IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v173y2025ics0967070x25002914.html

An empirical analysis framework to evaluate the impact of residential electric vehicles on power grid

Author

Listed:
  • Jiang, An
  • Qiu, Jiehong
  • Li, Aiyuan
  • Zhang, Guangnan

Abstract

This paper proposes an empirical analysis framework to model the charging behavior of electric vehicles (EVs) and estimate the impact on power grid. This method relies solely on residential charging data and is universally applicable to power grids in different regions. Moreover, the approach enables the simulation of the charging demand under varying EV ownership rate levels and the calculation of the maximum number of EVs that a given area can support without overloading the power grid. As a case study, we collect data on residential charging behavior from a city in Central China to estimate model parameters. Combining with load data, we find that even with a 5 % ownership rate, EVs will not significantly burden the grid load on most days throughout the year or during off-peak hours of the day. However, attention must be given to peak months and peak hours of the day. Additionally, we analyze the ownership rate of EVs that the city can sustain and determine that, with 6 % of the available capacity of distribution transformers, the city can accommodate EVs for 8.12 % of its population. This paper contributes to the engineering management of EVs charging, EVs promotion strategies, and the stability of power grid.

Suggested Citation

  • Jiang, An & Qiu, Jiehong & Li, Aiyuan & Zhang, Guangnan, 2025. "An empirical analysis framework to evaluate the impact of residential electric vehicles on power grid," Transport Policy, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:trapol:v:173:y:2025:i:c:s0967070x25002914
    DOI: 10.1016/j.tranpol.2025.07.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X25002914
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tranpol.2025.07.038?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. Yang, Lin & Cai, Yishan & Yang, Yixin & Deng, Zhongwei, 2020. "Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    2. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    3. 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.
    4. Zhao, Yinan & Wen, Yifan & Wang, Fang & Tu, Wei & Zhang, Shaojun & Wu, Ye & Hao, Jiming, 2023. "Feasibility, economic and carbon reduction benefits of ride-hailing vehicle electrification by coupling travel trajectory and charging infrastructure data," Applied Energy, Elsevier, vol. 342(C).
    5. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    6. Borne, Olivier & Perez, Yannick & Petit, Marc, 2018. "Market integration or bids granularity to enhance flexibility provision by batteries of electric vehicles," Energy Policy, Elsevier, vol. 119(C), pages 140-148.
    7. Jain, Monika & Singh, Archana, 2024. "An empirical study on electric vehicle adoption in India: A step towards a greener environment," Transport Policy, Elsevier, vol. 156(C), pages 112-125.
    8. Hu, Yang & Bahamonde-Birke, Francisco J. & Ettema, Dick, 2025. "Vehicle-to-grid, why not? An interview with battery electric vehicle users with various driving patterns in Utrecht, the Netherlands," Transport Policy, Elsevier, vol. 164(C), pages 231-240.
    9. Sai Sudharshan Ravi & Muhammad Aziz, 2022. "Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives," Energies, MDPI, vol. 15(2), pages 1-27, January.
    10. Ren, Fei & Tian, Chenlu & Zhang, Guiqing & Li, Chengdong & Zhai, Yuan, 2022. "A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features," Energy, Elsevier, vol. 250(C).
    11. Qiu, Yueming Lucy & Wang, Yi David & Iseki, Hiroyuki & Shen, Xingchi & Xing, Bo & Zhang, Huiming, 2022. "Empirical grid impact of in-home electric vehicle charging differs from predictions," Resource and Energy Economics, Elsevier, vol. 67(C).
    12. Tikka, Ville & Haapaniemi, Jouni & Räisänen, Otto & Honkapuro, Samuli, 2022. "Convolutional neural networks in estimating the spatial distribution of electric vehicles to support electricity grid planning," Applied Energy, Elsevier, vol. 328(C).
    13. Khaleghikarahrodi, Mehrsa & Macht, Gretchen A., 2023. "Patterns, no patterns, that is the question: Quantifying users’ electric vehicle charging," Transport Policy, Elsevier, vol. 141(C), pages 291-304.
