{"title":"多代理主动配电网络的分散式最佳功率流：一种差分私有共识 ADMM 算法","authors":"Chao Lei;Siqi Bu;Qifan Chen;Qianggang Wang;Qin Wang;Dipti Srinivasan","doi":"10.1109/TSG.2024.3451793","DOIUrl":null,"url":null,"abstract":"In multi-agent active distribution networks, the information exchanges in the ADMM algorithm for the decentralized distribution-level optimal power flow (D-OPF) may expose sensitive load flows of tie-lines across adjacent agents. This may be overheard by adversarial agents for business competition. To preserve this privacy, this paper proposes a differentially private consensus ADMM (DP-C-ADMM) algorithm, which can offer a mixture solution of both realistically optimal generator outputs and obfuscated-but-feasible load flows of tie-lines. And \n<inline-formula> <tex-math>$\\epsilon -$ </tex-math></inline-formula>\ndifferential privacy holds for load flows of tie-lines across agents over iterations. Case study justifies the theoretical properties of this algorithm up to specified privacy parameters.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"15 6","pages":"6175-6178"},"PeriodicalIF":8.6000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decentralized Optimal Power Flow for Multi-Agent Active Distribution Networks: A Differentially Private Consensus ADMM Algorithm\",\"authors\":\"Chao Lei;Siqi Bu;Qifan Chen;Qianggang Wang;Qin Wang;Dipti Srinivasan\",\"doi\":\"10.1109/TSG.2024.3451793\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In multi-agent active distribution networks, the information exchanges in the ADMM algorithm for the decentralized distribution-level optimal power flow (D-OPF) may expose sensitive load flows of tie-lines across adjacent agents. This may be overheard by adversarial agents for business competition. To preserve this privacy, this paper proposes a differentially private consensus ADMM (DP-C-ADMM) algorithm, which can offer a mixture solution of both realistically optimal generator outputs and obfuscated-but-feasible load flows of tie-lines. And \\n<inline-formula> <tex-math>$\\\\epsilon -$ </tex-math></inline-formula>\\ndifferential privacy holds for load flows of tie-lines across agents over iterations. Case study justifies the theoretical properties of this algorithm up to specified privacy parameters.\",\"PeriodicalId\":13331,\"journal\":{\"name\":\"IEEE Transactions on Smart Grid\",\"volume\":\"15 6\",\"pages\":\"6175-6178\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Smart Grid\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10659236/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Smart Grid","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10659236/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Decentralized Optimal Power Flow for Multi-Agent Active Distribution Networks: A Differentially Private Consensus ADMM Algorithm
In multi-agent active distribution networks, the information exchanges in the ADMM algorithm for the decentralized distribution-level optimal power flow (D-OPF) may expose sensitive load flows of tie-lines across adjacent agents. This may be overheard by adversarial agents for business competition. To preserve this privacy, this paper proposes a differentially private consensus ADMM (DP-C-ADMM) algorithm, which can offer a mixture solution of both realistically optimal generator outputs and obfuscated-but-feasible load flows of tie-lines. And
$\epsilon -$
differential privacy holds for load flows of tie-lines across agents over iterations. Case study justifies the theoretical properties of this algorithm up to specified privacy parameters.
期刊介绍：
The IEEE Transactions on Smart Grid is a multidisciplinary journal that focuses on research and development in the field of smart grid technology. It covers various aspects of the smart grid, including energy networks, prosumers (consumers who also produce energy), electric transportation, distributed energy resources, and communications. The journal also addresses the integration of microgrids and active distribution networks with transmission systems. It publishes original research on smart grid theories and principles, including technologies and systems for demand response, Advance Metering Infrastructure, cyber-physical systems, multi-energy systems, transactive energy, data analytics, and electric vehicle integration. Additionally, the journal considers surveys of existing work on the smart grid that propose new perspectives on the history and future of intelligent and active grids.
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