Real-Time Energy Management in Microgrids Using Reinforcement Learning

Real-time energy management in microgrids is crucial for ensuring reliable power distribution, cost efficiency, and sustainability. Reinforcement learning offers adaptive decision-making to optimize demand-supply coordination in dynamic environments. However, existing methods often face challenges such as limited scalability, delayed response to fluctuations, and inadequate prioritization of critical loads. To overcome these limitations, this work proposes a Hierarchical Multi-Agent Reinforcement Learning (HMARL) framework that integrates dynamic priority scheduling for efficient real-time demand-supply balancing. The proposed approach enables distributed agents to learn cooperative strategies, ensuring resilience and adaptability under varying load and generation conditions. The framework effectively enhances resource allocation, reduces operational costs, and improves the reliability of microgrid operations. Experimental results demonstrate that HMARL achieves faster convergence, superior load prioritization, and robust performance in handling real-time uncertainties compared to conventional methods, thereby supporting stable and efficient microgrid management.