Two-time scale microgrid scheduling based on power fluctuation mitigation priority and model predictive control

With the continuous increase in the penetration rate of renewable energy and the growing randomness of new energy electric vehicles, microgrids face new challenges in achieving optimal scheduling, maintaining power supply stability, and economic viability. This paper proposes a dual-time-scale power scheduling strategy based on model predictive control. In the day-ahead stage, considering the short-term forecast information of renewable energy and load, the optimal scheduling model is established with the lowest total operating cost of the microgrid as the objective, obtaining the optimal exchange power values of various components including electric vehicles and battery energy storage systems, as well as the main grid. In the intra-day stage, considering ultra-short-term power forecast information, the priority method for power smoothing is embedded into the rolling optimization strategy based on model predictive control. Different smoothing priorities for hybrid energy storage and electric vehicles are designed to track the day-ahead scheduling plan and minimize power adjustment, aiming to achieve closed-loop control and obtain the optimal output of each component. Various case studies validate the effectiveness and performance of the proposed strategy.