Analysis of user behaviour for modelling an electric vehicle loading profile generator

Due to the constant expansion of renewable energies, supply security must be ensured even in the low-voltage grid via flexibilities. One of these flexibilities are provided by electric vehicles (EV) by sector coupling. To validate and generate charge control algorithms to maximize the use of these flexibilities, it is necessary to model the detailed charging of EVs based on measured EV charging processes and taking the users’ behaviour into account. Often, the charging process is assumed to be idealized. Therefore, within the scope of the paper, the charges of EVs have been measured and compiled into realistic charging profiles for different vehicle types. Furthermore, statistical data of user behaviour has been analysed to generate daily, weekly, and monthly charging profiles of EVs. The statistical data analysed includes age, working hours and distance to work. The measurements of EV charging and the analysis of statistical data have been summarized in an electric vehicle charging profile generator (EVCPG) written in Python. The EVCPG is able to generate a variety of different charging profiles of EVs with different charging powers, vehicle types and users. Due to this, a more detailed modelling of the electrical grids is possible.