A genetic algorithm solution to the unit commitment problem

Author

Kazarlis, S.A. ; Bakirtzis, A.G. ; Petridis, V.

Author_Institution

Dept. of Electr. & Comput. Eng., Aristotelian Univ. of Thessaloniki, Greece

Volume

11

Issue

1

fYear

1996

fDate

2/1/1996 12:00:00 AM

Firstpage

83

Lastpage

92

Abstract

This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the varying quality function technique and adding problem specific operators, satisfactory solutions to the unit commitment problem were obtained. Test results for power systems of up to 100 units and comparisons with results obtained using Lagrangian relaxation and dynamic programming are also reported

Keywords

dynamic programming; genetic algorithms; load dispatching; load distribution; power system analysis computing; power system planning; CPU time; Lagrangian relaxation; computer simulation; crossover; dynamic programming; genetic algorithm; genetic recombination; mutation operators; natural selection; optimization techniques; planning problems; power systems; survival of the fittest; unit commitment; varying quality function technique; Cost function; Dynamic programming; Fuels; Genetic algorithms; Lagrangian functions; Power generation economics; Power system dynamics; Power system economics; Power systems; Production;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.485989

Filename

485989