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Conference Paper: Supplier multi-trading strategy: A stochastic programming approach

TitleSupplier multi-trading strategy: A stochastic programming approach
Authors
KeywordsElectricity Market
Genetic Algorithm
Monte Carlo Simulation
Multitrading Strategy
Portfolio Optimization
Risk Management
Issue Date2008
Citation
Ieee Power And Energy Society 2008 General Meeting: Conversion And Delivery Of Electrical Energy In The 21St Century, Pes, 2008 How to Cite?
AbstractA power supplier in deregulated environment needs to allocate its generation capacities to participate in contract and spot markets. The well-known mean-variance method is inappropriate to deal with assets whose price distribution is nonnormal. In order to model the electricity assets with different distributions into portfolio optimization, this paper proposes a stochastic programming approach based on Genetic Algorithm and Monte-Carlo simulation. In the real market data based numerical study, the performances of the proposed method and the standard mean-variance method are compared. It was found that the proposed method can obtain significantly better portfolios in the situation that non-normally distributed assets exist for trading. The modeling capacity, flexibility and robustness will make the proposed method potentially useful in application. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/158562
References

 

DC FieldValueLanguage
dc.contributor.authorFeng, Den_US
dc.contributor.authorGan, Den_US
dc.contributor.authorZhong, Jen_US
dc.date.accessioned2012-08-08T09:00:16Z-
dc.date.available2012-08-08T09:00:16Z-
dc.date.issued2008en_US
dc.identifier.citationIeee Power And Energy Society 2008 General Meeting: Conversion And Delivery Of Electrical Energy In The 21St Century, Pes, 2008en_US
dc.identifier.urihttp://hdl.handle.net/10722/158562-
dc.description.abstractA power supplier in deregulated environment needs to allocate its generation capacities to participate in contract and spot markets. The well-known mean-variance method is inappropriate to deal with assets whose price distribution is nonnormal. In order to model the electricity assets with different distributions into portfolio optimization, this paper proposes a stochastic programming approach based on Genetic Algorithm and Monte-Carlo simulation. In the real market data based numerical study, the performances of the proposed method and the standard mean-variance method are compared. It was found that the proposed method can obtain significantly better portfolios in the situation that non-normally distributed assets exist for trading. The modeling capacity, flexibility and robustness will make the proposed method potentially useful in application. © 2008 IEEE.en_US
dc.languageengen_US
dc.relation.ispartofIEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PESen_US
dc.subjectElectricity Marketen_US
dc.subjectGenetic Algorithmen_US
dc.subjectMonte Carlo Simulationen_US
dc.subjectMultitrading Strategyen_US
dc.subjectPortfolio Optimizationen_US
dc.subjectRisk Managementen_US
dc.titleSupplier multi-trading strategy: A stochastic programming approachen_US
dc.typeConference_Paperen_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.doi10.1109/PES.2008.4596119en_US
dc.identifier.scopuseid_2-s2.0-52349115658en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-52349115658&selection=ref&src=s&origin=recordpageen_US
dc.identifier.scopusauthoridFeng, D=7401981343en_US
dc.identifier.scopusauthoridGan, D=7005499404en_US
dc.identifier.scopusauthoridZhong, J=13905948700en_US

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