文献类型：期刊文献

英文题名：A Finite-Time Distributed Optimization Algorithm for Economic Dispatch in Smart Grids

作者：Mao, Shuai[1];Dong, Ziwei[1];Schultz, Paul[2];Tang, Yang[1];Meng, Ke[3];Dong, Zhao Yang[3];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]Potsdam Inst Climate Impact Res, Complex Sci Dept, D-14412 Potsdam, Germany;[3]Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia

年份：2021

卷号：51

期号：4

起止页码：2068

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20211310146129);WOS:【SCI-EXPANDED(收录号:WOS:000631202400004)】；

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 61751305 and Grant 61673176, in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, and in part by the Deutsche Forschungsgemeinschaft under Grant KU 837/39-1/RA 516/13-1.

语种：英文

外文关键词：Convergence; Smart grids; Generators; Cost function; Consensus; distributed optimization algorithm; economic dispatch; finite time; smart grids

摘要：The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed optimization algorithm is proposed to solve EDP. It is only required that each device in the communication network has access to its own local generation cost function, designed virtual local demand and its neighbors' local optimization variables. The proposed finite-time algorithm can solve EDP, if the gain parameters in the algorithm satisfy some conditions under undirected and connected time-varying graphs. Moreover, the bounded or linear increasing assumption on the gradient and subgradient of objecive functions is relaxed in this algorithm. Examples under several cases are provided to verify the effectiveness of the proposed distributed optimization algorithm.