Comparative Analysis of Control Strategies for Microgrid Energy Management with a Focus on Reinforcement Learning

Abstract

The depletion of fossil fuel reserves and the urgent need to cut greenhouse gas emissions are driving a significant shift in the global energy sector. This transformation necessitates advanced energy management strategies in microgrids to integrate renewable energy sources efficiently. Traditional methods, including classical, metaheuristic, model predictive, stochastic, and robust control techniques, have been extensively studied. However, these approaches often struggle with slow performance and high computational demands, making them less effective for real-time applications due to the need for frequent re-optimization. In contrast, reinforcement learning, a branch of machine learning, excels by continuously learning and optimizing through real-time interactions This approach offers greater flexibility and adaptability in complex and dynamic environments. This paper provides a comparative review of various control strategies for microgrid energy management, examining their strengths and limitations. We place a particular focus on reinforcement learning algorithms and their application in microgrids. This paper conducts a detailed analysis of studies on reinforcement learning-based energy management systems, evaluating their potential to enhance energy efficiency, optimize usage, and contribute to the sustainability of modern energy infrastructures. This review aims to enhance understanding in this field and offer insights for future research on smart energy management systems.

Keywords

• Demand response
• energy management
• microgrids
• reinforcement learning