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Techno-Probabilistic Flexibility Assessment of EV2G Based on Chargers’ Historical Records

Author

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  • Kabseok Ko

    (Department of Electronics Engineering, Kangwon National University, Chuncheon 24314, Republic of Korea)

  • Eunjung Lee

    (Research Institute for Solar and Sustainable Energies, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea)

  • Keon Baek

    (Department of Electrical Engineering, Chosun University, Gwangju 61452, Republic of Korea)

Abstract

As the proportion of renewable energy is rapidly increasing under the commitment to carbon neutrality, technical research and demonstrations regarding electric vehicle-to-grid (EV2G) charging are in progress. Meanwhile, commercialization of EV2G in the power system should be preceded by a quantitative assessment of EV2G flexibility based on practical data analysis. In this paper, we propose a framework to evaluate the technical flexibility of EV2G using the accumulated historical records of chargers. The framework consists of a charger profile generation model that derives a probabilistic state profile of each segmented charger group and a virtual EV2G flexibility model that derives flexibility through optimal operation of a virtual EV2G. The experiment was conducted based on islanded grid and charger data. The experimental results validated the economic and environmental contribution effects of EV2G flexibility. The proposed framework can contribute to stakeholders’ decision-making on the utilization of EV2G as a flexible resource based on reliable analysis results.

Suggested Citation

  • Kabseok Ko & Eunjung Lee & Keon Baek, 2025. "Techno-Probabilistic Flexibility Assessment of EV2G Based on Chargers’ Historical Records," Energies, MDPI, vol. 18(8), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2031-:d:1635587
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    References listed on IDEAS

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    1. Selim Karkour & Yuki Ichisugi & Amila Abeynayaka & Norihiro Itsubo, 2020. "External-Cost Estimation of Electricity Generation in G20 Countries: Case Study Using a Global Life-Cycle Impact-Assessment Method," Sustainability, MDPI, vol. 12(5), pages 1-35, March.
    2. Heeyun Lee & Hyunjoong Kim & Hyewon Kim & Hyunsup Kim, 2025. "Optimal Vehicle-to-Grid Charge Scheduling for Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 18(5), pages 1-15, February.
    3. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    4. Jordehi, A. Rezaee & Javadi, Mohammad Sadegh & Catalão, João P.S., 2021. "Day-ahead scheduling of energy hubs with parking lots for electric vehicles considering uncertainties," Energy, Elsevier, vol. 229(C).
    5. Lotfi, Mohamed & Almeida, Tiago & Javadi, Mohammad S. & Osório, Gerardo J. & Monteiro, Cláudio & Catalão, João P.S., 2022. "Coordinating energy management systems in smart cities with electric vehicles," Applied Energy, Elsevier, vol. 307(C).
    6. Salvatore Micari & Giuseppe Napoli, 2024. "Electric Vehicles for a Flexible Energy System: Challenges and Opportunities," Energies, MDPI, vol. 17(22), pages 1-26, November.
    7. Badr Lami & Mohammed Alsolami & Ahmad Alferidi & Sami Ben Slama, 2025. "A Smart Microgrid Platform Integrating AI and Deep Reinforcement Learning for Sustainable Energy Management," Energies, MDPI, vol. 18(5), pages 1-30, February.
    8. Kong, Yigang & Kong, Zhigang & Liu, Zhiqi & Wei, Congmei & Zhang, Jingfang & An, Gaocheng, 2017. "Pumped storage power stations in China: The past, the present, and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 720-731.
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