PV assisted electric vehicle charging station considering the integration of stationary first- or second-life battery storage

In recent years, the wider penetration of Renewable Energy Sources (RES) and Electric Vehicles (EV) has required the introduction of new sources of flexibility and adequate control models to increase the reliability of the energy sector. Indeed, the optimal management at the local scale of the match between energy demand and production is key to dealing with the technical and economic issues caused by the high variability of RES and EV charging demand. In this study, an environmentally oriented optimal design of a photovoltaic (PV) powered EV Charging Station (EVCS) integrated with an Electric Energy Storage System (ESS) is presented. Batteries characteristics and their role on life cycle emissions are also addressed. The system design is determined with the double aim of minimizing the carbon emissions due to the charging process and carbon footprint of the installed technologies and maximizing the self-consumption of local energy production. A bi-level optimization approach is adopted: a Multi-Objective Genetic Algorithm is used to optimally define the number of slow and medium EV Supply Equipment (EVSE), PV peak power and battery capacity, while a mixed integer linear programming (MILP) algorithm is used for the optimal control of the charging process. A comparison of different solutions is carried out over an eight-year time horizon, accounting for uncertainties in renewable generation and EV charging demand. The results highlight that the installation of Second-Life batteries as energy storage media in sustainable terms. Indeed, the cumulative CO2 emissions over the whole time horizon of the Electric Vehicle Charging Station are decreased by 15% and 10% respectively if compared with First-Life batteries and a baseline case without any stationary storage system. These results confirm that long-term evaluation of techno-environmental performance is crucial to make sustainable and conscious choices.