Optimal Coordinated Scheduling of Electric Vehicles and Battery Energy Storage Systems

Abstract

Electric vehicles (EVs) and battery energy storage systems (BESS) are rapidly gaining adoption worldwide as emerging consumer electronics products, playing an important role in the transition to sustainable energy. While their integration into power grids offers significant benefits when managed appropriately, it also introduces challenges related to grid stability and efficiency, such as increased peak demand and voltage fluctuations. Effective management of these challenges demands coordinated scheduling of EVs and BESS for both charging from the grid and discharging back into it. Various optimization approaches, including mixed-integer nonlinear programming (MINLP), have been proposed to tackle this problem. However, these approaches often involve numerous binary decision variables, which result in increased computational complexity or even make the computation practically infeasible in large-scale scenarios. To address this issue, this paper formulates the coordinated scheduling of EVs and BESS as a nonconvex constrained optimization problem with significantly fewer decision variables. A Modified Jacobi Proximal Alternating Direction Method of Multipliers (M-ProxJADMM) is proposed to efficiently solve the problem, with guaranteed convergence. Case studies are conducted to validate the effectiveness of the M-ProxJADMM algorithm in optimizing the coordinated scheduling of EVs and BESS.