Intelligent Energy Management System for Microgrids using Reinforcement Learning

Microgrids, decentralized energy systems capable of operating independently or in conjunction with the main grid, play a crucial role in modern energy management. This paper presents a comprehensive investigation into the implementation of an Intelligent Energy Management System (IEMS) for microgrids using Reinforcement Learning (RL) techniques. Through simulation-based experiments, the performance of the RL-based system is evaluated and compared with traditional rule-based approaches across various metrics. Results indicate that the RL-based system achieves superior performance in energy efficiency, peak load management, battery utilization, cost savings, and environmental sustainability. Specifically, the system demonstrates a remarkable improvement in energy efficiency, achieving higher ratings and optimizing resource utilization compared to the baseline rule-based approach. Implementation of the RL-based system leads to substantial cost savings and an impressive return on investment (ROI), highlighting its financial viability and efficiency gains. Moreover, the system exhibits notable environmental benefits by reducing greenhouse gas emissions, increasing renewable energy integration, and promoting sustainability. The scalability and adaptability of the RL-based system make it suitable for deployment in various microgrid settings and applications. The findings underscore the significant potential of reinforcement learning-based approaches in revolutionizing microgrid operations, maximizing energy efficiency, reducing costs, and promoting environmental sustainability. These insights are valuable for policymakers, energy stakeholders, and researchers aiming to address the evolving challenges of modern energy systems and pave the way for a more sustainable energy future.