MPC-driven optimal scheduling of grid-connected microgrid: Cost and degradation minimization with PEVs integration

The lifespan and degradation of energy storage systems are important factors in ensuring efficient energy management and reducing operational costs in microgrids. This paper proposes a model predictive control (MPC)-based optimization framework aimed at minimizing operational costs while mitigating battery degradation and extending the lifespan of energy storage systems. The optimization is formulated within the MPC framework, accounting for costs related to battery and plug-in electric vehicle (PEV) charging/discharging, as well as grid purchases and sales. Furthermore, the model introduces a control penalty mechanism to manage the state of charge (SoC) of batteries and PEVs within optimal limits by penalizing constraint violations. This contributes to extending lifespan of energy system and reduces storage capacity degradation. To address uncertainties in renewable energy generation, Monte–Carlo simulations generate multiple scenarios. Scenario reduction techniques are applied to ensure computational efficiency. The optimization problem is addressed using quadratic programming, which effectively solves the multi-objective optimization problem and manages constraints. Simulation results demonstrate that the proposed framework effectively manages uncertainties while reducing operational costs. Additionally, it enhances the state of health (SOH) retention of storage systems by approximately 46.67% and extends cyclic life by 73.66% compared to conventional methods. This verifies the effectiveness of proposed method.