Distributed optimal power flow.

<h4>Objective</h4>The objectives of this paper are to 1) construct a new network model compatible with distributed computation, 2) construct the full optimal power flow (OPF) in a distributed fashion so that an effective, non-inferior solution can be found, and 3) develop a scalable algo...

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Autor principal:	HyungSeon Oh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/163b4e868dd642cb8cf45f765b212383
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spelling	oai:doaj.org-article:163b4e868dd642cb8cf45f765b2123832021-12-02T20:05:20ZDistributed optimal power flow.1932-620310.1371/journal.pone.0251948https://doaj.org/article/163b4e868dd642cb8cf45f765b2123832021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251948https://doaj.org/toc/1932-6203<h4>Objective</h4>The objectives of this paper are to 1) construct a new network model compatible with distributed computation, 2) construct the full optimal power flow (OPF) in a distributed fashion so that an effective, non-inferior solution can be found, and 3) develop a scalable algorithm that guarantees the convergence to a local minimum.<h4>Existing challenges</h4>Due to the nonconvexity of the problem, the search for a solution to OPF problems is not scalable, which makes the OPF highly limited for the system operation of large-scale real-world power grids-"the curse of dimensionality". The recent attempts at distributed computation aim for a scalable and efficient algorithm by reducing the computational cost per iteration in exchange of increased communication costs.<h4>Motivation</h4>A new network model allows for efficient computation without increasing communication costs. With the network model, recent advancements in distributed computation make it possible to develop an efficient and scalable algorithm suitable for large-scale OPF optimizations.<h4>Methods</h4>We propose a new network model in which all nodes are directly connected to the center node to keep the communication costs manageable. Based on the network model, we suggest a nodal distributed algorithm and direct communication to all nodes through the center node. We demonstrate that the suggested algorithm converges to a local minimum rather than a point, satisfying the first optimality condition.<h4>Results</h4>The proposed algorithm identifies solutions to OPF problems in various IEEE model systems. The solutions are identical to those using a centrally optimized and heuristic approach. The computation time at each node does not depend on the system size, and Niter does not increase significantly with the system size.<h4>Conclusion</h4>Our proposed network model is a star network for maintaining the shortest node-to-node distances to allow a linear information exchange. The proposed algorithm guarantees the convergence to a local minimum rather than a maximum or a saddle point, and it maintains computational efficiency for a large-scale OPF, scalable algorithm.HyungSeon OhPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0251948 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q HyungSeon Oh Distributed optimal power flow.
description	<h4>Objective</h4>The objectives of this paper are to 1) construct a new network model compatible with distributed computation, 2) construct the full optimal power flow (OPF) in a distributed fashion so that an effective, non-inferior solution can be found, and 3) develop a scalable algorithm that guarantees the convergence to a local minimum.<h4>Existing challenges</h4>Due to the nonconvexity of the problem, the search for a solution to OPF problems is not scalable, which makes the OPF highly limited for the system operation of large-scale real-world power grids-"the curse of dimensionality". The recent attempts at distributed computation aim for a scalable and efficient algorithm by reducing the computational cost per iteration in exchange of increased communication costs.<h4>Motivation</h4>A new network model allows for efficient computation without increasing communication costs. With the network model, recent advancements in distributed computation make it possible to develop an efficient and scalable algorithm suitable for large-scale OPF optimizations.<h4>Methods</h4>We propose a new network model in which all nodes are directly connected to the center node to keep the communication costs manageable. Based on the network model, we suggest a nodal distributed algorithm and direct communication to all nodes through the center node. We demonstrate that the suggested algorithm converges to a local minimum rather than a point, satisfying the first optimality condition.<h4>Results</h4>The proposed algorithm identifies solutions to OPF problems in various IEEE model systems. The solutions are identical to those using a centrally optimized and heuristic approach. The computation time at each node does not depend on the system size, and Niter does not increase significantly with the system size.<h4>Conclusion</h4>Our proposed network model is a star network for maintaining the shortest node-to-node distances to allow a linear information exchange. The proposed algorithm guarantees the convergence to a local minimum rather than a maximum or a saddle point, and it maintains computational efficiency for a large-scale OPF, scalable algorithm.
format	article
author	HyungSeon Oh
author_facet	HyungSeon Oh
author_sort	HyungSeon Oh
title	Distributed optimal power flow.
title_short	Distributed optimal power flow.
title_full	Distributed optimal power flow.
title_fullStr	Distributed optimal power flow.
title_full_unstemmed	Distributed optimal power flow.
title_sort	distributed optimal power flow.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/163b4e868dd642cb8cf45f765b212383
work_keys_str_mv	AT hyungseonoh distributedoptimalpowerflow
_version_	1718375467905449984

Distributed optimal power flow.

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