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Article: Optimal trading strategy for GenCo in LMP-based and bilateral markets using self-organising hierarchical PSO

TitleOptimal trading strategy for GenCo in LMP-based and bilateral markets using self-organising hierarchical PSO
Authors
KeywordsOptimal bidding strategy
Energy trading
Locational marginal price
Bilateral contract market
Particle swarm optimisation
Issue Date2010
PublisherInternational Journal of Engineering, Science and Technology. The Journal's web site is located at http://www.ijest-ng.com
Citation
International Journal of Engineering, Science and Technology, 2010, v. 2 n. 3, p. 82-93 How to Cite?
AbstractThis paper proposes an optimal trading strategy for a generation company (GenCo) in multi-market environment including day-ahead spot and long term bilateral contract markets using self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO-TVAC). The proposed trading strategy is formulated as a two-stage optimisation problem. Firstly, the GenCo’s objective model which is to maximise expected profit and to minimise risk of profit variation is solved by SPSO-TVAC. Secondly, the market clearing model which is to minimise system cost of locational marginal price (LMP) based market is solved by DC optimal power flow (DCOPF). The Monte Carlo method is employed to simulate other bidders’ behaviour in competitive environment. Test results on the PJM 5-bus system indicate that SPSO-TVAC is superior to inertia weight approach particle swarm optimisation (IWAPSO) and genetic algorithm (GA) in searching the optimal trading solution. In addition, different bilateral contract prices and spot demand significantly impact GenCos’ trading behaviour. Accordingly, the proposed approach could be a beneficial decision-making tool for a GenCo in energy trading.
Persistent Identifierhttp://hdl.handle.net/10722/124666
ISSN

 

DC FieldValueLanguage
dc.contributor.authorBoonchuay, Cen_HK
dc.contributor.authorOngsakul, Wen_HK
dc.contributor.authorZhong, Jen_HK
dc.contributor.authorWu, Fen_HK
dc.date.accessioned2010-10-31T10:47:23Z-
dc.date.available2010-10-31T10:47:23Z-
dc.date.issued2010en_HK
dc.identifier.citationInternational Journal of Engineering, Science and Technology, 2010, v. 2 n. 3, p. 82-93en_HK
dc.identifier.issn2141-2820-
dc.identifier.urihttp://hdl.handle.net/10722/124666-
dc.description.abstractThis paper proposes an optimal trading strategy for a generation company (GenCo) in multi-market environment including day-ahead spot and long term bilateral contract markets using self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO-TVAC). The proposed trading strategy is formulated as a two-stage optimisation problem. Firstly, the GenCo’s objective model which is to maximise expected profit and to minimise risk of profit variation is solved by SPSO-TVAC. Secondly, the market clearing model which is to minimise system cost of locational marginal price (LMP) based market is solved by DC optimal power flow (DCOPF). The Monte Carlo method is employed to simulate other bidders’ behaviour in competitive environment. Test results on the PJM 5-bus system indicate that SPSO-TVAC is superior to inertia weight approach particle swarm optimisation (IWAPSO) and genetic algorithm (GA) in searching the optimal trading solution. In addition, different bilateral contract prices and spot demand significantly impact GenCos’ trading behaviour. Accordingly, the proposed approach could be a beneficial decision-making tool for a GenCo in energy trading.-
dc.languageengen_HK
dc.publisherInternational Journal of Engineering, Science and Technology. The Journal's web site is located at http://www.ijest-ng.com-
dc.relation.ispartofInternational Journal of Engineering, Science and Technologyen_HK
dc.subjectOptimal bidding strategy-
dc.subjectEnergy trading-
dc.subjectLocational marginal price-
dc.subjectBilateral contract market-
dc.subjectParticle swarm optimisation-
dc.titleOptimal trading strategy for GenCo in LMP-based and bilateral markets using self-organising hierarchical PSOen_HK
dc.typeArticleen_HK
dc.identifier.emailZhong, J: jinzhong@hkucc.hku.hken_HK
dc.identifier.emailWu, F: ffwu@eee.hku.hken_HK
dc.identifier.authorityZhong, J=rp00212en_HK
dc.identifier.authorityWu, FF=rp00194en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.hkuros171901en_HK
dc.identifier.volume2en_HK
dc.identifier.issue3-
dc.identifier.spage82en_HK
dc.identifier.epage93en_HK
dc.publisher.placeNigeria-

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