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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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).
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