Stochastic Generation of Aggregated Charging Profiles of PEVs for the Operation Analysis of Low Voltage Networks

Abstract

The increasing deployment of plug-in electric vehicle (PEV) in electrical networks is bringing new challenges to power system operators due to the high load concentration and their stochastic behaviour. Therefore, it becomes necessary to foresee the impact when connecting them to the grid. In this sense, this paper discusses the results of a thorough statistical analysis of a real PEV fleet data to identify their commuting and charging patterns, the number of connections by charging level and the charging frequency per week as main parameters to understand how they will influence the network. In that regard, a methodology based on the Poisson process and the Monte Carlo method to generate a random series of aggregated charging profiles in case of unavailability of real data that can be used to perform network studies is proposed. Finally, the methodology is validated through a series of simulations and the outcomes are discussed.