An Advanced Methodology for Characterizing the Net Load of Electric Vehicle Charging Stations Equipped with Onsite PV Systems

Abstract

The number of Electric Vehicles (EVs) in service is expected to vastly increase in the next years, requiring an adequate effort in promoting and installing new EV Charging Stations (EVCSs) spread around the territory. From a power system perspective, these are configured as additional uncertain loads of MV and LV distribution grids. The problem is complicated in the case of EVCSs equipped with onsite Photovoltaic (PV) systems, as the overall net load is affected by the uncertainties of the charging energy and of the energy generated from the renewable source. A proper statistical characterization of the net load of such EVCSs is mandatory to manage and operate the networks, addressing the increased load and its random behavior. This paper contributes to this field by developing a probabilistic Monte Carlo based methodology that considers three random features: the expected usage of the individual EV Charging Points (EVCPs), the composition of the EV fleet that uses the EVCS, and the features of the solar resource at the EVCS location. Numerical experiments based on actual data are presented to validate the proposed methodology, with different case studies and modeling of the charging events.