Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm

We propose a dual decomposition based algorithm that solves the AC optimal power flow (ACOPF) problem in the radial distribution systems and microgrids in a collaborative and distributed manner. The proposed algorithm adopts the sec-ond-order cone program (SOCP) relaxed branch flow ACOPF model. In t...

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Autores principales:	Zheyuan Cheng, Mo-Yuen Chow
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Distributed convex optimization distributed energy management system optimal power flow primal-dual decomposition Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
Acceso en línea:	https://doaj.org/article/25458a1893a04e6c97e985e3272902d0
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id	oai:doaj.org-article:25458a1893a04e6c97e985e3272902d0
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spelling	oai:doaj.org-article:25458a1893a04e6c97e985e3272902d02021-11-27T00:00:14ZCollaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm2196-542010.35833/MPCE.2020.000395https://doaj.org/article/25458a1893a04e6c97e985e3272902d02021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9356482/https://doaj.org/toc/2196-5420We propose a dual decomposition based algorithm that solves the AC optimal power flow (ACOPF) problem in the radial distribution systems and microgrids in a collaborative and distributed manner. The proposed algorithm adopts the sec-ond-order cone program (SOCP) relaxed branch flow ACOPF model. In the proposed algorithm, bus-level agents collaboratively solve the global ACOPF problem by iteratively sharing partial variables with its 1-hop neighbors as well as carrying out local scalar computations that are derived using augmented Lagrangian and primal-dual subgradient methods. We also propose two distributed computing platforms, i. e., high-perfor-mance computing (HPC) based platform and hardware-in-the-loop (HIL) testbed, to validate and evaluate the proposed algorithm. The computation and communication performances of the proposed algorithm are quantified and analyzed on typical IEEE test systems. Experimental results indicate that the proposed algorithm can be executed on a fully distributed computing structure and yields accurate ACOPF solution. Besides, the proposed algorithm has a low communication overhead.Zheyuan ChengMo-Yuen ChowIEEEarticleDistributed convex optimizationdistributed energy management systemoptimal power flowprimal-dual decompositionProduction of electric energy or power. Powerplants. Central stationsTK1001-1841Renewable energy sourcesTJ807-830ENJournal of Modern Power Systems and Clean Energy, Vol 9, Iss 6, Pp 1414-1423 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Distributed convex optimization distributed energy management system optimal power flow primal-dual decomposition Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
spellingShingle	Distributed convex optimization distributed energy management system optimal power flow primal-dual decomposition Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830 Zheyuan Cheng Mo-Yuen Chow Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm
description	We propose a dual decomposition based algorithm that solves the AC optimal power flow (ACOPF) problem in the radial distribution systems and microgrids in a collaborative and distributed manner. The proposed algorithm adopts the sec-ond-order cone program (SOCP) relaxed branch flow ACOPF model. In the proposed algorithm, bus-level agents collaboratively solve the global ACOPF problem by iteratively sharing partial variables with its 1-hop neighbors as well as carrying out local scalar computations that are derived using augmented Lagrangian and primal-dual subgradient methods. We also propose two distributed computing platforms, i. e., high-perfor-mance computing (HPC) based platform and hardware-in-the-loop (HIL) testbed, to validate and evaluate the proposed algorithm. The computation and communication performances of the proposed algorithm are quantified and analyzed on typical IEEE test systems. Experimental results indicate that the proposed algorithm can be executed on a fully distributed computing structure and yields accurate ACOPF solution. Besides, the proposed algorithm has a low communication overhead.
format	article
author	Zheyuan Cheng Mo-Yuen Chow
author_facet	Zheyuan Cheng Mo-Yuen Chow
author_sort	Zheyuan Cheng
title	Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm
title_short	Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm
title_full	Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm
title_fullStr	Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm
title_full_unstemmed	Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm
title_sort	collaborative distributed ac optimal power flow: a dual decomposition based algorithm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/25458a1893a04e6c97e985e3272902d0
work_keys_str_mv	AT zheyuancheng collaborativedistributedacoptimalpowerflowadualdecompositionbasedalgorithm AT moyuenchow collaborativedistributedacoptimalpowerflowadualdecompositionbasedalgorithm
_version_	1718409218280652800

Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm

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