Privacy-Preserving Distributed Economic Dispatch of Microgrids Using Edge-Based Additive Perturbations: An Accelerated Consensus Algorithm

Chen，Wei1; Wang，Zidong2; Hu，Jun3; Han，Qing Long4; Liu，Guo Ping1

2024

10.1109/TSMC.2023.3344885

IEEE Transactions on Systems, Man, and Cybernetics: Systems   影响因子和分区

2168-2216

2168-2232

This article investigates the privacy-preserving distributed economic dispatch (DED) problem of islanded microgrids. To improve the convergence rate of the DED algorithm, an accelerated consensus scheme is adopted by utilizing a short memory. Then, a privacy-preserving strategy is introduced to prevent sensitive information leakage by adding well-designed perturbations into the proposed consensus algorithm at the initial time instant. The primary objective of this article is to design a privacy-preserving accelerated consensus scheme to achieve a balance between supply and demand at the globally minimized cost while preserving the initial local demand information. By virtue of rigorous algebra manipulation and mathematical induction, a unified framework is established under which the convergence, the optimal convergence rate, and the optimality of the proposed DED algorithm are simultaneously analyzed, and the main results are extended to satisfy the privacy-preserving needs. Furthermore, the proposed privacy-preserving DED algorithm is shown to be resilient against both internal (honest-but-curious) and external eavesdroppers. Finally, the effectiveness of the developed privacy-preserving accelerated consensus algorithm is validated on the IEEE 39-bus power systems.

Accelerated consensus algorithm Additives Consensus algorithm Convergence Costs distributed economic dispatch (DED) edge-based additive perturbations Microgrids microgrids Perturbation methods Privacy privacy preservation

SCI ; EI

英语

第一

2-s2.0-85183962811

Scopus

1.Shenzhen Key Laboratory of Control Theory and Intelligent Systems, Southern University of Science and Technology, Shenzhen, China
2.Department of Computer Science, Brunel University London, Uxbridge, U.K
3.Department of Mathematics and the Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems, Harbin University of Science and Technology, Harbin, China
4.School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC, Australia

Chen，Wei,Wang，Zidong,Hu，Jun,et al. Privacy-Preserving Distributed Economic Dispatch of Microgrids Using Edge-Based Additive Perturbations: An Accelerated Consensus Algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems,2024,PP(99):1-13.

Chen，Wei,Wang，Zidong,Hu，Jun,Han，Qing Long,&Liu，Guo Ping.(2024).Privacy-Preserving Distributed Economic Dispatch of Microgrids Using Edge-Based Additive Perturbations: An Accelerated Consensus Algorithm.IEEE Transactions on Systems, Man, and Cybernetics: Systems,PP(99),1-13.

Chen，Wei,et al."Privacy-Preserving Distributed Economic Dispatch of Microgrids Using Edge-Based Additive Perturbations: An Accelerated Consensus Algorithm".IEEE Transactions on Systems, Man, and Cybernetics: Systems PP.99(2024):1-13.