 245(C).
    2. Liu, Lu & Zhou, Kaile, 2022. "Electric vehicle charging scheduling considering urgent demand under different charging modes," Energy, Elsevier, vol. 249(C).
    3. Jorge García Álvarez & Miguel Ángel González & Camino Rodríguez Vela & Ramiro Varela, 2018. "Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm," Energies, MDPI, vol. 11(10), pages 1-19, October.
    4. Yao, Zhaosheng & Wang, Zhiyuan & Ran, Lun, 2023. "Smart charging and discharging of electric vehicles based on multi-objective robust optimization in smart cities," Applied Energy, Elsevier, vol. 343(C).
    5. Francesco Lo Franco & Vincenzo Cirimele & Mattia Ricco & Vitor Monteiro & Joao L. Afonso & Gabriele Grandi, 2022. "Smart Charging for Electric Car-Sharing Fleets Based on Charging Duration Forecasting and Planning," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    6. 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.
    7. Davidov, Sreten, 2020. "Optimal charging infrastructure planning based on a charging convenience buffer," Energy, Elsevier, vol. 192(C).
    8. Balu, Korra & Mukherjee, V., 2024. "Optimal deployment of electric vehicle charging stations, renewable distributed generation with battery energy storage and distribution static compensator in radial distribution network considering un," Applied Energy, Elsevier, vol. 359(C).
    9. Hu, Xu & Yang, Zhaojun & Sun, Jun & Zhang, Yali, 2021. "Sharing economy of electric vehicle private charge posts," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 258-275.
    10. Verónica Anadón Martínez & Andreas Sumper, 2023. "Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review," Energies, MDPI, vol. 16(14), pages 1-41, July.
    11. 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).
    12. Fayez Alanazi & Talal Obaid Alshammari & Abdelhalim Azam, 2023. "Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities," Sustainability, MDPI, vol. 15(22), pages 1-23, November.
    13. Rafał Różycki & Joanna Józefowska & Krzysztof Kurowski & Tomasz Lemański & Tomasz Pecyna & Marek Subocz & Grzegorz Waligóra, 2022. "A Quantum Approach to the Problem of Charging Electric Cars on a Motorway," Energies, MDPI, vol. 16(1), pages 1-20, December.
    14. Nagaraju Dharavat & Suresh Kumar Sudabattula & Suresh Velamuri & Sachin Mishra & Naveen Kumar Sharma & Mohit Bajaj & Elmazeg Elgamli & Mokhtar Shouran & Salah Kamel, 2022. "Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm," Energies, MDPI, vol. 15(18), pages 1-25, September.
    15. Phillip K. Agbesi & Rico Ruffino & Marko Hakovirta, 2024. "Creating an optimal electric vehicle ecosystem: an investigation of electric vehicle stakeholders and ecosystem trends in the US," SN Business & Economics, Springer, vol. 4(3), pages 1-36, March.
    16. VJ Vijayalakshmi & A Amudha & K Dhayalini & A Prakash, 2024. "A hybrid WFS-CGO based approach for optimal allocation of EV charging spots along with capacitors in smart distribution network for congestion management," Energy & Environment, , vol. 35(4), pages 1673-1702, June.
    17. Yan Bao & Fangyu Chang & Jinkai Shi & Pengcheng Yin & Weige Zhang & David Wenzhong Gao, 2022. "An Approach for Pricing of Charging Service Fees in an Electric Vehicle Public Charging Station Based on Prospect Theory," Energies, MDPI, vol. 15(14), pages 1-20, July.
    18. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    19. Winschermann, Leoni & Bañol Arias, Nataly & Hoogsteen, Gerwin & Hurink, Johann, 2023. "Assessing the value of information for electric vehicle charging strategies at office buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    20. Ming, Fangzhu & Gao, Feng & Liu, Kun & Li, Xingqi, 2023. "A constrained DRL-based bi-level coordinated method for large-scale EVs charging," Applied Energy, Elsevier, vol. 331(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:transe:v:198:y:2025:i:c:s1366554525001528. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.