Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2015
Abstract
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 message-passing 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.
Keywords
Distributed constraint optimization, Multi-agent systems, Power restoration, Smart grids
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Publication
AAMAS '15: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems: Istanbul, Turkey, May 4-8
First Page
1275
Last Page
1283
ISBN
9781450334136
Publisher
IFAAMAS
City or Country
Richland, SC
Citation
AGRAWAL, Pritee; Akshat KUMAR; and Pradeep VARAKANTHAM.
Near-Optimal Decentralized Power Supply Restoration in Smart Grids. (2015). AAMAS '15: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems: Istanbul, Turkey, May 4-8. 1275-1283.
Available at: https://ink.library.smu.edu.sg/sis_research/3156
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://dl.acm.org/citation.cfm?id=2772879.2773315
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons