Real-Time Energy Management of EVs in a Microgrid Integrated with Distributed Energy Resources Considering Uncertainties

Though microgrids can efficiently integrate distributed energy resources (DERs) and electric vehicles (EVs), it also poses a challenge for energy management due to multiple uncertainties associated with DERs, load, and EVs. This study presents an energy management system (EMS) for a microgrid that optimizes the energy flows between the grid and DERs by scheduling EVs in real-time while considering the uncertainties as well as dynamic pricing. The proposed EMS is formulated as a stochastic mixed integer linear programming (MILP) problem to minimize the operating cost and burden on the distribution transformer while satisfying EV driving needs. Moreover, the inherent variability of the microgrid load and DERs are incorporated into the developed EMS using scenario generation and subsequent reduction techniques. Furthermore, uncertainties of EVs are handled in real-time by implementing the designed EMS as a receding horizon model. The results indicate 22% reduction in the operating cost and 86% reduction in the loss-of-life (LoL) of transformer using the proposed EMS compared with uncoordinated charging case.