Networked microgrid restoration using Reinforcement Learning

This paper explores the use of Reinforcement Learning (RL) for enhancing networked microgrid (NMG) load restoration and scheduling amid the growing integration of machine learning in energy solutions. It starts with a modified Q-learning approach to improve grid sectionalization and operational efficiency, then applies Deep Reinforcement Learning (DRL) to adeptly manage actions in varied stochastic environments. Our results highlight RL's ability to address the uncertainties in power grid restoration, thanks to its adaptive learning capabilities. The plug-and-play nature of RL also simplifies its incorporation into existing microgrid systems, offering a promising avenue for energy distribution and management in facing complex challenges.