Near-Optimal Decentralized Power Supply Restoration in Smart Grids

Next generation of smart grids face a number of challenges including co-generation from intermittent renewable power sources, a shift away from monolithic control due to increased market deregulation, and robust operation in the face of disasters. Such heterogeneous nature and high operational readiness requirement of smart grids necessitates decentralized control for critical tasks such as power supply restoration (PSR) after line failures. We present a novel multiagent system based approach for PSR using Lagrangian dual decomposition. Our approach works on general graphs, provides provable quality-bounds and requires only local messagepassing among different connected sub-regions of a smart grid, enabling decentralized control. Using these quality bounds, we show that our approach can provide near-optimal solutions on a number of large real-world and synthetic benchmarks. Our approach compares favorably both in solution quality and scalability with previous best multiagent PSR approach.