Distributed optimization for economic power dispatch with event‐triggered communication

Shi, Xiasheng; Zheng, Ronghao; Lin, Zhiyun; Yan, Gangfeng

doi:10.1002/asjc.2140

Cited by 29 publications

(27 citation statements)

References 36 publications

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“…In general, DED aims at designing a distributed algorithm for each generator to dispatch the active power based on the locally available information to cooperatively meet electricity demand at a minimized cost. Many distributed approaches have been developed to deal with the DED problems (DEDPs) under various scenarios, such as communication delay (Yang T et al, 2017), asynchronous setting (Bragin and Luh, 2017), switching communication graph (Yang ZQ et al, 2017), transmission losses (Wang R et al, 2019b), and eventtriggered communication (Shi et al, 2020). In this study, we present a state-of-the-art review on some theoretical advances toward DED in SGs.…”

Section: Introductionmentioning

confidence: 99%

Recent progress on the study of distributed economic dispatch in smart grid: an overview

Wen

Liu

2021

Front Inform Technol Electron Eng

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Designing an efficient distributed economic dispatch (DED) strategy for the smart grid (SG) in the presence of multiple generators plays a paramount role in obtaining various benefits of a new generation power system, such as easy implementation, low maintenance cost, high energy efficiency, and strong robustness against uncertainties. It has drawn a lot of interest from a wide variety of scientific disciplines, including power engineering, control theory, and applied mathematics. We present a state-of-the-art review of some theoretical advances toward DED in the SG, with a focus on the literature published since 2015. We systematically review the recent results on this topic and subsequently categorize them into distributed discrete-and continuous-time economic dispatches of the SG in the presence of multiple generators. After reviewing the literature, we briefly present some future research directions in DED for the SG, including the distributed security economic dispatch of the SG, distributed fast economic dispatch in the SG with practical constraints, efficient initialization-free DED in the SG, DED in the SG in the presence of smart energy storage batteries and flexible loads, and DED in the SG with artificial intelligence technologies.

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Section: Introductionmentioning

confidence: 99%

Recent progress on the study of distributed economic dispatch in smart grid: an overview

Wen

Liu

2021

Front Inform Technol Electron Eng

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“…Distributed convex optimization has received a great deal of interest in recent years due to its widespread practical applications in many fields [1–8]. Compared with centralized optimization, distributed optimization has an essential difference in that the full knowledge of the overall problem structure need not to be known in advance.…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive online learning for convex optimization with weight decay

Shen

Fang

et al. 2020

Asian Journal of Control

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This paper investigates an adaptive gradient‐based online convex optimization problem over decentralized networks. The nodes of a network aim to track the minimizer of a global time‐varying convex function, and the communication pattern among nodes is captured as a connected undirected graph. To tackle such optimization problems in a collaborative and distributed manner, a weight decay distributed adaptive online gradient algorithm, called WDDAOG, is firstly proposed, which incorporates distributed optimization methods with adaptive strategies. Then, our theoretical analysis clearly illustrates the difference between weight decay and L2 regularization for distributed adaptive gradient algorithms. The dynamic regret bound for the proposed algorithm is further analyzed. It is shown that the dynamic regret bound for convex functions grows with order of scriptOfalse(nfalse(1+logTfalse)+nTfalse), where T and n represent the time horizon and the number of nodes associated with the network, respectively. Numerical experiments demonstrate that WDDAOG works well in practice and compares favorably to existing distributed online optimization schemes.

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“…It is known that the most current works about the distributed resource allocation problem are based on discrete‐time multi‐agent systems. However, with the development of cyber‐physical systems and the well‐developed continuous‐time control techniques, the continuous‐time distributed optimization algorithms have attracted much attention recently [20–27]. Moreover, the continuous‐time multi‐agent systems correspond to the steepest descent direction of the local cost function.…”

Section: Introductionmentioning

confidence: 99%

An exponentially convergent distributed algorithm for resource allocation problem

Shi

Zheng

Lin

et al. 2020

Asian Journal of Control

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In this paper, the distributed resource allocation problem over undirected networks, considering box constraints and demand–supply constraints, is studied. Furthermore, each agent has one private cost function that is only known by itself. The aim is to minimize the sum of all local cost functions in a distributed manner while meeting the constraints. To solve the dispatch problem, a continuous‐time distributed algorithm with fixed step‐size is proposed. After that, a modified distributed algorithm of discrete‐time multi‐agent system is provided. Moreover, the proposed algorithms obtain the global optimal solution with an exponential convergence speed by the conventional Lyapunov methods. Finally, several simulations of IEEE‐14 bus are presented to validate and illustrate the proposed designed algorithm.

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Distributed optimization for economic power dispatch with event‐triggered communication

Cited by 29 publications

References 36 publications

Recent progress on the study of distributed economic dispatch in smart grid: an overview

Recent progress on the study of distributed economic dispatch in smart grid: an overview

Distributed adaptive online learning for convex optimization with weight decay

An exponentially convergent distributed algorithm for resource allocation problem

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