Modelling and sizing sensitivity analysis of a fully renewable energy-based electric vehicle charging station microgrid

This paper presents long-term modelling and second-by-second simulation of an autonomous microgrid (MG), including only renewable energy sources (RESs) and a hybrid energy storage system (HESS) as energy provider, and an electric vehicle (EV) Charge Station as a group load. The model uses forecast data for wind speed and solar radiation to provide wind turbine (WT) and photovoltaic (PV) generated powers, and statistical data for vehicles within a defined car park to model the EV demand. It is flexible and can support varying several planning parameters, e.g. varying sizes of WT and PV generation as well as various capacities of energy storage systems (ESSs). Therefore, in order to examine the impact of variations in RESs and ESS sizes, as well as the impact of EV demand uncertainties on the performance and efficiency of the MG, e.g. EV unmet energy, several sensitivity analyses are provided. Based on sensitivity analysis results, one can find reasonable ranges of MG module sizes, and make a decision for sizing of the overall system. For the case study represented here, results show that at least one WT is required, increasing PV panels is more effective to meet the midday EV load in at the target location, and a lower level of Li-ion ESS capacity is sufficient storage for the charging/discharging of the EVs.