Hybrid power plant bidding strategy including a commercial compressed air energy storage aggregator and a wind power producer

Ghavidel, S; Ghadi, MJ; Azizivahed, A; Barani, M; Aghaei, J; Li, L; Zhang, J

Hybrid power plant bidding strategy including a commercial compressed air energy storage aggregator and a wind power producer

Ghavidel, S Ghadi, MJ

Azizivahed, A Barani, M Aghaei, J Li, L

Zhang, J

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Publication Type:: Conference Proceeding
Citation:: 2017 Australasian Universities Power Engineering Conference, AUPEC 2017, 2018, 2017-November pp. 1 - 6
Issue Date:: 2018-02-05

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Field	Value	Language
dc.contributor.author	Ghavidel, S	en_US
dc.contributor.author	Ghadi, MJ https://orcid.org/0000-0002-5564-5055	en_US
dc.contributor.author	Azizivahed, A	en_US
dc.contributor.author	Barani, M	en_US
dc.contributor.author	Aghaei, J	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722	en_US
dc.date.available	2020-05-25T19:23:28Z
dc.date.issued	2018-02-05	en_US
dc.identifier.citation	2017 Australasian Universities Power Engineering Conference, AUPEC 2017, 2018, 2017-November pp. 1 - 6	en_US
dc.identifier.isbn	9781538626474	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127279
dc.description.abstract	© 2017 IEEE. In this paper, a commercial compressed air energy storage (CAES) aggregator equipped with a simple cycle mode operation having the ability to work like a gas turbine is coordinated with a wind power aggregator (WPA) as a hybrid power plant to participate in electricity markets. In the proposed approach, the WPA uses the CAES to tackle its stochastic input and uncertainties related to different electricity market prices, and CAES can also use WPA to manage its charging/discharging and simple cycle modes more economically. A three-stage stochastic decision-making method is used to model the mentioned optimization problem which considers three electricity markets including day-ahead, intraday and balancing markets. The problem is formulated as a mixed integer linear programming which can be solved with available commercial solvers. Also, conditional value-at-risk is added to the problem to control the financial risk of the problem and offer different operation strategies for different financials risk levels. The proposed method can provide both bidding quantity and bidding curves to be submitted to the electricity markets which is tested on a realistic case study based on a wind farm and electricity market located in Spain. The results confirm that the proposed method can provide extra profit in joint operation, have more flexibility and reduce the financial risks.	en_US
dc.relation.ispartof	2017 Australasian Universities Power Engineering Conference, AUPEC 2017	en_US
dc.relation.isbasedon	10.1109/AUPEC.2017.8282398	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Hybrid power plant bidding strategy including a commercial compressed air energy storage aggregator and a wind power producer	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-November	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2017-November	en_US

Abstract:

© 2017 IEEE. In this paper, a commercial compressed air energy storage (CAES) aggregator equipped with a simple cycle mode operation having the ability to work like a gas turbine is coordinated with a wind power aggregator (WPA) as a hybrid power plant to participate in electricity markets. In the proposed approach, the WPA uses the CAES to tackle its stochastic input and uncertainties related to different electricity market prices, and CAES can also use WPA to manage its charging/discharging and simple cycle modes more economically. A three-stage stochastic decision-making method is used to model the mentioned optimization problem which considers three electricity markets including day-ahead, intraday and balancing markets. The problem is formulated as a mixed integer linear programming which can be solved with available commercial solvers. Also, conditional value-at-risk is added to the problem to control the financial risk of the problem and offer different operation strategies for different financials risk levels. The proposed method can provide both bidding quantity and bidding curves to be submitted to the electricity markets which is tested on a realistic case study based on a wind farm and electricity market located in Spain. The results confirm that the proposed method can provide extra profit in joint operation, have more flexibility and reduce the financial risks.

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http://hdl.handle.net/10453/127279