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

Alexandra Märtz (), Uwe Langenmayr, Sabrina Ried, Katrin Seddig and Patrick Jochem
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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

Energies, 2022, vol. 15, issue 18, 1-26

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.

Keywords: battery electric vehicles (BEV); temporal charging behavior of BEV users; flexibility potential in charging processes of BEVs; k-means clustering; Gaussian mixture model clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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