Development of an Intelligent Tool for Planning the Operation
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.
[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.
Comparação entre Abordagem Estocástica e Determinística no
Planejamento da Opera├º├úo de Médio Prazo de Sistemas Hidrotérmicos
Interligados. XVII SNPTEE - Seminário Nacional de Produção 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. Utilização 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 São 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 Potência. 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
Inteligência 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).
[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.
Comparação entre Abordagem Estocástica e Determinística no
Planejamento da Opera├º├úo de Médio Prazo de Sistemas Hidrotérmicos
Interligados. XVII SNPTEE - Seminário Nacional de Produção 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. Utilização 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 São 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 Potência. 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
Inteligência 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).
@article{"International Journal of Electrical, Electronic and Communication Sciences:50238", author = "T. R. Alencar and P. T. Leite", title = "Development of an Intelligent Tool for Planning the Operation", abstract = "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", volume = "4", number = "3", pages = "398-7", }
