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Article: Simulation embedded artificial intelligence search method for supplier trading portfolio decision

TitleSimulation embedded artificial intelligence search method for supplier trading portfolio decision
Authors
Issue Date2010
PublisherThe Institution of Engineering and Technology. The Journal's web site is located at http://www.ietdl.org/IP-GTD
Citation
IET Generation, Transmission And Distribution, 2010, v. 4 n. 2, p. 221-230 How to Cite?
AbstractAn electric power supplier in the deregulated environment needs to allocate its generation capacities to participate in contract and spot markets. Different trading portfolios will provide suppliers with different future revenue streams of various distributions. The classical mean-variance (MV) method is inappropriate to deal with the trading portfolios whose return distribution is non-normal. In order to consider the non-normal characteristics in electricity trading, this study proposes a new model based on expected utility theory (EUT) and employs a hybrid genetic algorithm (GA) - Monte-Carlo simulation technique as solution approach. In the real market data-based numerical studies, the performances of the proposed method and the standard MV method are compared. It was found that the proposed method is able to obtain better portfolios than MV method when non-normal asset exists for trading. The simulation results also reveal the accumulation effect along trading period, which will improve the normality of the supplier trading portfolios. The authors believe the proposed method is a useful complement for the MV method and conditional value at risk (CVaR)-based methods in the supplier trading portfolio decision and evaluation. © 2010 © The Institution of Engineering and Technology.
Persistent Identifierhttp://hdl.handle.net/10722/155570
ISSN
2015 Impact Factor: 1.576
2015 SCImago Journal Rankings: 1.332
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorFeng, Den_US
dc.contributor.authorYan, Zen_US
dc.contributor.authorØstergaard, Jen_US
dc.contributor.authorXu, Zen_US
dc.contributor.authorGan, Den_US
dc.contributor.authorZhong, Jen_US
dc.contributor.authorZhang, Nen_US
dc.contributor.authorDai, Ten_US
dc.date.accessioned2012-08-08T08:34:09Z-
dc.date.available2012-08-08T08:34:09Z-
dc.date.issued2010en_US
dc.identifier.citationIET Generation, Transmission And Distribution, 2010, v. 4 n. 2, p. 221-230en_US
dc.identifier.issn1751-8687en_US
dc.identifier.urihttp://hdl.handle.net/10722/155570-
dc.description.abstractAn electric power supplier in the deregulated environment needs to allocate its generation capacities to participate in contract and spot markets. Different trading portfolios will provide suppliers with different future revenue streams of various distributions. The classical mean-variance (MV) method is inappropriate to deal with the trading portfolios whose return distribution is non-normal. In order to consider the non-normal characteristics in electricity trading, this study proposes a new model based on expected utility theory (EUT) and employs a hybrid genetic algorithm (GA) - Monte-Carlo simulation technique as solution approach. In the real market data-based numerical studies, the performances of the proposed method and the standard MV method are compared. It was found that the proposed method is able to obtain better portfolios than MV method when non-normal asset exists for trading. The simulation results also reveal the accumulation effect along trading period, which will improve the normality of the supplier trading portfolios. The authors believe the proposed method is a useful complement for the MV method and conditional value at risk (CVaR)-based methods in the supplier trading portfolio decision and evaluation. © 2010 © The Institution of Engineering and Technology.en_US
dc.languageengen_US
dc.publisherThe Institution of Engineering and Technology. The Journal's web site is located at http://www.ietdl.org/IP-GTDen_US
dc.relation.ispartofIET Generation, Transmission and Distributionen_US
dc.titleSimulation embedded artificial intelligence search method for supplier trading portfolio decisionen_US
dc.typeArticleen_US
dc.identifier.emailZhong, J:jinzhong@hkucc.hku.hken_US
dc.identifier.authorityZhong, J=rp00212en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1049/iet-gtd.2009.0096en_US
dc.identifier.scopuseid_2-s2.0-77953443801en_US
dc.identifier.hkuros201936-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77953443801&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume4en_US
dc.identifier.issue2en_US
dc.identifier.spage221en_US
dc.identifier.epage230en_US
dc.identifier.isiWOS:000274626100010-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridFeng, D=7401981343en_US
dc.identifier.scopusauthoridYan, Z=7402519416en_US
dc.identifier.scopusauthoridØstergaard, J=7004506852en_US
dc.identifier.scopusauthoridXu, Z=24492630500en_US
dc.identifier.scopusauthoridGan, D=7005499404en_US
dc.identifier.scopusauthoridZhong, J=13905948700en_US
dc.identifier.scopusauthoridZhang, N=55187562100en_US
dc.identifier.scopusauthoridDai, T=16063282500en_US

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