Comparative study of the Genetic Algorithms and Hessians Method for Minimization of the Electric Power Production Cost
In this paper, we present a comparative study of the
genetic algorithms and Hessian-s methods for optimal research of the
active powers in an electric network of power. The objective function
which is the performance index of production of electrical energy is
minimized by satisfying the constraints of the equality type and
inequality type initially by the Hessian-s methods and in the second
time by the genetic Algorithms. The results found by the application
of AG for the minimization of the electric production costs of power
are very encouraging. The algorithms seem to be an effective
technique to solve a great number of problems and which are in
constant evolution. Nevertheless it should be specified that the
traditional binary representation used for the genetic algorithms
creates problems of optimization of management of the large-sized
networks with high numerical precision.
[1] Holland J.H., "Adaptation in natural and artificial system", Ann Arbor,
The University of Michigan Press, 1975.
[2] D.E Goldberg, "Genetic Algorithm in Search Optimization and Machine
Learning", Addison Wesley 1994.
[3] D.M Himmelblau, "Applied non linear programming Edition Mc
Graw-Hill, 1972
[4] M. Minoux: "Programmation mathématique: Théorie et algorithmes
Tome 1" Edition Dunod, 1983.
[5] J.C Dodu et P. Huard: "Méthodes quasi-newtoniennes sous contraintes
non linéaires "Bulletin de la direction des études de recherche,
Electricité de France, série C, N┬░2, 1988.
[6] M. Rahli et P. Pirotte, "Dispatching économique par une nouvelle
méthode de programmation non linéaire ├á la répartition économique des
puissances actives dans un réseau d-énergie électrique", CIMASI-96
Casablanca, Maroc, 14-16 Novembre 1996,pp325-330.
[7] T. Vallée et M. Yildizoglu, "Présentation des algorithmes génétiques et
de leurs applications en économie. " 7 septembre 2001, v. 1.2
[8] Z. Michalewicz, et N.F. Attia, "Evolutionary optimization of constrained
problems", Proceedings of the 3rd Annuel Conference on Evolutionary
Programming, World Scientific, pp. 98-108.
[9] M. Rahli, "Contribution ├á l-Etude de la Répartition Optimale des
Puissances Actives dans un Réseau d-Energie Electrique", thèse de
doctorat, 06 janvier 1996, USTO. Algérie
[10] L. Abdelmalek, "Répartition Optimale des Puissances Actives et
Réactives par les méthodes Hessiennes", CIMASI-2002, Casablanca 22-
25 Octobre 2002. Maroc.
[11] M. Rahli et P. Pirotte, "Optimal load flow using sequential
unconstrained minimization technique SUMT method under power
transmission losses minimization", Electric Power Research, 1999
Elsevier Science.
[12] R. Ouiddir et M. Rahli, "Dispatching Economique Actif dans un Réseau
d-Energie Electrique par un Algorithme Génétique", 2nd International
Conférence on Electrotechnics, 13-15 Novembre2000, ICEL2000,
USTOran, Algérie.
[13] L. Drdi, Extrait du 1er chapitre de la thèse de doctorat INRS-ETE, 2005.
[1] Holland J.H., "Adaptation in natural and artificial system", Ann Arbor,
The University of Michigan Press, 1975.
[2] D.E Goldberg, "Genetic Algorithm in Search Optimization and Machine
Learning", Addison Wesley 1994.
[3] D.M Himmelblau, "Applied non linear programming Edition Mc
Graw-Hill, 1972
[4] M. Minoux: "Programmation mathématique: Théorie et algorithmes
Tome 1" Edition Dunod, 1983.
[5] J.C Dodu et P. Huard: "Méthodes quasi-newtoniennes sous contraintes
non linéaires "Bulletin de la direction des études de recherche,
Electricité de France, série C, N┬░2, 1988.
[6] M. Rahli et P. Pirotte, "Dispatching économique par une nouvelle
méthode de programmation non linéaire ├á la répartition économique des
puissances actives dans un réseau d-énergie électrique", CIMASI-96
Casablanca, Maroc, 14-16 Novembre 1996,pp325-330.
[7] T. Vallée et M. Yildizoglu, "Présentation des algorithmes génétiques et
de leurs applications en économie. " 7 septembre 2001, v. 1.2
[8] Z. Michalewicz, et N.F. Attia, "Evolutionary optimization of constrained
problems", Proceedings of the 3rd Annuel Conference on Evolutionary
Programming, World Scientific, pp. 98-108.
[9] M. Rahli, "Contribution ├á l-Etude de la Répartition Optimale des
Puissances Actives dans un Réseau d-Energie Electrique", thèse de
doctorat, 06 janvier 1996, USTO. Algérie
[10] L. Abdelmalek, "Répartition Optimale des Puissances Actives et
Réactives par les méthodes Hessiennes", CIMASI-2002, Casablanca 22-
25 Octobre 2002. Maroc.
[11] M. Rahli et P. Pirotte, "Optimal load flow using sequential
unconstrained minimization technique SUMT method under power
transmission losses minimization", Electric Power Research, 1999
Elsevier Science.
[12] R. Ouiddir et M. Rahli, "Dispatching Economique Actif dans un Réseau
d-Energie Electrique par un Algorithme Génétique", 2nd International
Conférence on Electrotechnics, 13-15 Novembre2000, ICEL2000,
USTOran, Algérie.
[13] L. Drdi, Extrait du 1er chapitre de la thèse de doctorat INRS-ETE, 2005.
@article{"International Journal of Electrical, Electronic and Communication Sciences:65006", author = "L. Abdelmalek and M. Zerikat and M. Rahli", title = "Comparative study of the Genetic Algorithms and Hessians Method for Minimization of the Electric Power Production Cost", abstract = "In this paper, we present a comparative study of the
genetic algorithms and Hessian-s methods for optimal research of the
active powers in an electric network of power. The objective function
which is the performance index of production of electrical energy is
minimized by satisfying the constraints of the equality type and
inequality type initially by the Hessian-s methods and in the second
time by the genetic Algorithms. The results found by the application
of AG for the minimization of the electric production costs of power
are very encouraging. The algorithms seem to be an effective
technique to solve a great number of problems and which are in
constant evolution. Nevertheless it should be specified that the
traditional binary representation used for the genetic algorithms
creates problems of optimization of management of the large-sized
networks with high numerical precision.", keywords = "Genetic algorithm, Flow of optimum loadimpedances, Hessians method, Optimal distribution.", volume = "1", number = "10", pages = "1558-8", }