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Mobile energy storage systems (MESSs) provide mobility and flexibility toenhance distribution system resilience. The paper proposes a Markov decisionprocess (MDP) formulation for an integrated service restoration strategy thatcoordinates the scheduling of MESSs and resource dispatching of microgrids. Theuncertainties in load consumption are taken into account. The deepreinforcement learning (DRL) algorithm is utilized to solve the MDP for optimalscheduling. Specifically, the twin delayed deep deterministic policy gradient(TD3) is applied to train the deep Q-network and policy network, then the welltrained policy can be deployed in on-line manner to perform multiple actionssimultaneously. The proposed model is demonstrated on an integrated test systemwith three microgrids connected by Sioux Falls transportation network. Thesimulation results indicate that mobile and stationary energy resources can bewell coordinated to improve system resilience.

Resilient Load Restoration in Microgrids Considering Mobile Energy Storage Fleets: A Deep Reinforcement Learning Approach