Distributed inexact dual consensus ADMM for network resource allocation

Long, Jian; Hu, Jiangping; Wang, Jun; Shi, Kaibo

doi:10.1002/oca.2538

Cited by 13 publications

(4 citation statements)

References 38 publications

(93 reference statements)

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“…In this paper, ADMM [30,31] is used to solve the built optimization model. As for (19), the quadratically augmented Lagrangian function can be written as (20).…”

Section: Distributed Algorithmmentioning

confidence: 99%

ADMM‐Based Distributed Algorithm for Energy Management in Multi‐Microgrid System

Lou,

Fujimura

2023

IEEJ Transactions Elec Engng

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This article focuses on Energy Management Problem (EMP) in Muti‐microgrid systems. Each microgrid (MG) has four basic equipments including Renewable Generator (RG), Fuel Generator (FG), Storage Device (SD) and Smart Load (SL). In consideration of equipment capacity, confidence interval for forecasts of RG output, supply–demand balance and other factors, an optimization model with the objective of minimizing operation cost was established. Through Lagrange dual problem and variable substitution, we transform the centralized problem into an equivalent distributed form. Then, a completely distributed algorithm based on Alternating Direction Method of Multipliers (ADMM) and Average Consensus (AC) is proposed to attain a global optimal schedule plan. The algorithm overcomes the defect that each microgrid needs to know the total must‐run load in advance. At the same time, the electricity clearing price is related to the objective function through Lagrange dual variables, which can be obtained while the optimal plan is determined. Finally, the simulation verifies the convergence of the algorithm, and the calculated optimal cost is the same as that of the centralized method, ensuring its effectiveness. Besides, the algorithm also has good performance in plug‐and‐play scenarios. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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“…In this paper, ADMM [30,31] is used to solve the built optimization model. As for (19), the quadratically augmented Lagrangian function can be written as (20).…”

Section: Distributed Algorithmmentioning

confidence: 99%

ADMM‐Based Distributed Algorithm for Energy Management in Multi‐Microgrid System

Lou,

Fujimura

2023

IEEJ Transactions Elec Engng

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“…In addition, the gradient method is very sensitive to the selection of the initial point and the step size, where improper parameters may lead to divergence. Recently, the ADMM [27,36] has received much attention because of its broad applications in regularized estimation [37], resource allocation [38] and distributed learning [39]. The ADMM has several important theoretical properties as follows.…”

Section: B Algorithm For Sparse Optimizationmentioning

confidence: 99%

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Guo

et al. 2021

IEEE Access

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The assembly accuracy of the extendible support structure is of importance for the imaging capability of synthetic aperture radar antennas. In general, due to manufacturing imperfections and installation variations, its assembly accuracy will be inevitably degraded. Therefore, controlling the assembly deviation is highly concerned in practice. To meet the accuracy requirement and make "the control" more efficient, this study proposes a novel method to quantitatively conduct dimensional adjustment of links for extendible support structures of synthetic aperture radar antennas. Since the extendible support structure is generally over-constrained in the deployed configuration, the relationship between the assembly deviation and the variation sources is first derived by means of equivalent transformation. Based on the mathematical expression of assembly deviation, an inequality constrained sparse optimization model for quantitatively resizing links is formulated. Then, an efficient algorithm integrating the alternating direction method of multipliers and binary search is developed to solve the above optimization model, thereby acquiring the optimal combination of link adjustment. Finally, numerical case studies are carried out to demonstrate the effectiveness of the proposed method in Matlab, which show that it can not only achieve satisfactory performance in prediction but also significantly improve the assembly efficiency.

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“…Due to the wide background and applications in resource allocation, 17‐19 big data analysis, 20 and distributed learning, 21 distributed optimization of the sum of convex objective functions

\sum_{i = 1}^{m} f_{i} (x)

, where f i ( x ) is known only to a particular agent i , has attracted a lot of research attention. Various subgradient‐based strategies have been proposed to solve the distributed problems 22 .…”

Section: Introductionmentioning

confidence: 99%

Randomized optimal consensus of multiagent systems based on a novel intermittent projected subgradient algorithm

Shi

Zhou

2020

Optim Control Appl Methods

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Summary In this article, a novel intermittent projected subgradient algorithm is presented to solve the randomized optimal consensus problem for heterogeneous multiagent systems with time‐varying communication topologies. The multiagent systems achieve the consensus meanwhile minimizing the global objective function ∑i=1mfi(x) via the proposed algorithm, where fi(x) is the convex objective function of agent i itself. Due to the common Bernoulli distribution adopted in the existing random optimization algorithm without considering the different computing capability of each agent. An individual projection probability is assigned for each agent based on computing capabilities so that either making projection or taking average is chosen according to the above probability which can effectively avoid overload for some agents with lower computing capabilities and improve the reliability of the overall systems. A new sufficient step‐size condition is given to ensure all agents converge to the optimal solution with probability one. Finally, a numerical example is also given to validate the proposed method.

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Distributed inexact dual consensus ADMM for network resource allocation

Cited by 13 publications

References 38 publications

ADMM‐Based Distributed Algorithm for Energy Management in Multi‐Microgrid System

ADMM‐Based Distributed Algorithm for Energy Management in Multi‐Microgrid System

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Randomized optimal consensus of multiagent systems based on a novel intermittent projected subgradient algorithm

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