Reinforcement learning-assisted distributionally robust energy management for multi-microgrid networks (opens in new tab)

This paper proposes a hybrid reinforcement learning–assisted distributionally robust optimization (RL–DRO) framework for robust and economically efficient energy management in interconnected multi-microgrid systems under renewable, demand, and price uncertainty. The framework integrates deep reinforcement learning to generate adaptive scheduling policies with a Wasserstein-metric distributionally robust optimization formulation that enhances robustness against probability distribution shifts ...