A Coordinated Model Predictive Control-Based Approach for Vehicle-to-Grid Scheduling Considering Range Anxiety and Battery Degradation

Abstract

Electric vehicles (EVs) spend the majority of their operational lifecycle in a state of nonuse, which provides sufficient time and flexibility for charging. Utilizing this flexibility with vehicle-to-grid (V2G) technology not only supports grid stability but also offers additional revenue for vehicle owners. However, V2G scheduling based solely on maximizing the individual profits of EVs will lead to a high cost of peak demand charge. Moreover, concerns about range anxiety, battery degradation, and the computational burden of optimization have constrained the widespread of V2G technology. To address these challenges, a coordinated model predictive control (CMPC)-based scheduling scheme is proposed while considering users’ range anxiety and battery degradation model. A mixed integer linear programming (MILP) is developed to consider range anxiety, battery degradation model, and constant current-constant voltage (CC-CV) charging mode. The CMPC method is proposed to avoid the penalty of peak demand charge by coordinating V2G charging among chargers and to mitigate sharp fluctuations in power by adding a penalty term in the power change rate. Instead of optimizing calculations for each time stage, a control package strategy and an online rolling optimization method are presented to reduce computational burden and consider stochastic charging demand arrivals. Simulations and hardware experiments are performed to demonstrate the effectiveness and feasibility of the proposed scheme.