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Multi-microgrid formation (MMGF) is a promising solution to enhance powersystem resilience. This paper proposes a new deep reinforcement learning (RL)based model-free on-line dynamic multi-MG formation (MMGF) scheme. The dynamicMMGF problem is formulated as a Markov decision process, and a complete deep RLframework is specially designed for the topology-transformable micro-grids. Inorder to reduce the large action space caused by flexible switch operations, atopology transformation method is proposed and an action-decoupling Q-value isapplied. Then, a CNN based multi-buffer double deep Q-network (CM-DDQN) isdeveloped to further improve the learning ability of original DQN method. Theproposed deep RL method provides real-time computing to support on-line dynamicMMGF scheme, and the scheme handles a long-term resilience enhancement problemusing adaptive on-line MMGF to defend changeable conditions. The effectivenessof the proposed method is validated using a 7-bus system and the IEEE 123-bussystem. The results show strong learning ability, timely response for varyingsystem conditions and convincing resilience enhancement.

Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience