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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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