An Efficient Modified HPSO-TVAC-Based Dynamic Economic Dispatch of Generating Units

Ghasemi, M; Akbari, E; Zand, M; Hadipour, M; Ghavidel, S; Li, L

An Efficient Modified HPSO-TVAC-Based Dynamic Economic Dispatch of Generating Units

Ghasemi, M Akbari, E Zand, M Hadipour, M Ghavidel, S Li, L

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Publisher:: Informa UK Limited
Publication Type:: Journal Article
Citation:: Electric Power Components and Systems, 2019, 47, (19-20), pp. 1826-1840
Issue Date:: 2019-12-14

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Field	Value	Language
dc.contributor.author	Ghasemi, M
dc.contributor.author	Akbari, E
dc.contributor.author	Zand, M
dc.contributor.author	Hadipour, M
dc.contributor.author	Ghavidel, S
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.date.accessioned	2020-05-22T00:47:30Z
dc.date.available	2020-05-22T00:47:30Z
dc.date.issued	2019-12-14
dc.identifier.citation	Electric Power Components and Systems, 2019, 47, (19-20), pp. 1826-1840
dc.identifier.issn	1532-5008
dc.identifier.issn	1532-5016
dc.identifier.uri	http://hdl.handle.net/10453/140879
dc.description.abstract	© 2020, © 2020 Taylor & Francis Group, LLC. This paper proposes a novel particle swarm optimization (PSO) algorithm with population reduction, which is called modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients (MNHPSO-JTVAC). The proposed method is used for solving well-known benchmark functions, as well as non-convex and non-smooth dynamic economic dispatch (DED) problems for a 24 h time interval in two different test systems. Operational constraints including the prohibited operating zones (POZs), the transmission losses, the ramp-rate limits and the valve-point effects are considered in solving the DED problem. The obtained numerical results show that the MNHPSO-JTVAC algorithm is very suitable and competitive compared to other algorithms and have the capacity to obtain better optimal solutions in solving the non-convex and non-smooth DED problems compared to the other variants of PSO and the state of the art optimization algorithms proposed in recent literature. The source codes of the HPSO-TVAC algorithms and supplementary data for this paper are publicly available at https://github.com/ebrahimakbary/MNHPSO-JTVAC.
dc.language	en
dc.publisher	Informa UK Limited
dc.relation.ispartof	Electric Power Components and Systems
dc.relation.isbasedon	10.1080/15325008.2020.1731876
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.title	An Efficient Modified HPSO-TVAC-Based Dynamic Economic Dispatch of Generating Units
dc.type	Journal Article
utslib.citation.volume	47
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-22T00:47:28Z
pubs.issue	19-20
pubs.publication-status	Published
pubs.volume	47
utslib.start-page	1826
utslib.citation.issue	19-20

Abstract:

© 2020, © 2020 Taylor & Francis Group, LLC. This paper proposes a novel particle swarm optimization (PSO) algorithm with population reduction, which is called modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients (MNHPSO-JTVAC). The proposed method is used for solving well-known benchmark functions, as well as non-convex and non-smooth dynamic economic dispatch (DED) problems for a 24 h time interval in two different test systems. Operational constraints including the prohibited operating zones (POZs), the transmission losses, the ramp-rate limits and the valve-point effects are considered in solving the DED problem. The obtained numerical results show that the MNHPSO-JTVAC algorithm is very suitable and competitive compared to other algorithms and have the capacity to obtain better optimal solutions in solving the non-convex and non-smooth DED problems compared to the other variants of PSO and the state of the art optimization algorithms proposed in recent literature. The source codes of the HPSO-TVAC algorithms and supplementary data for this paper are publicly available at https://github.com/ebrahimakbary/MNHPSO-JTVAC.

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http://hdl.handle.net/10453/140879