An economic model predictive control strategy for EV-integrated microgrids considering battery degradation

The increasing penetration of renewable energy introduces volatility, which poses significant challenges to the stability of the microgrid. The electric vehicle (EV) fleet can serve as distributed energy storage to smooth these fluctuations, while Vehicle-to-Grid (V2G) technology enables stable operation of both the microgrid and the EV fleet. However, existing V2G scheduling strategies, which are predominantly based on hierarchical model predictive control (MPC) frameworks, often overlook system-level economic dynamics, potentially resulting in constraint violations and accelerated battery degradation. This study proposes a novel scheduling strategy based on economic MPC (EMPC), which integrates microgrid energy management with coordinated decentralized scheduling of EV charging and discharging in a single control framework, while explicitly considering the dynamics of battery state-of-charge (SOC) and degradation. Considering the presence of both binary and continuous variables, as well as the time-varying nature of the decision space, an improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to efficiently solve the mixed-integer nonlinear programming (MINLP) problem. Simulation results verify that, the proposed approach significantly enhances microgrid operational stability, mitigates battery degradation, ensures user cost-effectiveness, and improves overall economic performance compared to traditional hierarchical methods.