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Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden

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  • Huang, Pei
  • Ma, Zhenliang

Abstract

Accurately estimating the charging behaviours of electric vehicles (EVs) is crucial for various applications, such as charging station planning and grid impact estimation. However, the analysis of EV charging behaviours using real-world data remains limited due to (confidential) data availability constraints. Furthermore, while existing modelling studies have demonstrated EVs as effective tools for electricity balancing and delivery between locations, their potential remains unexplored empirically. This study aims to bridge the research gap by studying EV charging behaviours and their capacity for electricity balancing and delivery. Using data from 179,665 real-world charging sessions in Sweden, we employed statistical and clustering analysis to scrutinize charging behaviours comprehensively. Synthetic weekly charging load profiles are generated for both residential areas and workplaces, considering varying charging power levels, which can be used as inputs for large-scale EV charging load modelling. Furthermore, performance indicators are proposed to quantify the potential of EVs for electricity balancing and delivery. Results show that EVs exhibit significant potential for electricity balancing (up to 51.5 kWh daily). Many EV owners underutilize their EV battery capacity, providing an opportunity for active electricity delivery across locations. This study can help understand EV charging behaviours and recognize their significant potentials for electricity regulation and integrating more renewables in the future power system.

Suggested Citation

  • Huang, Pei & Ma, Zhenliang, 2024. "Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden," Renewable Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:renene:v:236:y:2024:i:c:s0960148124015799
    DOI: 10.1016/j.renene.2024.121511
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    References listed on IDEAS

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    1. Sandström, Maria & Huang, Pei & Bales, Chris & Dotzauer, Erik, 2023. "Evaluation of hosting capacity of the power grid for electric vehicles – A case study in a Swedish residential area," Energy, Elsevier, vol. 284(C).
    2. Zhou, Yuekuan, 2022. "Energy sharing and trading on a novel spatiotemporal energy network in Guangdong-Hong Kong-Macao Greater Bay Area," Applied Energy, Elsevier, vol. 318(C).
    3. 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).
    4. Powell, Siobhan & Cezar, Gustavo Vianna & Rajagopal, Ram, 2022. "Scalable probabilistic estimates of electric vehicle charging given observed driver behavior," Applied Energy, Elsevier, vol. 309(C).
    5. Huang, Pei & Lovati, Marco & Zhang, Xingxing & Bales, Chris, 2020. "A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered," Applied Energy, Elsevier, vol. 268(C).
    6. Barone, G. & Buonomano, A. & Calise, F. & Forzano, C. & Palombo, A., 2019. "Building to vehicle to building concept toward a novel zero energy paradigm: Modelling and case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 625-648.
    7. Yu, Hang & Niu, Songyan & Shang, Yitong & Shao, Ziyun & Jia, Youwei & Jian, Linni, 2022. "Electric vehicles integration and vehicle-to-grid operation in active distribution grids: A comprehensive review on power architectures, grid connection standards and typical applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. Palm, J. & Kojonsaari, A.-R. & Öhrlund, I. & Fowler, N. & Bartusch, C., 2023. "Drivers and barriers to participation in Sweden's local flexibility markets for electricity," Utilities Policy, Elsevier, vol. 82(C).
    9. Huang, Pei & Ma, Zhenjun & Xiao, Longzhu & Sun, Yongjun, 2019. "Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities," Applied Energy, Elsevier, vol. 255(C).
    10. Alexandra Märtz & Uwe Langenmayr & Sabrina Ried & Katrin Seddig & Patrick Jochem, 2022. "Charging Behavior of Electric Vehicles: Temporal Clustering Based on Real-World Data," Energies, MDPI, vol. 15(18), pages 1-26, September.
    11. Board, Anthony & Sun, Yongjun & Huang, Pei & Xu, Tao, 2024. "Community-to-vehicle-to-community (C2V2C) for inter-community electricity delivery and sharing via electric vehicle: Performance evaluation and robustness analysis," Applied Energy, Elsevier, vol. 363(C).
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