A hybrid biased random key genetic algorithm approach for the unit commitment problem

L. A. C. Roque D. B. M. M. Fontes 1 F. A. C. C. Fontes 2
1 DEEC - Departamento de Engenharia Electrotécnica e de Computadores [Porto]
2 Universidade do Porto [Porto]
Abstract : This work proposes a hybrid genetic algorithm (GA) to address the unit commitment (UC) problem. In the UC problem, the goal is to schedule a subset of a given group of electrical power generating units and also to determine their production output in order to meet energy demands at minimum cost. In addition, the solution must satisfy a set of technological and operational constraints. The algorithm developed is a hybrid biased random key genetic algorithm (HBRKGA). It uses random keys to encode the solutions and introduces bias both in the parent selection procedure and in the crossover strategy. To intensify the search close to good solutions, the GA is hybridized with local search. Tests have been performed on benchmark large-scale power systems. The computational results demonstrate that the HBRKGA is effective and efficient. In addition, it is also shown that it improves the solutions obtained by current state-of-the-art methodologies.
Type de document :
Article dans une revue
Journal of Combinatorial Optimization, Springer Verlag, 2014, 28 (1), pp.140-166. 〈10.1007/s10878-014-9710-8〉
Liste complète des métadonnées
https://hal.inria.fr/hal-01122263
Contributeur : Estelle Bouzat <>
Soumis le : mardi 3 mars 2015 - 15:14:22
Dernière modification le : lundi 21 mars 2016 - 11:29:12

Identifiants

Collections

INSMI | SADCO | TDS-MACS

Citation

L. A. C. Roque, D. B. M. M. Fontes, F. A. C. C. Fontes. A hybrid biased random key genetic algorithm approach for the unit commitment problem. Journal of Combinatorial Optimization, Springer Verlag, 2014, 28 (1), pp.140-166. 〈10.1007/s10878-014-9710-8〉. 〈hal-01122263〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

75

Contact


archive-ouverte@inria.fr