文献类型：期刊文献

英文题名：A Privacy Preserving Distributed Optimization Algorithm for Economic Dispatch Over Time-Varying Directed Networks

作者：Mao, Shuai[1];Tang, Yang[1];Dong, Ziwei[1];Meng, Ke[2];Dong, Zhao Yang[2];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia

年份：2021

卷号：17

期号：3

起止页码：1689

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20205109657709);WOS:【SCI-EXPANDED(收录号:WOS:000597195500015)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0809302, in part by the National Natural Science Foundation of China under Grant 61988101, Grant 61751305, and Grant 61673176, in part by the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Privacy; Optimization; Communication networks; Generators; Convergence; Economics; Smart grids; Distributed optimization; economic dispatch; privacy preservation; smart grids

摘要：The economic dispatch problem (EDP) plays a fundamental and significant role in smart grids. Its purpose is to decide the output power of every generator in smart grids for achieving the minimal generation cost. With advantages in flexibility, robustness, and scalability, it is desirable to apply distributed optimization methods to solve EDPs. In most existing distributed optimization approaches, all generators explicitly exchange their states with neighbors to obtain the optimal solution, which may result in disclosing the privacy information of generators. This problem becomes worse if there are some adversaries aimed at inferring privacy information from the communication network for nefarious purposes. For privacy preservation, a privacy preserving distributed optimization algorithm over time-varying directed communication networks is proposed in this article by adding conditional noises to the exchanged states. It is proved that this proposed algorithm is able to solve the EDP. Moreover, the convergence rate and privacy analysis of the proposed algorithm are also shown in this article. An example is provided to confirm the effectiveness of this proposed algorithm.