Integrating UAVs and AI for Resilient Smart Energy Systems in Disaster
Keywords:
UAV-BS placement; MORL; multi-objective optimization; smart grid restoration; disaster management; energy efficiency; system throughputAbstract
Natural disasters pose serious challenges to smart grid infrastructure by simultaneously disrupting power and communication systems, leading to isolated microgrids or “islands”. To support post-disaster recovery, this paper presents a multi-objective optimization framework for the efficient placement of unmanned aerial vehicle base stations (UAV-BSs) as mobile relay nodes connecting power sources (PSs) and static base stations (SBSs). The proposed method jointly optimizes geographical UAV-BS positions considering some conflicting objectives: minimizing the number of UAV-BSs and unserved PSs (NAPS), maximizing system throughput and energy efficiency. An enhanced Multi-Objective Reinforcement Learning (MORL) with clustering-based initialization is developed to improve convergence and solution diversity. Simulation results on the Simbench dataset confirm the effectiveness of the proposed approach in achieving robust, energy-efficient, and cost-effective UAV-BS deployment for smart grid restoration.
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Copyright (c) 2025 Muqtada Zuhair Ali, Jamshid Bagherzadeh (Author); Parviz Rashidi-Khazaee (Corresponding author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
