Unit commitment by genetic algorithms

Author

Shanthi, V. ; Jeyakumar, A. Ebenezer

fYear

2004

fDate

10-13 Oct. 2004

Firstpage

1329

Abstract

This paper presents an application of the genetic algorithms (GA) method for the unit commitment problem. Genetic algorithms (GA´s) are a general purpose optimization technique based on principle of natural selection and natural genetics. The Lagrangian relaxation (LR) method provides a fast solution but it may suffer from numerical convergence and solution quality problems. Numerical results on a system of 10 units are compared with results obtained using Lagrange relaxation (LR) and genetic algorithms (GA´s), show that the feature of easy implementation, better convergence, and highly near-optimal solution to the UC problem can be achieved by the GA.

Keywords

convergence of numerical methods; genetic algorithms; power generation dispatch; power generation economics; power generation scheduling; relaxation theory; Lagrangian relaxation method; economic dispatch; genetic algorithm; natural genetics; natural selection; numerical convergence; optimization technique; solution quality problem; unit commitment; Convergence of numerical methods; Cost function; Dynamic programming; Economic forecasting; Genetic algorithms; Lagrangian functions; Optimization methods; Power system planning; Production systems; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Systems Conference and Exposition, 2004. IEEE PES

Print_ISBN

0-7803-8718-X

Type

conf

DOI

10.1109/PSCE.2004.1397640

Filename

1397640