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Charging Behavior of Electric Vehicles: Temporal Clustering Based on Real-World Data

Author

Listed:
  • Alexandra Märtz

    (Chair of Energy Economics, Institute for Industrial Production, Karlsruhe Institute of Technology, 76187 Karlsruhe, Germany)

  • Uwe Langenmayr

    (Chair of Energy Economics, Institute for Industrial Production, Karlsruhe Institute of Technology, 76187 Karlsruhe, Germany)

  • Sabrina Ried

    (Chair of Energy Economics, Institute for Industrial Production, Karlsruhe Institute of Technology, 76187 Karlsruhe, Germany)

  • Katrin Seddig

    (Chair of Energy Economics, Institute for Industrial Production, Karlsruhe Institute of Technology, 76187 Karlsruhe, Germany)

  • Patrick Jochem

    (Institute of Networked Energy Systems, German Aerospace Center (DLR), Curiestr. 4, 70563 Stuttgart, Germany)

Abstract

The increasing adoption of battery electric vehicles (BEVs) is leading to rising demand for electricity and, thus, leading to new challenges for the energy system and, particularly, the electricity grid. However, there is a broad consensus that the critical factor is not the additional energy demand, but the possible load peaks occurring from many simultaneous charging processes. Hence, sound knowledge about the charging behavior of BEVs and the resulting load profiles is required for a successful and smart integration of BEVs into the energy system. This requires a large amount of empirical data on charging processes and plug-in times, which is still lacking in literature. This paper is based on a comprehensive data set of 2.6 million empirical charging processes and investigates the possibility of identifying different groups of charging processes. For this, a Gaussian mixture model, as well as a k-means clustering approach, are applied and the results validated against synthetic load profiles and the original data. The identified load profiles, the flexibility potential and the charging locations of the clusters are of high relevance for energy system modelers, grid operators, utilities and many more. We identified, in this early market phase of BEVs, a surprisingly high number of opportunity chargers during daytime, as well as switching of users between charging clusters.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6575-:d:909980
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    References listed on IDEAS

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    1. Christoph M. Flath & Jens P. Ilg & Sebastian Gottwalt & Hartmut Schmeck & Christof Weinhardt, 2014. "Improving Electric Vehicle Charging Coordination Through Area Pricing," Transportation Science, INFORMS, vol. 48(4), pages 619-634, November.
    2. 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).
    3. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
    4. Gunkel, Philipp Andreas & Bergaentzlé, Claire & Græsted Jensen, Ida & Scheller, Fabian, 2020. "From passive to active: Flexibility from electric vehicles in the context of transmission system development," Applied Energy, Elsevier, vol. 277(C).
    5. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    6. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
    7. Gonzalez Venegas, Felipe & Petit, Marc & Perez, Yannick, 2021. "Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    8. Helmus, J.R. & Spoelstra, J.C. & Refa, N. & Lees, M. & van den Hoed, R., 2018. "Assessment of public charging infrastructure push and pull rollout strategies: The case of the Netherlands," Energy Policy, Elsevier, vol. 121(C), pages 35-47.
    9. Knezović, Katarina & Marinelli, Mattia & Zecchino, Antonio & Andersen, Peter Bach & Traeholt, Chresten, 2017. "Supporting involvement of electric vehicles in distribution grids: Lowering the barriers for a proactive integration," Energy, Elsevier, vol. 134(C), pages 458-468.
    10. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
    11. Ma, Zhenjun & Yan, Rui & Nord, Natasa, 2017. "A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings," Energy, Elsevier, vol. 134(C), pages 90-102.
    12. Pagani, M. & Korosec, W. & Chokani, N. & Abhari, R.S., 2019. "User behaviour and electric vehicle charging infrastructure: An agent-based model assessment," Applied Energy, Elsevier, vol. 254(C).
    13. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2019. "Two-stage stochastic optimization for cost-minimal charging of electric vehicles at public charging stations with photovoltaics," Applied Energy, Elsevier, vol. 242(C), pages 769-781.
    14. Philipp Andreas Gunkel & Claire Bergaentzl'e & Ida Gr{ae}sted Jensen & Fabian Scheller, 2020. "From passive to active: Flexibility from electric vehicles in the context of transmission system development," Papers 2011.05830, arXiv.org.
    15. Heinz, Daniel, 2018. "Erstellung und Auswertung repräsentativer Mobilitäts- und Ladeprofile für Elektrofahrzeuge in Deutschland," Working Paper Series in Production and Energy 30, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
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