  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.
