Research on energy management and optimal scheduling of microgrids based on deep reinforcement learning
基于深度强化学习的微电网能量管理与优化调度研究

摘要

微电网在能源领域的日益重要，如何实现高效的能量管理与优化调度成为了研究的热点。深度强化学习（DRL）作为一种先进的人工智能技术，在智能控制和决策领域表现出显著优势。本文基于深度强化学习算法，提出了一种新的微电网能量管理与优化调度方案，旨在提高微电网运行的经济性和稳定性。通过建立微电网系统模型，并应用深度强化学习算法进行在线优化调度，能够有效平衡各能源源之间的负荷调度，降低能源损失。实验结果验证了该方案的可行性和优越性，为微电网的智能化管理提供了有力支持。

Abstract

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.