Two-Stage Robust Scheduling of Microgrid

To enhance the penetration of distributed generation in the grid, microgrid technology has emerged, addressing the randomness of renewable energy and demand response. Despite advances in AI-based forecasting, prediction accuracy remains insufficient for microgrid scheduling. Robust optimization methods, which consider uncertainties in pre-scheduling and rescheduling, have been developed to ensure optimal scheduling and improve renewable energy utilization. This study proposes a two-stage robust optimization model for microgrids, focusing on economic efficiency and robustness, validated through simulations, showing effectiveness in reducing wind and solar curtailment and load shedding risks.