A Distributed Algorithm for Economic Dispatch Over Time-Varying Directed Networks With Delays

Yang, Tao; Lu, Jie; Wu, Di; Wu, Junfeng; Shi, Guodong; Meng, Ziyang; Johansson, Karl Henrik

doi:10.1109/tie.2016.2617832

Cited by 224 publications

(159 citation statements)

References 30 publications

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“…An edge

false(i, j false) \in scriptE

between nodes i and j means that agent i and agent j can share information with each other. Suppose the following assumption is fulfilled as in many publications …”

Section: Problem Formulationmentioning

confidence: 99%

“…In a typical setting of this problem, each agent is assigned with a local cost function and the control objective is to propose distributed controls that guarantee a consensus on the optimal solution of the sum of all local cost functions. Many effective algorithms have been proposed to achieve this goal in different situations …”

Section: Introductionmentioning

confidence: 99%

“…Many effective algorithms have been proposed to achieve this goal in different situations. [4][5][6][7][8][9] Here, we follow this technical line but consider high-order continuous-time nonlinear agents with unknown dynamics. While most of the existing works were only devoted to single-integrator agents, there are many distributed optimization tasks implemented by or depending on physical plants of continuous dynamics in practice, eg, source seeking in multirobot systems, 10 attitude formation control of rigid bodies, 11 and optimal power dispatch over power networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed optimization for a class of high‐order nonlinear multiagent systems with unknown dynamics

Tang

2018

Intl J Robust & Nonlinear

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Summary In this paper, we study a distributed optimization problem for a class of high‐order multiagent systems with unknown dynamics. In comparison with existing results for integrators or linear agents, we need to overcome the difficulties brought by the unknown nonlinearities and the optimization requirement. For this purpose, we employ an embedded control‐based design and first convert this problem into an output stabilization problem. Then, two kinds of adaptive controllers are given for these agents to drive their outputs to the global optimal solution under some mild conditions. Finally, we show that the estimated parameter vector converges to the true parameter vector under some well‐known persistence of excitation condition. The efficacy of these algorithms was verified by a simulation example.

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“…An edge

false(i, j false) \in scriptE

between nodes i and j means that agent i and agent j can share information with each other. Suppose the following assumption is fulfilled as in many publications …”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed optimization for a class of high‐order nonlinear multiagent systems with unknown dynamics

Tang

2018

Intl J Robust & Nonlinear

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show abstract

Section: Introductionmentioning

confidence: 99%

“…Distributed optimization, which cooperatively achieves optimal decisions for the local manipulation with private data and the diffusion of local information through a multiagent network, was extensively studied recently, such as the consensus subgradient methods [10][11][12] and other algorithms in other works. [13][14][15] In the work of Yi et al, 16 decentralized resource allocation problems in the context of economics were examined. According to the global network resource constraint and the local allocation feasibility constraints problem, the authors proposed two classes of continuous-time algorithms in an initialization-free and scalable manner.…”

Section: Introductionmentioning

confidence: 99%

Distributed inexact dual consensus ADMM for network resource allocation

Long

Wang

et al. 2019

Optim Control Appl Methods

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Summary This paper investigates two novel distributed algorithms based on alternating direction method of multipliers (ADMM) for network resource allocation of N agents. The main objective is to derive an optimal allocation that minimizes a global objective expressed as a sum of locally known separable convex objective functions. Based on a communication matrix, the dual resource allocation problem is changed into a consensus optimization problem, in which each agent broadcasts the outcome of its local processing to all his neighbors. In this paper, we first propose a new distributed dual consensus ADMM (DC‐ADMM) algorithm to address this consensus problem. Moreover, by applying an inexact step for each ADMM update, a distributed inexact DC‐ADMM (IDC‐ADMM) is also developed, which enables agents to perform cheap computation at each iteration. Finally, numerical simulations are delivered to illustrate and validate the proposed algorithm.

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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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A Distributed Algorithm for Economic Dispatch Over Time-Varying Directed Networks With Delays

Cited by 224 publications

References 30 publications

Distributed optimization for a class of high‐order nonlinear multiagent systems with unknown dynamics

Distributed optimization for a class of high‐order nonlinear multiagent systems with unknown dynamics

Distributed inexact dual consensus ADMM for network resource allocation

An exponentially convergent distributed algorithm for resource allocation problem

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