Competitive strategic bidding optimization in electricity markets using bilevel programming and swarm technique

Zhang, G; Gao, Y; Lu, J

Competitive strategic bidding optimization in electricity markets using bilevel programming and swarm technique

Zhang, G

Gao, Y Lu, J

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Electronics, 2011, 58 (6), pp. 2138 - 2146
Issue Date:: 2011-06-01

Closed Access

	Filename	Description	Size
	2010004670OK.pdf		341.6 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Gao, Y	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2011-06-01	en_US
dc.identifier.citation	IEEE Transactions on Industrial Electronics, 2011, 58 (6), pp. 2138 - 2146	en_US
dc.identifier.issn	0278-0046	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15363
dc.description.abstract	Competitive strategic bidding optimization is now a key issue in electricity generator markets. Digital ecosystems provide a powerful technological foundation and support for the implementation of the optimization. This paper presents a new strategic bidding optimization technique which applies bilevel programming and swarm intelligence. In this paper, we first propose a general multileader-one-follower nonlinear bilevel (MLNB) optimization concept and related definitions based on the generalized Nash equilibrium. By analyzing the strategic bidding behavior of generating companies, we create a specific MLNB decision model for day-ahead electricity markets. The MLNB decision model allows each generating company to choose its biddings to maximize its individual profit, and a market operator can find its minimized purchase electricity fare, which is determined by the output power of each unit and the uniform marginal prices. We then develop a particle-swarm-optimization-based algorithm to solve the problem defined in the MLNB decision model. The experiment results on a strategic bidding problem for a day-ahead electricity market have demonstrated the validity of the proposed decision model and algorithm. © 2010 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0557154
dc.relation.ispartof	IEEE Transactions on Industrial Electronics	en_US
dc.relation.isbasedon	10.1109/TIE.2010.2055770	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Competitive strategic bidding optimization in electricity markets using bilevel programming and swarm technique	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	58	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
dc.location.activity	WOS:000290628500009	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	58	en_US

Abstract:

Competitive strategic bidding optimization is now a key issue in electricity generator markets. Digital ecosystems provide a powerful technological foundation and support for the implementation of the optimization. This paper presents a new strategic bidding optimization technique which applies bilevel programming and swarm intelligence. In this paper, we first propose a general multileader-one-follower nonlinear bilevel (MLNB) optimization concept and related definitions based on the generalized Nash equilibrium. By analyzing the strategic bidding behavior of generating companies, we create a specific MLNB decision model for day-ahead electricity markets. The MLNB decision model allows each generating company to choose its biddings to maximize its individual profit, and a market operator can find its minimized purchase electricity fare, which is determined by the output power of each unit and the uniform marginal prices. We then develop a particle-swarm-optimization-based algorithm to solve the problem defined in the MLNB decision model. The experiment results on a strategic bidding problem for a day-ahead electricity market have demonstrated the validity of the proposed decision model and algorithm. © 2010 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/15363