Distributed Alternating Direction Method of Multipliers

Wei, Ermin; Ozdaglar, Asuman

doi:10.1109/cdc.2012.6425904

Cited by 325 publications

(294 citation statements)

References 14 publications

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“…This is necessary because without such a penalty, each bus is prone to minimize his own generation cost selfishly based on a set of biased estimation. The decision variables of the problem (14) are only related to the bus i and its neighbors. So it is a local nonlinear optimization problem with a small scale.…”

Section: ) Consensus Algorithm Of the Estimatesmentioning

confidence: 99%

“…In [10], an unconstrained nonlinear optimization problem is solved by multiple agents cooperatively using sub-gradient method to minimize the individual objective function and also sharing information among agents in the neighborhood. For constrained optimization problem, distributed algorithms have been proposed based on penalty function method [7], alternative direction method of multiplier (ADMM) [1], [14].…”

Section: Introductionmentioning

confidence: 99%

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Consensus-based distributed optimal power flow algorithm

Liu

Benosman

Raghunathan

2015

2015 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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Optimal power flow (OPF) is a well known challenging optimization problem for power systems engineering. There have been a myriad of works dedicated to solving OPF problems in a centralized way, i.e. using a centralized solver, which necessitates for a central control center to receive and transmit a large amount of data. Recently, few attempts have been done to solve OPF problems in a distributed way, which means that the computations are distributed over the whole network, reducing the need for communications (with a central control center) and increasing the robustness of the control to loss of communications and to unexpected faults in the network nodes. Furthermore, a decentralized solution to the OPF problem has the advantage to be flexible to the network topology and size, since the computations are distributed over the network. In this paper we propose some preliminary results on a distributed OPF algorithm, to solve the original OPF problem, i.e. without any convexification steps. We propose a consensus-based distributed OPF algorithm, which uses consensus algorithm to estimate the optimization variables between neighboring nodes in a given network. We test the performance of this algorithm on the IEEE 14-node test-case. IEEE PES Innovative Smart Grid Technologies Conference (ISGT)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-Optimal power flow (OPF) is a well known challenging optimization problem for power systems engineering. There have been a myriad of works dedicated to solving OPF problems in a centralized way, i.e. using a centralized solver, which necessitates for a central control center to receive and transmit a large amount of data. Recently, few attempts have been done to solve OPF problems in a distributed way, which means that the computations are distributed over the whole network, reducing the need for communications (with a central control center) and increasing the robustness of the control to loss of communications and to unexpected faults in the network nodes. Furthermore, a decentralized solution to the OPF problem has the advantage to be flexible to the network topology and size, since the computations are distributed over the network. In this paper we propose some preliminary results on a distributed OPF algorithm, to solve the original OPF problem, i.e. without any convexification steps. We propose a consensus-based distributed OPF algorit...

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Section: ) Consensus Algorithm Of the Estimatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consensus-based distributed optimal power flow algorithm

Liu

Benosman

Raghunathan

2015

2015 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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“…Unlike existing distributed implementations, the proposed algorithm does not require multiple updates to compute the optimal BF response. Additionally, the BF computation can be performed via a range of existing distributed tools [2,13,16,21,17] for both cyclic and acyclic networks. This flexibility makes it possible to use the proposed algorithm for optimal beamforming without the impractical restriction of enforcing tree-shaped networks, as required in [1,11].…”

Section: Introductionmentioning

confidence: 99%

A distributed algorithm for robust LCMV beamforming

Sherson

Kleijn

Heusdens

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks, allows the optimal beamformer output to be computed at each node without the need for sharing raw data within the network. By exploiting the low rank structure of estimated covariance matrices in time-varying noise fields, the algorithm can also provide a reduction in the total amount of data transmitted during computation when compared to centralised solutions. This is particularly true when multiple microphones are used per node. We also compare the performance of DMV with state of the art distributed beamformers and demonstrate that it achieves greater improvements in SNR in dynamic noise fields with similar transmission costs.

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“…Let A i be the set of devices in household i. Letting e i,a , a ∈ A i , be the cost function of each of devices, the following objective function can be used: By introducing duplicated variables again such that x i,a = y i,a and y i,a = z i,a , it is not difficult to show that a similar profilebased distributed optimization can be derived (see also [14], for ADMM with multiple agents). Our future work includes the detailed analysis of such hierarchical architectures with multiple devices and/or multiple aggregators.…”

Section: B Multiple Devicesmentioning

confidence: 99%

Distributed mode scheduling for coordinated power balancing

Kawashima

Kato

Matsuyama

2013

2013 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-This paper proposes a distributed scheduling algorithm of demands of multiple households/consumers in order to achieve a power balancing in total. Assume that an autonomous energy management system is installed in each household and that those households are capable of communicating with an aggregator. Then, it becomes possible to negotiate via the autonomous energy management systems to find an agreement point that takes both the users' demands and the aggregator's objective into account. The key aspect of the algorithm is that it enables us to encapsulate the particular control and objective in each household, and realizes the negotiation based on power profiles. We also show the proposed framework can be smoothly integrated with a probabilistic generative model of power profiles.

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Distributed Alternating Direction Method of Multipliers

Cited by 325 publications

References 14 publications

Consensus-based distributed optimal power flow algorithm

Consensus-based distributed optimal power flow algorithm

A distributed algorithm for robust LCMV beamforming

Distributed mode scheduling for coordinated power balancing

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