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- Publisher Website: 10.1109/PES.2008.4596119
- Scopus: eid_2-s2.0-52349115658
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Conference Paper: Supplier multi-trading strategy: A stochastic programming approach
Title | Supplier multi-trading strategy: A stochastic programming approach |
---|---|
Authors | |
Keywords | Electricity Market Genetic Algorithm Monte Carlo Simulation Multitrading Strategy Portfolio Optimization Risk Management |
Issue Date | 2008 |
Citation | Ieee Power And Energy Society 2008 General Meeting: Conversion And Delivery Of Electrical Energy In The 21St Century, Pes, 2008 How to Cite? |
Abstract | A 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 Identifier | http://hdl.handle.net/10722/158562 |
References |
DC Field | Value | Language |
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dc.contributor.author | Feng, D | en_US |
dc.contributor.author | Gan, D | en_US |
dc.contributor.author | Zhong, J | en_US |
dc.date.accessioned | 2012-08-08T09:00:16Z | - |
dc.date.available | 2012-08-08T09:00:16Z | - |
dc.date.issued | 2008 | en_US |
dc.identifier.citation | Ieee Power And Energy Society 2008 General Meeting: Conversion And Delivery Of Electrical Energy In The 21St Century, Pes, 2008 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/158562 | - |
dc.description.abstract | A 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.language | eng | en_US |
dc.relation.ispartof | IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES | en_US |
dc.subject | Electricity Market | en_US |
dc.subject | Genetic Algorithm | en_US |
dc.subject | Monte Carlo Simulation | en_US |
dc.subject | Multitrading Strategy | en_US |
dc.subject | Portfolio Optimization | en_US |
dc.subject | Risk Management | en_US |
dc.title | Supplier multi-trading strategy: A stochastic programming approach | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Zhong, J:jinzhong@hkucc.hku.hk | en_US |
dc.identifier.authority | Zhong, J=rp00212 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1109/PES.2008.4596119 | en_US |
dc.identifier.scopus | eid_2-s2.0-52349115658 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-52349115658&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.scopusauthorid | Feng, D=7401981343 | en_US |
dc.identifier.scopusauthorid | Gan, D=7005499404 | en_US |
dc.identifier.scopusauthorid | Zhong, J=13905948700 | en_US |