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Article: Application of improved partheno-genetic algorithm in generation expansion planning of power system

TitleApplication of improved partheno-genetic algorithm in generation expansion planning of power system
Authors
KeywordsGeneration Expansion Planning
Partheno-Genetic Algorithm
Power System
Special Operator
Issue Date2004
PublisherPower System Technology Press. The Journal's web site is located at http://www.dwjs.com.cn/
Citation
Power System Technology, 2004, v. 28 n. 3, p. 11-15 How to Cite?
AbstractThe generation expansion planning (GEP) is essentially a complicated multi-stage combinatorial optimization problem. Traditional genetic operation may produce many ineffective chromosomes which would decrease the efficiency of search. A model for the generation expansion planning of power system based on partheno-genetic algorithm is presented and a subsection coding method is successfully used to solve the chromosome-coding problem. A variety of constraints which should be considered in GEP can be easily taken into account. The influence of several special operators, such as elitist reserved operators and disturbance operators, on the convergence is researched. Simulation results show that the presented model is reliable and effective, and using this model the global optimal solution with some sub-optimal solutions can be obtained. Acceding special operator into the presented model, both the calculation accuracy and the convergence speed can be improved.
Persistent Identifierhttp://hdl.handle.net/10722/155290
ISSN
2015 SCImago Journal Rankings: 0.684
References

 

DC FieldValueLanguage
dc.contributor.authorFeng, Ken_US
dc.contributor.authorLi, YDen_US
dc.contributor.authorHou, YHen_US
dc.contributor.authorWu, YWen_US
dc.contributor.authorXiong, XYen_US
dc.date.accessioned2012-08-08T08:32:44Z-
dc.date.available2012-08-08T08:32:44Z-
dc.date.issued2004en_US
dc.identifier.citationPower System Technology, 2004, v. 28 n. 3, p. 11-15en_US
dc.identifier.issn1000-3673en_US
dc.identifier.urihttp://hdl.handle.net/10722/155290-
dc.description.abstractThe generation expansion planning (GEP) is essentially a complicated multi-stage combinatorial optimization problem. Traditional genetic operation may produce many ineffective chromosomes which would decrease the efficiency of search. A model for the generation expansion planning of power system based on partheno-genetic algorithm is presented and a subsection coding method is successfully used to solve the chromosome-coding problem. A variety of constraints which should be considered in GEP can be easily taken into account. The influence of several special operators, such as elitist reserved operators and disturbance operators, on the convergence is researched. Simulation results show that the presented model is reliable and effective, and using this model the global optimal solution with some sub-optimal solutions can be obtained. Acceding special operator into the presented model, both the calculation accuracy and the convergence speed can be improved.en_US
dc.languageengen_US
dc.publisherPower System Technology Press. The Journal's web site is located at http://www.dwjs.com.cn/en_US
dc.relation.ispartofPower System Technologyen_US
dc.subjectGeneration Expansion Planningen_US
dc.subjectPartheno-Genetic Algorithmen_US
dc.subjectPower Systemen_US
dc.subjectSpecial Operatoren_US
dc.titleApplication of improved partheno-genetic algorithm in generation expansion planning of power systemen_US
dc.typeArticleen_US
dc.identifier.emailHou, YH:yhhou@eee.hku.hken_US
dc.identifier.authorityHou, YH=rp00069en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-2542461483en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-2542461483&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume28en_US
dc.identifier.issue3en_US
dc.identifier.spage11en_US
dc.identifier.epage15en_US
dc.publisher.placeChinaen_US
dc.identifier.scopusauthoridFeng, K=36723653100en_US
dc.identifier.scopusauthoridLi, YD=7502095742en_US
dc.identifier.scopusauthoridHou, YH=7402198555en_US
dc.identifier.scopusauthoridWu, YW=7406898040en_US
dc.identifier.scopusauthoridXiong, XY=7201634426en_US

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