Economic Optimal Scheduling of Microgrids Based on Model Predictive Control

Microgrids, as independent and controllable power systems, are key to integrating renewable energy sources. However, the stochastic nature of renewables and load fluctuations complicates dispatch modeling. This study addresses system uncertainties by developing a two-level stochastic programming model with compensation and proposes a model predictive control (MPC)-based algorithm to solve it. Simulation results show that the proposed method reduces operating costs by 10.9% compared to traditional deterministic models, demonstrating improved operational economy and practical application potential.