Microgrid black start recovery strategy based on deep reinforcement learning
基于深度强化学习的微电网黑启动恢复策略

摘要

微电网黑启动恢复是电力系统恢复中的关键问题，尤其是在遭遇大规模停电或自然灾害时。为了解决这一问题，基于深度强化学习的策略被提出用于优化微电网黑启动的恢复过程。通过利用深度强化学习模型的自学习能力，系统能够从大量的历史数据和当前环境中获取有效的恢复策略，显著提高恢复速度与精确度。本研究利用深度强化学习的优势，结合微电网特点，提出了一种适应性强、效率高的黑启动恢复策略，为微电网的智能恢复和能源管理提供了新思路。实验结果表明，该方法在不同的运行环境下均表现出良好的恢复效果。

Abstract

Microgrid black start recovery is a key issue in power system restoration, especially in the event of large-scale power outages or natural disasters. To address this problem, a strategy based on deep reinforcement learning is proposed to optimize the microgrid black start recovery process. By leveraging the self-learning capability of deep reinforcement learning models, the system can obtain effective recovery strategies from a large amount of historical data and the current environment, significantly improving recovery speed and accuracy. This study takes advantage of deep reinforcement learning and combines the characteristics of microgrids to propose a highly adaptive and efficient black start recovery strategy, providing new ideas for intelligent recovery and energy management of microgrids. Experimental results show that this method achieves good recovery performance in different operating environments.