Zafra-Cabeza, Ascension , Hernandez-Rivera, Andres , VELARDE RUEDA, PABLO ANIBAL, Bordons, Carlos , Ridao, Miguel A.
No
Smart Grids Sustain. Energy.
Article
Científica
3
2
16/03/2026
001715942600001
2-s2.0-105033452119
The increasing integration of renewable energy sources (RESs) and the growing complexity of modern power systems have underscored the need for resilience-oriented microgrid planning and operation. Microgrids, as decentralized energy systems, remain vulnerable to uncertainties, cyber-physical threats, and extreme weather events. To address these challenges, optimal scheduling strategies must be developed to ensure efficient energy dispatch, fault tolerance, and risk-aware decision-making under uncertain conditions and in different time scales. This paper presents a comprehensive 2-layer hierarchical Model Predictive Control (MPC) framework designed to address these issues on two different time scales. The lower layer includes two parallel MPC controllers: one for optimizing energy dispatch and another for optimizing the reconfiguration of the microgrid based on fault occurrences. The latter controller operates with a multicriteria function that is executed when faults are detected, utilizing structured residuals and stochastic thresholds. The upper layer develops an optimal mitigation risk-based strategy considering external information. The key contributions of this work include enhancing microgrid resilience, utilizing dual MPC controllers for fault and risk optimization, unifying terminology in the optimization problems, and designing an interactive collaboration between the two layers that improves the learning capabilities of the entire scheme. The effectiveness of the framework is demonstrated through experiments on a real microgrid, incorporating renewable energy sources, battery storage systems, and a hydrogen-based energy storage system. The results indicate that the hierarchical MPC framework effectively maintains microgrid stability and efficiency under various operational scenarios and uncertainty conditions.
Microgrids; Energy management system; Risk management; Model predictive control; Fault diagnosis; Hierarchical control