Reinforcement Learning–Based Optimal Energy Dispatch Strategy for Autonomous Smart Microgrid Systems

Abstract

The increasing integration of distributed renewable energy resources and the growing demand for decentralized power management have accelerated the development of autonomous smart microgrid systems. However, the intermittent nature of renewable energy generation and dynamic load variations create significant challenges in maintaining reliable, efficient, and economically optimized energy dispatch operations. Conventional energy management approaches often lack adaptive decision-making capability and real-time optimization performance under uncertain operating conditions. This research presents a reinforcement learning–based optimal energy dispatch strategy for autonomous smart microgrid systems to enhance energy efficiency, operational stability, and intelligent power management. The proposed framework integrates reinforcement learning algorithms with smart microgrid control architecture to enable adaptive energy scheduling and autonomous decision-making based on real-time system conditions. Renewable energy sources, battery energy storage systems, and distributed loads are incorporated into the optimization model to achieve balanced energy distribution and minimize operational costs. The reinforcement learning agent continuously interacts with the microgrid environment to learn optimal dispatch policies by analyzing load demand, renewable generation variability, energy pricing, and storage conditions.