Solución de un problema de optimización clásico usando el paquete optimizador GAMS: implementación del problema de despacho económico
Main Article Content
Keywords
Pensamiento computacional, despacho económico, modelado matemático, paquete de optimización GAMS, problema de optimización no lineal
Resumen
En este artículo se presenta una estrategia para modelar y resolver problemas no lineales de optimización con estudiantes de pregrado en ingeniería eléctrica empleando el Sistema de Modelado Algebraico General (GAMS). El problema clásico conocido como despacho económico ha sido seleccionado para mostrar la necesidad de usar herramientas matemáticas para resolver problemas de gran tamaño relacionados con ingeniería. El despacho económico es un problema clásico de optimización en operación de sistemas termoeléctricos, cuya idea principal es encontrar la operación más económica para los generadores térmicos. Esta operación está basada en una curva cuadrática de costos con algunas restricciones operativas, como por ejemplo, balance de potencia y capacidades de generación. La simulación numérica es implementada en GAMS usando una versión de prueba. Esta investigación ha sido desarrollada con la ayuda de 36 estudiantes del curso de Regulación y Operación de Sistemas Eléctricos (ROES) del programa de Ingeniería Eléctrica de la Universidad Tecnológica de Pereira (UTP).
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[2] G. L. Donohue, “Undergraduate use of complex simulation tools for airspace design,” Computing in Science Engineering, vol. 5, no. 5, pp. 72–75, Sept 2003.
[3] J. H. Chow and K. W. Cheung, “A toolbox for power system dynamics and controlengineeringeducationandresearch,” IEEE Trans. Power Syst., vol.7, no. 4, pp. 1559–1564, Nov 1992.
[4] F. Milano, L. Vanfretti, and J. C. Morataya, “An open source power system virtual laboratory: The psat case and experience,” IEEE Trans. Educ., vol. 51, no. 1, pp. 17–23, Feb 2008.
[5] T. Boonseng and N. Sarasiri, “Dynamic railway simulation using digsilent programming language,” in 2016 19th International Conference on Electrical Machines and Systems (ICEMS), Nov 2016, pp. 1–4.
[6] P. Pradhan, T. Deki, P. Wangmo, D. Dorji, D. Phuntsho, and C. Dorji, “Simulation and optimization of blackstart restoration plan in bhutan using digsilent,” in 2015 2nd International Conference on Recent Advances in Engineering Computational Sciences (RAECS), Dec 2015, pp. 1–6.
[7] M. Brignone, F. Delfino, R. Procopio, M. Rossi, and F. Rachidi, “Evaluation of power system lightning performance, part i: Model and numerical solution using the pscad-emtdc platform,” IEEE Trans. Electromagn. Compat., vol. 59, no. 1, pp. 137–145, Feb 2017.
[8] D. Bica, C. Moldovan, and M. Muji, “Power engineering education using neplan software,” in 2008 43rd International Universities Power Engineering Conference, Sept 2008, pp. 1–3.
[9] V. F. Pires and J. F. A. Silva, “Teaching nonlinear modeling, simulation, and control of electronic power converters using matlab/simulink,” IEEE Trans. Educ., vol. 45, no. 3, pp. 253–261, Aug 2002.
[10] F. Benhamida, I. Ziane, S. Souag, Y. Salhi, and B. Dehiba, “A quadratic programming optimization for dynamic economic load dispatch: Comparison with GAMS,” in 3rd International Conference on Systems and Control, Oct 2013, pp. 625–630.
[11] L. Tartibu, B. Sun, and M. Kaunda, “Multi-objective optimization of the stack of a thermoacoustic engine using GAMS,” Appl. Soft Comput., vol. 28, pp. 30 – 43, 2015.
[12] M. K. Amosa and T. Majozi, “GAMS supported optimization and predictability study of a multi-objective adsorption process with conflicting regions of optimal operating conditions,” Comput. Chem. Eng., vol. 94, pp. 354 – 361, 2016.
[13] J. A. Gonzalez, “Probabilistic production costing modeled with ampl,” IEEE Trans. Power Syst., vol. 17, no. 2, pp. 277–282, May 2002.
[14] B. Liu and N. Jin, “An application of lingo software to solve dynamic programming problem in the field of environmental protection,” in 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Dec 2015, pp. 577–580.
[15] A. F. J. López, M. C. P. Pelayo, and . R. Forero, “Teaching image processing in engineering using python,” IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 11, no. 3, pp. 129–136, Aug 2016.
[16] W. Chuan, Y. Lei, and Z. Jianguo, “Study on optimization of radiological worker allocation problem based on nonlinear programming functionfmincon,” in 2014 IEEE International Conference on Mechatronics and Automation, Aug 2014, pp. 1073–1078.
[17] M. S. Castro, J. T. Saraiva, and J. C. Sousa, “Application of the matlab linprog function to plants short term operation of hydro stations considered as price makers,” in 2016 13th International Conference on the European Energy Market (EEM), June 2016, pp. 1–5.
