Energy management system for a workplace PV-EV charging station with active and reactive power dispatch of EVs considering transformer aging and uncertainties

This study presents a new energy management system (EMS) for a grid-tied photovoltaic (PV) – electric vehicle (EV) integrated workplace charging station. The proposed EMS is developed as a convex stochastic mixed-integer quadratically constrained problem (MIQCP) to minimize the expected apparent power demand while limiting the distribution transformer's accelerated aging and satisfying the EV driving needs. This is accomplished by scheduling real and reactive powers of EVs in real-time through Vehicle-to-grid (V2G) mode. The inherent diurnal uncertainties and seasonal variations associated with the workplace non-EV load, PV generation are incorporated using probabilistic and hierarchical clustering techniques, respectively. The implementation of the developed EMS under receding horizon model enables real-time operation by adapting to dynamic arrivals of EVs. The effectiveness of the proposed EMS is validated through numerous simulations from the view point of the distribution system operator (DSO), charging station owner (CSO), and EV prosumer. The results indicate a substantial reduction in the peak demand, minimized transformer's loss-of-life (LoL), and operating cost saving while satisfying the EV driving needs in comparison to uncoordinated charging method.