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Quantum-Secure Microgrid

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Journal 01 Mar 2021Embargo end date: 01 Jan 2020Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Power Systems, volume 36, pages 1,250-1,263 (issn: 0885-8950, eissn: 1558-0679,

Authors: Zefan Tang; Yanyuan Qin; Zimin Jiang; Walter O. Krawec; Peng Zhang;

doi: 10.1109/tpwrs.2020.3011071 , 10.48550/arxiv.2001.02301

arXiv: http://arxiv.org/abs/2001.02301 , 2001.02301

Quantum-Secure Microgrid

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Abstract

Existing microgrid communication relies on classical public key systems, which are vulnerable to attacks from quantum computers. This paper uses quantum key distribution (QKD) to solve these quantum-era microgrid challenges. Specifically, this paper makes the following novel contributions: 1) it offers a QKD-based microgrid communication architecture for microgrids; 2) it shows how to build a quantum-secure microgrid testbed in an RTDS environment; 3) it develops a key pool sharing (KPS) strategy to improve the cyberattack resilience of the QKD-based microgrid; and 4) it analyzes the impacts of critical QKD parameters with the testbed. Test results provide insightful resources for building a quantum-secure microgrid.

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Keywords

FOS: Computer and information sciences, Quantum Physics, Computer Science - Cryptography and Security, FOS: Physical sciences, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, FOS: Electrical engineering, electronic engineering, information engineering, Quantum Physics (quant-ph), Cryptography and Security (cs.CR)

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	33
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%