Please use this identifier to cite or link to this item: http://dspace2020.uniten.edu.my:8080/handle/123456789/7658
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dc.contributor.authorRahmat, N.A.
dc.contributor.authorMusirin, I.
dc.date.accessioned2018-01-11T10:00:23Z-
dc.date.available2018-01-11T10:00:23Z-
dc.date.issued2012
dc.identifier.urihttp://dspace.uniten.edu.my/jspui/handle/123456789/7658-
dc.description.abstractElectric utilities are the companies responsible for ensuring energy supply meets their customers' requirement. While ensuring the energy is generated in the right amount, they have to guarantee that the energy is generated within feasible cost. Economic Load Dispatch (ELD) problem involves the scheduling of generating unit outputs that can satisfy load demand at minimum operating cost. Several approaches have been applied to yield the best solution for the problem, such as Genetic Algorithm, Bees Algorithm and Neural Network Algorithm. This paper presents Differential Evolution Ant Colony Optimization (DEACO) to optimize Economic Load Dispatch in power system. Implementation of the IEEE Reliability Test System (RTS) demonstrated that this technique is feasible to crack the economic problem. Comparative studies with respect to ACO and the traditional ELD techniques designate that the proposed DEACO outperformed these two techniques. © 2012 IEEE.
dc.titleDifferential Evolution Ant Colony Optimization (DEACO) technique in solving economic load dispatch problem
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