Journal of Electrical Engineering and Automation

Microgrids have become increasingly important in the energy field, and how to achieve efficient energy management and optimal scheduling has become a research hotspot. As an advanced artificial intelligence technology, Deep Reinforcement Learning (DRL) has demonstrated significant advantages in the fields of intelligent control and decision-making. Based on the deep reinforcement learning algorithm, this paper proposes a new energy management and optimal scheduling scheme for microgrids, aiming to improve the economic efficiency and stability of microgrid operation. By establishing a microgrid system model and applying the deep reinforcement learning algorithm for online optimal scheduling, it is possible to effectively balance the load scheduling among various energy sources and reduce energy losses. The experimental results verify the feasibility and superiority of this scheme, providing strong support for the intelligent management of microgrids.

Deep reinforcement learning; Microgrid; Energy management; Optimal scheduling; Intelligent control