MAGE: Microgrids with Advanced Grid Efficiency through Battery-Aware EV management

Higher penetration of Electric Vehicles (EVs) yields a positive impact by curbing pollution. However, this rapid proliferation of EVs presents several challenges to the efficient management of EVs and the existing grid infrastructure due to the very high energy requirement of newly adopted EVs, which can also act as energy resources. This work delves into the problem of intelligent charging-discharging of EVs in a single offline microgrid framework, which can partially cater to the energy needs of other EVs (V2V, vehicle-to-vehicle), grid (V2G, vehicle-to-grid), building (V2B, vehicle-to-building), etc. This approach also preserves the battery's health by limiting the number of charging-discharging cycles. Hence, a battery degradation-aware energy scheduler named MAGE (Microgrids with Advanced Grid Efficiency through Battery-Aware EV Management) is formulated to minimize the microgrid's operational cost (MOC). The MOC is comprised of- the energy procurement cost, the cost of surplus energy sales, and penalties for unsatisfied demands. A formulation based on Mixed-Integer Quadratic Programming is proposed to ascertain an optimal solution. Heuristic algorithms are designed to identify a reasonable solution quickly. A comprehensive experimental analysis of our proposed approach exhibits its potential in optimizing MOC and ensures grid efficiency by reducing the peak load and fattening the load curve.