A Cyber-Physical Framework for Drone-Based Mobile Energy Storage: Spatio-Temporal Optimization for Resilience and Lifecycle Viability

Abstract

Energy supply risks are growing, driven by the massive integration of intermittent renewable energy and the more frequent extreme events under climate change. We propose a decision model for drone-based portable energy storage systems (DPESS), conceptualizing them as highly flexible, topology-independent Cyber-Physical Distributed Energy Resources (DERs). We develop optimization models incorporating energy and transportation constraints for UAVs. Experimental results show that, in grid-connected economic scheduling applications (i.e., energy arbitrage), by dynamically dispatching energy storage units across nodes with price differences to arbitrage, DPESS significantly mitigates the asset idleness. In emergency support, DPESS could reduce load shedding losses by over 55% compared to trucks, covering 100% of critical nodes with an average response time of 2.1 hours (versus 8.7 hours for trucks). This work provides a theoretical foundation and technical roadmap for UAV applications in enhancing grid flexibility and disaster resilience.