Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31103
Development of an Intelligent Tool for Planning the Operation

Authors: T. R. Alencar, P. T. Leite


Several optimization algorithms specifically applied to the problem of Operation Planning of Hydrothermal Power Systems have been developed and are used. Although providing solutions to various problems encountered, these algorithms have some weaknesses, difficulties in convergence, simplification of the original formulation of the problem, or owing to the complexity of the objective function. Thus, this paper presents the development of a computational tool for solving optimization problem identified and to provide the User an easy handling. Adopted as intelligent optimization technique, Genetic Algorithms and programming language Java. First made the modeling of the chromosomes, then implemented the function assessment of the problem and the operators involved, and finally the drafting of the graphical interfaces for access to the User. The program has managed to relate a coherent performance in problem resolution without the need for simplification of the calculations together with the ease of manipulating the parameters of simulation and visualization of output results.

Keywords: Energy, Optimization, Hydrothermal Power Systemsand Genetic Algorithms

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1402


[1] E. L. Silva. Forma├º├úo de Pre├ºo em Mercados de Energia Elétrica. Editora Sagra Luzzatto, (2001).
[2] P. T. Leite, A. A. F. M. Carneiro and A. C. P. L. F. Carvalho, "Energetic Operation Planning Using Genetic Algorithms", IEEE Transaction on Power Systems, vol. 17, no. 1, February 2002, pp. 173-179.
[3] M. V. Perreira. Overview-Optimal Scheduling of Hydrothermal Systems. IFAC Symposium on Planning and Operation of Electric Energy Systems, pg. 1-9, (1985).
[4] M. E. P. Macieira and R. M. Marcato and A. L. M. Marcato. Compara├º├úo entre Abordagem Estoc├ística e Determin├¡stica no Planejamento da Opera├º├úo de Médio Prazo de Sistemas Hidrotérmicos Interligados. XVII SNPTEE - Semin├írio Nacional de Produ├º├úo e Transmiss├úo de Energia Elétrica. Uberl├óndia - MG, Brasil. (2003).
[5] M. Basu. An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling. Electric Power Systems, pg. 277-285. (2004).
[6] T. R. Alencar, P. T. Leite. Utiliza├º├úo de uma ferramenta inteligente na determina├º├úo do planejamento energético. 30┬░ CILAMCE - Congresso Ibero-Latino-Americano de Métodos Computacionais em Engenharia. B├║zios - RJ, Brasil. (2009).
[7] K. K. Mandal, M. Basu, N. Chakraborty. Particle swarm optimization technique based short-term hydrothermal scheduling. Applied Soft Computing, pg. 1392-1399. (2007).
[8] R. C. Zambon. Planejamento da Opera├º├úo de Sistemas Hidrotérmicos de Grande Porte. Tese de Doutorado - Escola Politécnica da Universidade de S├úo Paulo, (2008).
[9] T. R. Alencar, P. T. Leite. Desenvolvimento de uma Ferramenta Inteligente para o Planejamento da Opera├º├úo de Sistemas Hidrotérmicos de Pot├¬ncia. 8th CLAGTEE - Latin-American Congress on Electricity Generation and Transmission. Ubatuba - SP, Brasil. (2009).
[10] C. O. GALVÃO e M. J. S. VALENÇA. Sistemas Inteligentes - Aplicação a Recursos Hídricos e Ciência Ambiental. Editora da Universidade, Porto Alegre, Universidade Federal do Rio Grande do Sul, ABRH, Associação Brasileira de Recursos Hídricos.1999.
[11] R. Linden. Algoritmos Genéticos - Uma importante ferramenta da Intelig├¬ncia Computacional. Editora Brasport, Rio de Janeiro - RJ. 2006.
[12] A. A. F. M. Carneiro and S. Soares and P. S. Bond. A Large Scale Application of an Optimal Deterministic Hydrothermal Scheduling Algorithm. IEEE Trasaction on Power Systems. Vol. 5. n. 1. pp. 204- 210. February. (1990).
[13] D. E. Goldberg. Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley Pub. Co. (1989).
[14] D. Beasley and D. Bull and R. Martin. An Overview of Genetic Algorithms: Part 1, Fundamentals. Inter-University Commitee on Computing. (1993).
[15] M. Gen and R. Cheng. Genetic Algorithms and Enginneering Design. Reading. MA., Addison Wesley. United States of America. (1989).
[16] D. Corne, M. Dorigo and F. Glover. New ideas in optimization. David Hatter, McGraw-Hill. Vol. 1. pp. 493. Great Britain at the University Press, Cambridge. (1999).
[17] Salomon. Evolutionary Algorithms and Gradient Search: Similarities and Differences. IEEE Transactions on Evolutionary Computation. Vol. 2. n. 2. pp. 45-55. July. (1998).