Multi-Area Dynamic Economic Dispatch Considering Water Consumption Minimization, Wind Generation, and Energy Storage System

Azizivahed, A; Karandeh, R; Cecchi, V; Naderi, E; Li, L; Zhang, J

Multi-Area Dynamic Economic Dispatch Considering Water Consumption Minimization, Wind Generation, and Energy Storage System

Azizivahed, A Karandeh, R Cecchi, V Naderi, E Li, L

Zhang, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020, 00, pp. 1-5
Issue Date:: 2020-05-07

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Field	Value	Language
dc.contributor.author	Azizivahed, A
dc.contributor.author	Karandeh, R
dc.contributor.author	Cecchi, V
dc.contributor.author	Naderi, E
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722
dc.date	2020-02-17
dc.date.accessioned	2021-05-14T05:36:12Z
dc.date.available	2021-05-14T05:36:12Z
dc.date.issued	2020-05-07
dc.identifier.citation	2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020, 00, pp. 1-5
dc.identifier.isbn	978-1-7281-3104-7
dc.identifier.issn	2167-9665
dc.identifier.issn	2472-8152
dc.identifier.uri	http://hdl.handle.net/10453/148907
dc.description.abstract	This paper introduces a novel, practical model for Multi-Area Dynamic Economic Dispatch (MADED) problem whose primary targets are to minimize both operational cost and water consumption. To make the proposed study more practical, several additional requirements that are inevitable in forthcoming power systems are taken into account including wind power penetration, pumped storage systems, and different interconnected areas. Such a complicated problem needs to be solved by a potent multi-objective optimization tool. In this regard, one of the relatively new optimization approaches, Whale Optimization Algorithm (WOA), is implemented to solve the proposed problem. The obtained optimal solutions on a 40-unit power network illustrate the effectiveness of the proposed approach and reveal a substantial improvement in reducing the operational cost, i.e., more than $30 million is reduced annually.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
dc.relation.ispartof	IEEE Power and Energy Society Innovative Smart Grid Technologies Conference
dc.relation.isbasedon	10.1109/isgt45199.2020.9087689
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Multi-Area Dynamic Economic Dispatch Considering Water Consumption Minimization, Wind Generation, and Energy Storage System
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Washington, DC, USA
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-14T05:36:11Z
pubs.finish-date	2020-02-20
pubs.publication-status	Published
pubs.start-date	2020-02-17
pubs.volume	00

Abstract:

This paper introduces a novel, practical model for Multi-Area Dynamic Economic Dispatch (MADED) problem whose primary targets are to minimize both operational cost and water consumption. To make the proposed study more practical, several additional requirements that are inevitable in forthcoming power systems are taken into account including wind power penetration, pumped storage systems, and different interconnected areas. Such a complicated problem needs to be solved by a potent multi-objective optimization tool. In this regard, one of the relatively new optimization approaches, Whale Optimization Algorithm (WOA), is implemented to solve the proposed problem. The obtained optimal solutions on a 40-unit power network illustrate the effectiveness of the proposed approach and reveal a substantial improvement in reducing the operational cost, i.e., more than $30 million is reduced annually.

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http://hdl.handle.net/10453/148907