论文标题
基于深度强化学习的基于无模型的在线动态多微晶形成以增强弹性
Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience
论文作者
论文摘要
多感细胞形成（MMGF）是增强电力系统弹性的有前途解决方案。本文提出了一种新的基于无模型的在线动态多MG形成（MMGF）方案的新的深钢筋学习（RL）。动态的MMGF问题被提出为马尔可夫决策过程，并且完整的深度RL框架是专门为拓扑转换的微网格设计的。为了减少由柔性开关操作引起的大型动作空间，提出了一种拓扑转换方法，并应用了动作耦合Q值。然后，开发了基于CNN的多缓冲器双重Q-NETWORK（CM-DDQN），以进一步提高原始DQN方法的学习能力。提出的深入RL方法提供了实时计算以支持在线动态MMGF方案，该方案使用自适应在线MMGF来捍卫可变条件的长期弹性增强问题。使用7-BUS系统和IEEE 123-BUS系统验证了所提出方法的有效性。结果显示出强大的学习能力，对不同系统条件的及时反应以及令人信服的弹性增强。
Multi-microgrid formation (MMGF) is a promising solution to enhance power system resilience. This paper proposes a new deep reinforcement learning (RL) based model-free on-line dynamic multi-MG formation (MMGF) scheme. The dynamic MMGF problem is formulated as a Markov decision process, and a complete deep RL framework is specially designed for the topology-transformable micro-grids. In order to reduce the large action space caused by flexible switch operations, a topology transformation method is proposed and an action-decoupling Q-value is applied. Then, a CNN based multi-buffer double deep Q-network (CM-DDQN) is developed to further improve the learning ability of original DQN method. The proposed deep RL method provides real-time computing to support on-line dynamic MMGF scheme, and the scheme handles a long-term resilience enhancement problem using adaptive on-line MMGF to defend changeable conditions. The effectiveness of the proposed method is validated using a 7-bus system and the IEEE 123-bus system. The results show strong learning ability, timely response for varying system conditions and convincing resilience enhancement.
