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Title: | Privacy-Preserving Distributed Economic Dispatch of Microgrids Using Edge-Based Additive Perturbations: An Accelerated Consensus Algorithm |
Authors: | Chen, W Wang, Z Hu, J Han, Q-L Liu, G-P |
Keywords: | accelerated consensus algorithm;distributed economic dispatch (DED);edge-based additive perturbations;microgrids;privacy preservation |
Issue Date: | 24-Jan-2024 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Chen, W. et al. (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, 0 (early access), pp. 1 - 13. doi: 10.1109/TSMC.2023.3344885. |
Abstract: | 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. |
URI: | https://bura.brunel.ac.uk/handle/2438/28577 |
DOI: | https://doi.org/10.1109/TSMC.2023.3344885 |
ISSN: | 2168-2216 |
Other Identifiers: | ORCiD: Wei Chen https://orcid.org/0000-0002-6225-2110 ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 ORCiD: Jun Hu https://orcid.org/0000-0002-7852-5064 ORCiD: Qing-Long Han https://orcid.org/0000-0002-7207-0716 ORCiD: Guo-Ping Liu https://orcid.org/0000-0002-0699-2296 |
Appears in Collections: | Dept of Computer Science Research Papers |
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