Two-Time Scale Microgrid Scheduling Based on Power Fluctuation Mitigation Priority and Model Predictive Control

Abstract

With the increasing penetration of renewable energy and the increasing randomness of new energy vehicles, microgrids are facing new challenges in achieving optimal scheduling, and maintaining power supply stability and economy. This paper proposes a day-ahead and intra-day dual-time scale power scheduling strategy based on model predictive control. In the day-ahead stage, considering the short-term prediction information of renewable energy and load, the day-ahead scheduling plan is established with the goal of minimizing the total operating cost of the microgrid, and the optimal exchange power values of each component including Electric Vehicles (EVs) and battery energy storage and the large power grid are obtained. In the intra-day stage, considering the ultra-short-term power prediction information, the smoothing power priority method is embedded into the rolling optimization strategy based on model predictive control, and the different smoothing priorities of hybrid energy storage and EVs are designed. To track the day-ahead scheduling plan and minimize the power adjustment, the closed-loop control is realized to obtain the optimal output of each component. The simulation results show that the proposed scheduling strategy has lower operating costs and better translation power fluctuation effect under large and small power fluctuations.

Keywords: Microgrid， Renewable energy，Electric vehicles， Hybrid energy storage， Model predictive control