Two-stage model predictive control for battery electric vehicle-centric mobile energy storage in microgrids

With the accelerated global energy transition, integrating battery electric vehicles 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 framework for real-time energy dispatch in grid-connected microgrids, in which battery electric vehicles are treated as the dominant mobile energy storage resources, while stationary battery energy storage systems serve only an auxiliary role. A dynamic electricity-pricing mechanism guides vehicle charging and discharging behavior and couples the two control stages. In the first stage, feasible charging and discharging power ranges are determined based on user preference modeling, explicitly considering state of charge, travel demand, and price responsiveness. In the second stage, a quadratic programming problem is solved to obtain the cost-minimizing dispatch strategy while satisfying power balance and operational constraints. Tidal energy is integrated with wind and PV to form a more predictable renewable portfolio. Numerical simulations demonstrate that the vehicle-dominated architecture yields an extra daily economic benefit of 494.15 ¥ over the conventional stationary storage-dominated scheme, representing an approximately 225% increase in system savings. The framework also maintains stable performance under high-load scenarios while fully satisfying travel requirements. These results provide theoretical insights into microgrid energy management and the deployment of vehicle-to-grid technologies.