@inproceedings{634a9a810e1447169dd58dfc4757c736,
title = "Quantum Annealing for Distribution System Restoration via Resilient Microgrids Formation",
abstract = "Microgrids (MGs) formation enables distribution networks to enhance the resilience of the system after natural disasters and faults. In this paper, a quantum computing (QC) method is devised to resolve distribution grid restorations, which establishes a promising computational platform for grid resilience applications. The new method is based upon resilient MGs formation formulated as an combinatorial optimization problem to restore critical loads after natural disasters. Our main breakthrough consists of a quantum optimization model for resilient MGs formation and restoration, as well as a quantum annealing solution to combinatorially complex problems which are difficult for classical methods to tackle. To validate the efficacy of the quantum grid restoration/MGs formation, test results obtained by D-Waveare compared with those from classical solver, Gurobi.",
keywords = "Load restoration, Microgrids, Microgrids formation, Quantum annealing, Quantum computing",
author = "Nima Nikmehr and Peng Zhang and Honghao Zheng and Shamash, \{Yacov A.\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Power and Energy Society General Meeting, PESGM 2023 ; Conference date: 16-07-2023 Through 20-07-2023",
year = "2023",
doi = "10.1109/PESGM52003.2023.10252455",
language = "English",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE Power and Energy Society General Meeting, PESGM 2023",
}