Two-Stage Model Predictive Control for Battery Electric Vehicle-Centric Mobile Energy Storage in Microgrids

With the accelerated global energy transition, the integration of plug-in electric vehicles (BEVs) into microgrid systems has emerged as a promising approach to enhance operational flexibility and reduce energy storage infrastructure costs. This paper proposes a two-stage model predictive control (MPC) framework for real-time energy dispatch in grid-connected microgrids, where BEVs operating in pure electric mode (namely battery electric vehicles) are treated as dominant mobile energy storage resources. The proposed framework guides BEV charging and discharging behavior through a dynamic electricity pricing mechanism, enabling cost-efficient coordination between mobile storage, stationary battery energy storage systems (BESS), and the main grid.In contrast to conventional BESS-centric energy management strategies, the proposed approach prioritizes BEV-based mobile storage, with BESS serving only as auxiliary support. In the first stage, feasible charging and discharging power ranges of BEVs are determined based on user preference modeling, explicitly considering state of charge (SOC), travel demand, and price responsiveness. In the second stage, a quadratic programming (QP) problem is solved to obtain the cost-minimizing dispatch strategy while satisfying power balance and operational constraints. In addition, tidal energy is integrated with conventional wind and photovoltaic generation to construct a diversified and more predictable renewable energy portfolio.Numerical simulations demonstrate that, under normal operating conditions, the BEV-dominated energy storage architecture achieves a 55.17% reduction in total system cost compared with a traditional BESS-dominated scheme. Moreover, the proposed framework maintains stable performance under high-load scenarios while fully satisfying all BEV travel requirements. These results provide theoretical insights into microgrid energy management and offer practical references for the deployment of vehicle-to-grid (V2G) technologies.