  Mobile energy storage systems (MESSs) provide mobility and flexibility to
enhance distribution system resilience. The paper proposes a Markov decision
process (MDP) formulation for an integrated service restoration strategy that
coordinates the scheduling of MESSs and resource dispatching of microgrids. The
uncertainties in load consumption are taken into account. The deep
reinforcement learning (DRL) algorithm is utilized to solve the MDP for optimal
scheduling. Specifically, the twin delayed deep deterministic policy gradient
(TD3) is applied to train the deep Q-network and policy network, then the well
trained policy can be deployed in on-line manner to perform multiple actions
simultaneously. The proposed model is demonstrated on an integrated test system
with three microgrids connected by Sioux Falls transportation network. The
simulation results indicate that mobile and stationary energy resources can be
well coordinated to improve system resilience.
