Drone-Assisted Cyber-Physical Stability-Constrained Resilience Enhancement for Postcontingency Distribution Networks

Extreme events, such as earthquakes and hurricanes, can simultaneously damage power systems and communication networks, making it difficult to restore either system independently due to their increasing cyber-physical interdependence. This article investigates the use of drones to establish an emergency communication network that supports postcontingency distribution network operation. An end-to-end learning-based, cyber-physical stability-constrained framework is proposed for emergency cyber network formation. In this framework, drones are strategically deployed across the distribution network to enable postcontingency communication and coordinate distributed energy resources. The framework incorporates multidrone connected communication optimization, wireless resource allocation, cyber-physical network modeling, and stability criteria extraction. These processes introduce significant computational complexity, including NP-hard challenges and nonlinearities, which are difficult to solve efficiently using traditional optimization methods. To address these limitations, this article develops an end-to-end deep reinforcement learning algorithm that determines optimal multidrone trajectories from the depot, providing a feasible multidrone deployment solution that ensures a stable emergency energy supply.