The solution of the economic dispatch problem via anefficient teaching-learning-based optimizationmethod

Abstract

This paper is concerned with the economic generation dispatch problem. It is a well-knownfact that practical aspects of power plant equipment, as well as the objectives to be met, may result in anonconvex, nondifferentiable model that poses difficulties to conventional mathematical programmingmethods. This paper proposes the use of metaheuristic Teaching-Learning-Based Optimization toovercome such difficulties. This metaheuristic is well known for requiring a few parameters and, mostimportantly, it does not require the tuning of problem-dependent parameters. The algorithm proposedin this work is parameter-free; that is, the few parameters required by the Teaching-Learning-BasedOptimization method are set automatically based on the power system’s data. In addition, the handling ofconstraints, such as generators’ prohibited zones and the generator-load-loss power balance, is performedin a very efficient way. Simulation results are shown for power systems containing 3 to 40 generationunits, and the results provided by the proposed method are shown and discussed based on comparisonswith other metaheuristics and a mathematical programming technique