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Particle Swarm Optimization Performance: Comparison of Dynamic Economic Dispatch with Dantzig-Wolfe Decomposition

Journal from gdlhub / 2016-11-08 10:34:04
Oleh : Mohd Ruddin Ab Ghani, Saif Tahseen Hussein, Zanariah Jano, Tole Sutikno, Telkomnika
Dibuat : 2016-09-01, dengan 1 file

Keyword : Particle Swarm Optimization (PSO), Dantzig-Wolfe Decomposition, Problem Formulation, Dynamic Economic Dispatch (DED).
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4054

Economic Dispatch (ED) problem, in practice, is a nonlinear, non-convex type,which has developed gradually into a serious task management goal in the planning phase of the power system. The prime purpose of Dynamic Economic Dispatch (DED) is to minimize generators’ total cost of the power system. DED is to engage the committed generating units at a minimum cost to meet the load demand while fulfilling various constraints. Utilizing heuristic, population-based, and advanced optimization technique, Particle Swarm Optimization (PSO), represents a challenging problem with large dimension in providing a superior solution for DED optimization problem. The feasibility of the PSO method has been demonstrated technically, and economically for two different systems, and it is compared with the Dantzig-Wolfe technique regarding the solution quality and simplicity of implementation. While Dantzig-Wolfe method has its intrinsic drawbacks and positive features, PSO algorithm is the finest and the most appropriate solution. Conventional techniques have been unsuccessful to present compatible solutions to such problems due to their susceptibility to first estimates and possible entrapment into local optima which may complicate computations.

Deskripsi Alternatif :

Economic Dispatch (ED) problem, in practice, is a nonlinear, non-convex type,which has developed gradually into a serious task management goal in the planning phase of the power system. The prime purpose of Dynamic Economic Dispatch (DED) is to minimize generators’ total cost of the power system. DED is to engage the committed generating units at a minimum cost to meet the load demand while fulfilling various constraints. Utilizing heuristic, population-based, and advanced optimization technique, Particle Swarm Optimization (PSO), represents a challenging problem with large dimension in providing a superior solution for DED optimization problem. The feasibility of the PSO method has been demonstrated technically, and economically for two different systems, and it is compared with the Dantzig-Wolfe technique regarding the solution quality and simplicity of implementation. While Dantzig-Wolfe method has its intrinsic drawbacks and positive features, PSO algorithm is the finest and the most appropriate solution. Conventional techniques have been unsuccessful to present compatible solutions to such problems due to their susceptibility to first estimates and possible entrapment into local optima which may complicate computations.

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