    14. Chen, Ching-Fu & Lai, Ching-Ming, 2024. "Understanding the acceptance of vehicle-to-grid (V2G) services: Evidence from Taiwan," Transport Policy, Elsevier, vol. 159(C), pages 230-240.
    15. Yanyan Xu & Serdar Çolak & Emre C. Kara & Scott J. Moura & Marta C. González, 2018. "Planning for electric vehicle needs by coupling charging profiles with urban mobility," Nature Energy, Nature, vol. 3(6), pages 484-493, June.
    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. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    2. Zhang, Lei & Huang, Zhijia & Wang, Zhenpo & Li, Xiaohui & Sun, Fengchun, 2024. "An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing," Energy, Elsevier, vol. 299(C).
    3. Sheng, Yujie & Zeng, Hongtai & Guo, Qinglai & Yu, Yang & Li, Qiang, 2023. "Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations," Applied Energy, Elsevier, vol. 348(C).
    4. Jiazu Zhou & Tianyu Dong & Hongrong Yang & Seanglidet Yean & Bu-Sung Lee & Markus Schläpfer, 2026. "Decentralized electric vehicle charging enables large-scale photovoltaic integration in tropical cities," Nature Communications, Nature, vol. 17(1), pages 1-10, December.
    5. Yamaguchi, Yohei & Ochi, Yudai & Ohara, Ryotaro & Uchida, Hideaki & Yoshizawa, Shinya & Sakai, Katsuya & Ota, Yutaka & Shimoda, Yoshiyuki, 2025. "Large-scale spatiotemporal modeling of travel behavior, electric vehicle charging demand, and flexibility based on human mobility big data," Energy, Elsevier, vol. 335(C).
    6. Bin Zhang & Qingyao Xin & Siyuan Chen & Zhaohua Wang & Yang Lu & Niu Niu & Fang Zhang & Guangchuan Liu & Prateek Bansal, 2026. "Behavioral uncertainty in EV charging drives heterogeneous grid load variability under climate goals," Nature Communications, Nature, vol. 17(1), pages 1-12, December.
    7. Fu, Zhi & Liu, Xiaochen & Zhang, Ji & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2025. "Orderly solar charging of electric vehicles and its impact on charging behavior: A year-round field experiment," Applied Energy, Elsevier, vol. 381(C).
    8. 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.
    9. Zhao, Yang & Jiang, Ziyue & Chen, Xinyu & Liu, Peng & Peng, Tianduo & Shu, Zhan, 2023. "Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets," Energy, Elsevier, vol. 285(C).
    10. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    11. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhang, Zhaosheng & Dorrell, David G. & Li, Xiaohui, 2022. "Battery electric vehicle usage pattern analysis driven by massive real-world data," Energy, Elsevier, vol. 250(C).
    12. Ye, Tinghan & Liu, Shanshan & Kontou, Eleftheria, 2024. "Managed residential electric vehicle charging minimizes electricity bills while meeting driver and community preferences," Transport Policy, Elsevier, vol. 149(C), pages 122-138.
    13. Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.
    14. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    15. Mowry, Andrew M. & Mallapragada, Dharik S., 2021. "Grid impacts of highway electric vehicle charging and role for mitigation via energy storage," Energy Policy, Elsevier, vol. 157(C).
    16. Crozier, Constance & Morstyn, Thomas & McCulloch, Malcolm, 2020. "The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems," Applied Energy, Elsevier, vol. 268(C).
    17. Wiedemann, Nina & Xin, Yanan & Medici, Vasco & Nespoli, Lorenzo & Suel, Esra & Raubal, Martin, 2024. "Vehicle-to-grid for car sharing - A simulation study for 2030," Applied Energy, Elsevier, vol. 372(C).
    18. Zhang, Chengquan & Kitamura, Hiroshi & Goto, Mika, 2026. "Behavioral insights into vehicle-to-grid (V2G) adoption: Beyond economic perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 228(C).
    19. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    20. Siobhan Powell & Gustavo Vianna Cezar & Liang Min & Inês M. L. Azevedo & Ram Rajagopal, 2022. "Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption," Nature Energy, Nature, vol. 7(10), pages 932-945, October.

    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:trapol:v:173:y:2025:i:c:s0967070x25002914. 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/30473/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.