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Plug-in behavior of electric vehicles users: Insights from a large-scale trial and impacts for grid integration studies

Author

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  • Felipe Gonzalez

    (GeePs - Laboratoire Génie électrique et électronique de Paris - CentraleSupélec - SU - Sorbonne Université - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique)

  • Marc Petit

    (GeePs - Laboratoire Génie électrique et électronique de Paris - CentraleSupélec - SU - Sorbonne Université - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique)

  • Yannick Perez

    (LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay)

Abstract

Electric vehicle (EV) grid integration presents significant challenges and opportunities for electricity system operation and planning. Proper assessment of the costs and benefits involved in EV integration hinges on correctly modeling and evaluating EV-user driving and charging patterns. Recent studies have evidenced that EV users do not plug in their vehicle every day (here called non-systematic plug-in behavior), which can alter the impacts of EV charging and the flexibility that EV fleets can provide to the system. This work set out to evaluate the effect of considering non-systematic plug-in behavior in EV grid integration studies. To do so, an open-access agent-based EV simulation model that includes a probabilistic plug-in decision module was developed and calibrated to match the charging behavior observed in the Electric Nation project, a large-scale smart charging trial. Analysis shows that users tend to plug-in their EV between 2 and 3 times per week, with a lower plug-in frequency for large-battery EVs and large heterogeneity in user charging preferences. Results computed using our model show that non-systematic plug-in behavior effects reduce the impact of EV charging, especially for price-responsive charging, as fewer EVs charge simultaneously. On the other hand, non-systematic plug-in can reduce available flexibility, particularly when considering current trends towards larger battery sizes. Counter-intuitively, large-battery fleets can have reduced flexibility compared to small-battery fleets, both in power and stored energy, due to lower plug-in frequency and higher energy requirements per charging session. Improving plug-in ratios of EV users appears as key enabler for flexibility. In comparison, augmenting charging power can increase the flexibility provided by EV fleets but at the expense of larger impacts on distribution grids.

Suggested Citation

  • Felipe Gonzalez & Marc Petit & Yannick Perez, 2021. "Plug-in behavior of electric vehicles users: Insights from a large-scale trial and impacts for grid integration studies," Post-Print hal-03363782, HAL.
    • Handle: RePEc:hal:journl:hal-03363782
    DOI: 10.1016/j.etran.2021.100131
    Note: View the original document on HAL open archive server: https://hal.science/hal-03363782v1
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    References listed on IDEAS

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    2. García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Soria Alcaide, Rafael, 2023. "Carbon footprint of battery electric vehicles considering average and marginal electricity mix," Energy, Elsevier, vol. 268(C).
    3. Pampa Sinha & Kaushik Paul & Sanchari Deb & Sulabh Sachan, 2023. "Comprehensive Review Based on the Impact of Integrating Electric Vehicle and Renewable Energy Sources to the Grid," Energies, MDPI, vol. 16(6), pages 1-39, March.
    4. Sevdari, Kristian & Calearo, Lisa & Andersen, Peter Bach & Marinelli, Mattia, 2022. "Ancillary services and electric vehicles: An overview from charging clusters and chargers technology perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
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    7. Daneshzand, Farzaneh & Coker, Phil J & Potter, Ben & Smith, Stefan T, 2023. "EV smart charging: How tariff selection influences grid stress and carbon reduction," Applied Energy, Elsevier, vol. 348(C).
    8. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
    9. Kreft, Markus & Brudermueller, Tobias & Fleisch, Elgar & Staake, Thorsten, 2024. "Predictability of electric vehicle charging: Explaining extensive user behavior-specific heterogeneity," Applied Energy, Elsevier, vol. 370(C).
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