A Privacy Preserving Distributed Optimization Algorithm for Economic Dispatch Over Time-Varying Directed Networks

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 di...

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Vydané v:	IEEE transactions on industrial informatics Ročník 17; číslo 3; s. 1689 - 1701
Hlavní autori:	Mao, Shuai, Tang, Yang, Dong, Ziwei, Meng, Ke, Dong, Zhao Yang, Qian, Feng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Communication networks Convergence Distributed optimization economic dispatch Economics Exchanging Generators Optimization Optimization algorithms Power dispatch Privacy privacy preservation Smart grid Smart grids
ISSN:	1551-3203, 1941-0050
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Abstract	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.
AbstractList	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.
Author	Meng, Ke Qian, Feng Mao, Shuai Tang, Yang Dong, Ziwei Dong, Zhao Yang
Author_xml	– sequence: 1 givenname: Shuai orcidid: 0000-0002-1960-3456 surname: Mao fullname: Mao, Shuai email: mshecust@163.com organization: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China – sequence: 2 givenname: Yang orcidid: 0000-0002-2750-8029 surname: Tang fullname: Tang, Yang email: tangtany@gmail.com organization: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China – sequence: 3 givenname: Ziwei surname: Dong fullname: Dong, Ziwei email: 19921267380@163.com organization: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China – sequence: 4 givenname: Ke orcidid: 0000-0002-1897-4917 surname: Meng fullname: Meng, Ke email: kemeng@ieee.org organization: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia – sequence: 5 givenname: Zhao Yang orcidid: 0000-0001-9659-0858 surname: Dong fullname: Dong, Zhao Yang email: zydong@ieee.org organization: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia – sequence: 6 givenname: Feng orcidid: 0000-0003-2781-332X surname: Qian fullname: Qian, Feng email: fqian@ecust.edu.cn organization: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
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SubjectTerms	Algorithms Communication networks Convergence Distributed optimization economic dispatch Economics Exchanging Generators Optimization Optimization algorithms Power dispatch Privacy privacy preservation Smart grid Smart grids
Title	A Privacy Preserving Distributed Optimization Algorithm for Economic Dispatch Over Time-Varying Directed Networks
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