Machine Learning for Microgrids: Resiliency, Stability, Control, and Operation

Microgrids are a critical component in the transition toward a sustainable, reliable, and resilient electricity supply. By definition, microgrids can operate in both grid-connected and islanded modes, seamlessly transitioning between the two without interrupting the power supply. These capabilities position microgrids as effective solutions for enhancing resiliency against increasingly frequent and severe events such as extreme weather, cyber threats, and grid disruptions. Nonetheless, achieving optimal operation and stable control within microgrids can be challenging due to inherent physical characteristics, complex dynamic behaviors, and operational uncertainties, particularly in systems dominated by inverter-based and stochastic energy sources. Consequently, there is growing research interest in leveraging machine learning (ML)-based methods for the control and operation of microgrids. Techniques such as deep reinforcement learning and imitation-based control have demonstrated potential for enabling real-time, adaptive decision-making tailored to rapidly evolving microgrid conditions. This IEEE PES Trending Technologies explores the intersection of microgrid and ML research, highlighting state-of-the-art solutions, ongoing research trends, and future opportunities for intelligent microgrid management within increasingly complex electricity landscapes.