Electric vehicle user profiles for aggregated flexibility planning

Abstract

The global electric vehicle uptake and the increasing electrification of final energy consumption could bring grid management issues to the distribution system in a near future. Demand response programs are seen as a direct solution through the aggregation of distributed flexibility resources, expecting electric vehicles to be one of the main flexibility sources. However, the charging demand depends on the variability of daily drivers' behaviour, and calculate the aggregated flexibility potential from forecasted individual sessions may result in a highly inefficient process and uncertain result. Therefore, to reduce this uncertainty, this work proposes a methodology to discover distinct user profiles with a model-based clustering method followed by the aggregation of the clusters into daily user profiles. The clustering method uses only the two basic connection variables available for any charging infrastructure: the connection start time and the duration of the connection. The method has been tested with data from the public charging infrastructure of the city of Arnhem, in The Netherlands. The clustering process has resulted in 45 different clusters aggregated into 7 user profiles, some of them with very low variability and therefore clearly useful to consider in flexibility programs.