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A survey of distributed optimization
Univ North Texas, Dept Elect Engn, Denton, TX 76203 USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Zhejiang, Peoples R China..
Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Hubei, Peoples R China.;Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Hubei, Peoples R China..
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2019 (English)In: Annual Reviews in Control, ISSN 1367-5788, E-ISSN 1872-9088, Vol. 47, p. 278-305Article, review/survey (Refereed) Published
Abstract [en]

In distributed optimization of multi-agent systems, agents cooperate to minimize a global function which is a sum of local objective functions. Motivated by applications including power systems, sensor networks, smart buildings, and smart manufacturing, various distributed optimization algorithms have been developed. In these algorithms, each agent performs local computation based on its own information and information received from its neighboring agents through the underlying communication network, so that the optimization problem can be solved in a distributed manner. This survey paper aims to offer a detailed overview of existing distributed optimization algorithms and their applications in power systems. More specifically, we first review discrete-time and continuous-time distributed optimization algorithms for undirected graphs. We then discuss how to extend these algorithms in various directions to handle more realistic scenarios. Finally, we focus on the application of distributed optimization in the optimal coordination of distributed energy resources. 

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 47, p. 278-305
Keywords [en]
Distributed optimization, Coordination of distributed energy resources
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-255519DOI: 10.1016/j.arcontrol.2019.05.006ISI: 000474680200022Scopus ID: 2-s2.0-85065858312OAI: oai:DiVA.org:kth-255519DiVA, id: diva2:1363027
Note

QC 20191022

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-22Bibliographically approved

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Yi, XinleiJohansson, Karl H.

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