A multi-agent reinforcement learning method for distribution system restoration considering dynamic network reconfiguration

Abstract

Extreme weather, chain failures, and other events have increased the probability of wide-area blackouts, which highlights the importance of rapidly and efficiently restoring the affected loads. This paper proposes a multi-agent reinforcement learning method for distribution system restoration. Firstly, considering that the topology of the distribution system may change during network reconfiguration, a dynamic agent network (DAN) architecture is designed to address the challenge of input dimensions changing in neural network. Two encoders are created to capture observations of the environment and other agents respectively, and an attention mechanism is used to aggregate an arbitrary-sized neighboring agent feature set. Then, considering the operation constraints of the DSR, an action mask mechanism is implemented to filter out invalid actions, ensuring the security of the strategy. Finally, an IEEE 123-node test system is used for validation, and the experimental results showed that the proposed algorithm can effectively assist agents in accomplishing collaborative DSR tasks.