Particle Swarm Optimization Performance: Comparison of Dynamic Economic Dispatch with Dantzig-Wolfe Decomposition
Abstract: Economic Dispatch
(ED) problem, in practice, is a nonlinear, non-convex type,whish 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.
Keywords: particle swarm optimization (PSO),
Dantzig-Wolfe decomposition, problem formulation, dynamic economic dispatch
(DED)
Author: Mohd Ruddin Ab Ghani
Journal Code: jptkomputergg160135
