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

arXiv: https://arxiv.org/abs/1911.02206 · HTML: https://mlsanity.com/paper/1911.02206/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Exploring a Physics-Informed Decision Transformer for Distribution System Restoration: Methodology and Performance Analysis](https://mlsanity.com/paper/2407.00808/) (2024)
- [Dual-Head Physics-Informed Graph Decision Transformer for Distribution System Restoration](https://mlsanity.com/paper/2508.06634/) (2025)

## Abstract (arXiv)

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.