Post-Disaster Distribution System Restoration Considering UAV-Based Communication Recovery Based on Multi-Agent Reinforcement Learning

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Abstract
Due to the coupling characteristics of the physical system and the communication system, distribution network fault scenarios and post-disaster recovery procedures become more complicated in the aftermath of catastrophic natural disasters. Consideration of communication system restoration by unmanned aerial vehicle base station (UAV-BS) can effectively reduce distribution network outage duration. This paper proposes a post-disaster recovery strategy for distribution networks that takes UAV-based communication recovery into account. Consideration is given to the cooperation of multiple UAV-BSs in the recovery process using multi-agent reinforcement learning (MA-RL). The communication recovery procedure of multiple UAVs is initially converted into a Markov decision process (MDP). To actualize agent interaction, the distribution network reconfiguration model is constructed as a reinforcement learning environment that takes into account communication constraints. The problem is resolved by MA-RL, and the effectiveness of the proposed strategy is evaluated by IEEE 33-bus system.
Funding Information
  • National Key R&D Program of China (2018YFA0702200)

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