Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid

In this paper, we present a distributed privacy-preserving quadratic optimization algorithm to solve the Security Constrained Optimal Power Flow (SCOPF) problem in the smart grid. The SCOPF problem seeks the optimal dispatch subject to a set of postulated constraints under the normal and contingency...

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Autores principales:	Xiangyu Niu, Hung Khanh Nguyen, Jinyuan Sun, Zhu Han
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Optimal power flow privacy-preserving security cloud computing smart grid distributed systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/19016b0c7a074717a174577b10797c91
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spelling	oai:doaj.org-article:19016b0c7a074717a174577b10797c912021-11-18T00:11:13ZPrivacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid2169-353610.1109/ACCESS.2021.3119618https://doaj.org/article/19016b0c7a074717a174577b10797c912021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9568896/https://doaj.org/toc/2169-3536In this paper, we present a distributed privacy-preserving quadratic optimization algorithm to solve the Security Constrained Optimal Power Flow (SCOPF) problem in the smart grid. The SCOPF problem seeks the optimal dispatch subject to a set of postulated constraints under the normal and contingency conditions. However, due to the large problem size and real-time requirement, a fast and robust technique is required to solve this problem. Moreover, due to privacy concerns, it is important that the data remains confidential and processed on local computers. Therefore, a fully privacy-preserving algorithm is proposed which performs computation directly over the encrypted SCOPF problem. The SCOPF is decomposed into smaller subproblems corresponding to individual pre-contingency and post-contingency cases using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. Both algorithms are presented for solving the SCOPF problem in a privacy-preserving and distributed manner. Security analysis shows that our algorithm can preserve both system confidentiality and data privacy. Performance evaluations validate the correctness and effectiveness of the proposed algorithm.Xiangyu NiuHung Khanh NguyenJinyuan SunZhu HanIEEEarticleOptimal power flowprivacy-preservingsecuritycloud computingsmart griddistributed systemsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148144-148155 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Optimal power flow privacy-preserving security cloud computing smart grid distributed systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Optimal power flow privacy-preserving security cloud computing smart grid distributed systems Electrical engineering. Electronics. Nuclear engineering TK1-9971 Xiangyu Niu Hung Khanh Nguyen Jinyuan Sun Zhu Han Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid
description	In this paper, we present a distributed privacy-preserving quadratic optimization algorithm to solve the Security Constrained Optimal Power Flow (SCOPF) problem in the smart grid. The SCOPF problem seeks the optimal dispatch subject to a set of postulated constraints under the normal and contingency conditions. However, due to the large problem size and real-time requirement, a fast and robust technique is required to solve this problem. Moreover, due to privacy concerns, it is important that the data remains confidential and processed on local computers. Therefore, a fully privacy-preserving algorithm is proposed which performs computation directly over the encrypted SCOPF problem. The SCOPF is decomposed into smaller subproblems corresponding to individual pre-contingency and post-contingency cases using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. Both algorithms are presented for solving the SCOPF problem in a privacy-preserving and distributed manner. Security analysis shows that our algorithm can preserve both system confidentiality and data privacy. Performance evaluations validate the correctness and effectiveness of the proposed algorithm.
format	article
author	Xiangyu Niu Hung Khanh Nguyen Jinyuan Sun Zhu Han
author_facet	Xiangyu Niu Hung Khanh Nguyen Jinyuan Sun Zhu Han
author_sort	Xiangyu Niu
title	Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid
title_short	Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid
title_full	Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid
title_fullStr	Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid
title_full_unstemmed	Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid
title_sort	privacy-preserving computation for large-scale security-constrained optimal power flow problem in smart grid
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/19016b0c7a074717a174577b10797c91
work_keys_str_mv	AT xiangyuniu privacypreservingcomputationforlargescalesecurityconstrainedoptimalpowerflowprobleminsmartgrid AT hungkhanhnguyen privacypreservingcomputationforlargescalesecurityconstrainedoptimalpowerflowprobleminsmartgrid AT jinyuansun privacypreservingcomputationforlargescalesecurityconstrainedoptimalpowerflowprobleminsmartgrid AT zhuhan privacypreservingcomputationforlargescalesecurityconstrainedoptimalpowerflowprobleminsmartgrid
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Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid

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