Journal
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
Volume 30, Issue 5, Pages 297-307Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2007.08.001
Keywords
Economic dispatch problem; electric power generation; particle swarm optimization; thermal generator constraints; chaotic sequences
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The objective of the Economic Dispatch Problems (EDPs) of electric power generation is to schedule the committed generating units outputs so as to meet the required load demand at minimum operating cost while satisfying all units and system equality and inequality constraints. Recently, global optimization approaches inspired by swarm intelligence and evolutionary computation approaches have proven to be a potential alternative for the optimization of difficult EDPs. Particle swarm optimization (PSO) is a population-based stochastic algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Inspired by the swarm intelligence and probabilities theories, this work presents the use of combining of PSO, Gaussian probability distribution functions and/or chaotic sequences. In this context, this paper proposes improved PSO approaches for solving EDPs that takes into account nonlinear generator features such as ramp-rate limits and prohibited operating zones in the power system operation. The PSO and its variants are validated for two test systems consisting of 15 and 20 thermal generation units. The proposed combined method outperforms other modern metaheuristic optimization techniques reported in the recent literature in solving for the two constrained EDPs case studies. (c) 2007 Elsevier Ltd. All rights reserved.
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