Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31525
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dc.contributor.authorAn, W-
dc.contributor.authorDing, D-
dc.contributor.authorWang, Z-
dc.contributor.authorLiu, Q-
dc.contributor.authorDong, H-
dc.date.accessioned2025-07-10T08:03:26Z-
dc.date.available2025-07-10T08:03:26Z-
dc.date.issued2025-04-09-
dc.identifierORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401-
dc.identifier112275-
dc.identifier.citationAn, 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.en_US
dc.identifier.issn0005-1098-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/31525-
dc.descriptionThe 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.en_US
dc.description.abstractThis 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.en_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grants 62373251, U21A2019, 62222312 and 62473285; in part by the National Key Research and Development Program of China under Grant 2022YFB4501704; in part by the Shanghai Science and Technology Innovation Action Plan Project of China under Grant 22511100700; and in part by Fundamental Research Funds for the Central Universities .en_US
dc.format.extent1 - 9-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subjectdistributed optimizationen_US
dc.subjecteconomic dispatch problemsen_US
dc.subjectdata privacyen_US
dc.subjectsmart gridsen_US
dc.titlePrivacy-preserving distributed optimization for economic dispatch in smart gridsen_US
dc.typeArticleen_US
dc.date.dateAccepted2025-03-18-
dc.identifier.doihttps://doi.org/10.1016/j.automatica.2025.112275-
dc.relation.isPartOfAutomatica-
pubs.publication-statusPublished-
pubs.volume177-
dc.identifier.eissn1873-2836-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.en-
dcterms.dateAccepted2025-03-18-
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