Near-Optimal Decentralized Power Supply Restoration in Smart Grids

Next generation of smart grids face a number of challenges includ-ing co-generation from intermittent renewable power sources, a shift away from monolithic control due to increased market dereg-ulation, and robust operation in the face of disasters. Such het-erogeneous 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, pro-vides provable quality-bounds and requires only local message-passing among different connected sub-regions of a smart grid, en-abling decentralized control. Using these quality bounds, we show that our approach can provide near-optimal solutions on a num-ber of large real-world and synthetic benchmarks. Our approach compares favorably both in solution quality and scalability with previous best multiagent PSR approach