Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31525
Title: Privacy-preserving distributed optimization for economic dispatch in smart grids
Authors: An, W
Ding, D
Wang, Z
Liu, Q
Dong, H
Keywords: distributed optimization;economic dispatch problems;data privacy;smart grids
Issue Date: 9-Apr-2025
Publisher: Elsevier
Citation: An, W. et al. (2025) 'Privacy-preserving distributed optimization for economic dispatch in smart grids', Automatica, 177, 112275, pp. 1 - 9. doi: 10.1016/j.automatica.2025.112275.
Abstract: This paper discusses a distributed economic dispatch problem (EDP) of smart grids while preventing sensitive information from being leaked during the communication process. In response to the problem, a novel privacy-preserving distributed economic dispatch strategy is developed via adding an exponentially decaying random noise to minimize the total cost of the grid while ensuring the privacy of sensitive state information. The quantitative relationship between the privacy and the estimation accuracy of eavesdroppers is profoundly disclosed in the framework of (ς, σ)-data-privacy. Furthermore, a sufficient condition on the iteration step size is achieved to ensure that the well-designed algorithm can converge to the optimal value of the addressed EDP exactly by resorting to the classical Lyapunov stability theory. Finally, simulation results verify the effectiveness of the carefully constructed privacy-preserving scheme.
Description: The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Daniele Casagrande under the direction of Editor Florian Dorfler.
URI: https://bura.brunel.ac.uk/handle/2438/31525
DOI: https://doi.org/10.1016/j.automatica.2025.112275
ISSN: 0005-1098
Other Identifiers: ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401
112275
Appears in Collections:Dept of Computer Science Embargoed Research Papers

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