Deep Reinforcement Learning-Based Model-Free On-Line Dynamic Multi-Microgrid Formation to Enhance Resilience

Multi-microgrid formation (MMGF) is a promising solution for enhancing power system resilience. This paper proposes a new deep reinforcement learning (RL) based model-free on-line dynamic MMGF scheme. The dynamic MMGF problem is formulated as a Markov decision process, and a complete deep RL framewo...

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Bibliographic Details
Published in:	IEEE transactions on smart grid 2022-07, Vol.13 (4), p.2557-2567
Main Authors:	Zhao, Jin, Li, Fangxing, Mukherjee, Srijib, Sticht, Christopher
Format:	Article
Language:	English
Subjects:	Artificial neural networks convolutional neural network Convolutional neural network (CNN) Decoupling Deep learning deep reinforcement learning Deep reinforcement learning (DRL) Distributed generation distributed generation (DG) extreme weather Markov processes Meteorology microgrids microgrids (MGs) multi-microgrid formation multi-microgrid formation (MMGF) Network topology Power system dynamics power system resilience POWER TRANSMISSION AND DISTRIBUTION Real-time systems Resilience Switches Topology
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