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Title: A Survey of Distributed Optimization

Abstract

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.

Authors:
 [1];  [2];  [3];  [4]; ORCiD logo [5];  [6];  [7];  [5];  [8];  [2]
  1. University of North Texas
  2. Kungliga Tekniska Hogskolan
  3. Zhejiang University
  4. Huazhong University of Science and Technology
  5. BATTELLE (PACIFIC NW LAB)
  6. Tsinghua University
  7. Chinese Academy of Science
  8. Other
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1532653
Report Number(s):
PNNL-SA-139833
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Annual Reviews in Control
Additional Journal Information:
Journal Volume: 47
Country of Publication:
United States
Language:
English

Citation Formats

Yang, Tao, Yi, Xinlei, Wu, Junfeng, Yuan, Ye, Wu, Di, Meng, Ziyang, Hong, Yiguang, Wang, Hong, Lin, Zongli, and Johansson, Karl. A Survey of Distributed Optimization. United States: N. p., 2019. Web. doi:10.1016/j.arcontrol.2019.05.006.
Yang, Tao, Yi, Xinlei, Wu, Junfeng, Yuan, Ye, Wu, Di, Meng, Ziyang, Hong, Yiguang, Wang, Hong, Lin, Zongli, & Johansson, Karl. A Survey of Distributed Optimization. United States. doi:10.1016/j.arcontrol.2019.05.006.
Yang, Tao, Yi, Xinlei, Wu, Junfeng, Yuan, Ye, Wu, Di, Meng, Ziyang, Hong, Yiguang, Wang, Hong, Lin, Zongli, and Johansson, Karl. Mon . "A Survey of Distributed Optimization". United States. doi:10.1016/j.arcontrol.2019.05.006.
@article{osti_1532653,
title = {A Survey of Distributed Optimization},
author = {Yang, Tao and Yi, Xinlei and Wu, Junfeng and Yuan, Ye and Wu, Di and Meng, Ziyang and Hong, Yiguang and Wang, Hong and Lin, Zongli and Johansson, Karl},
abstractNote = {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.},
doi = {10.1016/j.arcontrol.2019.05.006},
journal = {Annual Reviews in Control},
number = ,
volume = 47,
place = {United States},
year = {2019},
month = {6}
}