Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management

Ghavidel, S; Rajabi, A; Ghadi, MJ; Azizivahed, A; Li, L; Zhang, J

Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management

Ghavidel, S Rajabi, A

Ghadi, MJ Azizivahed, A Li, L

Zhang, J

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Publisher:: Wiley Open Access
Publication Type:: Journal Article
Citation:: IET Energy Systems Integration, 2021, 3, (2), pp. 130-141
Issue Date:: 2021-03-26

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Field	Value	Language
dc.contributor.author	Ghavidel, S
dc.contributor.author	Rajabi, A https://orcid.org/0000-0002-3203-9861
dc.contributor.author	Ghadi, MJ
dc.contributor.author	Azizivahed, A
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.accessioned	2022-03-14T05:10:29Z
dc.date.available	2022-03-14T05:10:29Z
dc.date.issued	2021-03-26
dc.identifier.citation	IET Energy Systems Integration, 2021, 3, (2), pp. 130-141
dc.identifier.issn	2516-8401
dc.identifier.issn	2516-8401
dc.identifier.uri	http://hdl.handle.net/10453/155203
dc.description.abstract	This article models a hybrid power plant (HPP), including a compressed air energy storage (CAES) aggregator with a wind power aggregator (WPA) considering network constraints. Three objective functions are considered including electricity market profit maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control curtailment wind power levels are also added to the wind power generator models. To optimize all the mentioned objective functions, a multi-objective Pareto front solution strategy is used. Finally, a fuzzy method is used to find the best compromise solution. The proposed approach is tested on a realistic case study based on an electricity market and wind farm located in Spain, and IEEE 57-bus test system is used to evaluate the network constraint effects on the HPP scheduling for different objective functions.
dc.language	en
dc.publisher	Wiley Open Access
dc.relation.ispartof	IET Energy Systems Integration
dc.relation.isbasedon	10.1049/esi2.12008
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management
dc.type	Journal Article
utslib.citation.volume	3
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	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-14T05:10:27Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	3
utslib.citation.issue	2

Abstract:

This article models a hybrid power plant (HPP), including a compressed air energy storage (CAES) aggregator with a wind power aggregator (WPA) considering network constraints. Three objective functions are considered including electricity market profit maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control curtailment wind power levels are also added to the wind power generator models. To optimize all the mentioned objective functions, a multi-objective Pareto front solution strategy is used. Finally, a fuzzy method is used to find the best compromise solution. The proposed approach is tested on a realistic case study based on an electricity market and wind farm located in Spain, and IEEE 57-bus test system is used to evaluate the network constraint effects on the HPP scheduling for different objective functions.

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http://hdl.handle.net/10453/155203