[18] Y. Hua and R. V. Iyer, “On the solution of operator equation problems with application to preisach density estimation,” in 2016 American Control Conference (ACC), July 2016, pp. 2427–2432.
[19] S. Nieto and H. Ramos, “Use of a symbolic computation program to reinforce the spatial abilities of engineering students,” IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 12, no. 1, pp. 37–44, Feb 2017.
[20] J.Holvikivi, “Logicalreasoningabilityinengineeringstudents: Acasestudy,” IEEE Trans. Educ., vol. 50, no. 4, pp. 367–372, Nov 2007.
[21] G. Silva-Maceda, P. D. A.-V. na, and F. E. Castillo-Barrera, “More time or better tools? a large-scale retrospective comparison of pedagogical approaches to teach programming,” IEEE Trans. Educ., vol. 59, no. 4, pp. 274–281, Nov 2016.
[22] A. Gomes, F. B. Correia, and P. H. Abreu, “Types of assessing student programming knowledge,” in 2016 IEEE Frontiers in Education Conference (FIE), Oct 2016, pp. 1–8.
[23] GAMS Development Corp. GAMS free demo version. [Online]. Available: https://www.gams.com/download/
[24] M. Resnick and N. Rusk, “The computer clubhouse: Preparing for life in a digital world,” IBM Syst. J., vol. 35, no. 3.4, pp. 431–439, 1996.
[25] J. W. Cortada, How Computers Spread Around the World So Fast. Wiley-IEEE Press, 2009, pp. 27–70. [Online]. Available: http://ieeexplore. ieee.org/xpl/articleDetails.jsp?arnumber=6381994
[26] M. Wollschlaeger, T. Sauter, and J. Jasperneite, “The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0,” IEEE Ind. Electron. Mag., vol. 11, no. 1, pp. 17–27, March 2017.
[27] Y. Guo, H. Zhu, and L. Yang, “Smart service system(sss): A novel architecture enabling coordination of heterogeneous networking technologies and devices for internet of things,” China Commun, vol. 14, no. 3, pp. 130–144, March 2017.
[28] G. H. Gaynor, “Solving problems,” IEEE Eng. Manage. Rev., vol. 44, no. 4, pp. 6–8, Fourth 2016.
[29] P. S. Steif, L. Fu, and L. B. Kara, “Computer tutors can address students learning to solve complex engineering problems,” in 2014 IEEE Frontiers in Education Conference (FIE) Proceedings, Oct 2014, pp. 1–8.
[30] K. Yevseyeva and M. Towhidnejad, “Work in progress: Teaching computational thinking in middle and high school,” in 2012 Frontiers in Education Conference Proceedings, Oct 2012, pp. 1–2.
[31] P. Silapachote and A. Srisuphab, “Teaching and learning computational thinking through solving problems in artificial intelligence: On designing introductory engineering and computing courses,” in 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Dec 2016, pp. 50–54.
[32] C.EnriquezandO.Aguilar,“Using robot to motivate computational thinking in high school students,” IEEE Lat. Am. Trans., vol. 14, no. 11, pp. 4620– 4625, Nov 2016.
[33] E. Castillo, A. Conejo, P. Pedregal, R. García, and N. Alguacil, Building and Solving Mathematical Programming Models in Engineering and Science, ser. Pure and Applied Mathematics: A Wiley Series of Texts, Monographs and Tracts. Wiley, 2001.
[34] D. Kothari and J. Dhillon, Power System Optimization. Prentice-Hall of India, 2004.
[35] C. Shao, X. Wang, M. Shahidehpour, X. Wang, and B. Wang, “Power system economic dispatch considering steady-state secure region for wind power,” IEEE Trans. Sustainable Energy, vol. 8, no. 1, pp. 268–278, Jan 2017.
[36] A. K. Zadeh, K. M. Nor, and H. Zeynal, “Multi-thread security constraint economic dispatch with exact loss formulation,” in 2010 IEEE International Conference on Power and Energy, Nov 2010, pp. 864–869.
[37] A. Gholami, J. Ansari, M. Jamei, and A. Kazemi, “Environmental/economic dispatch incorporating renewable energy sources and plug-in vehicles,” IET Generation, Transmission Distribution, vol. 8, no. 12, pp. 2183–2198, 2014.
[38] W. Uturbey and A. S. Costa, “Dynamic optimal power flow approach to account for consumer response in short term hydrothermal coordination studies,” IET Generation, Transmission Distribution, vol. 1, no. 3, pp. 414–421, May 2007.
Article Sidebar
Publicado
nov 7, 2017
Article Details
Cómo citar
Montoya Giraldo, O. D. (2017). Solución de un problema de optimización clásico usando el paquete optimizador GAMS: implementación del problema de despacho económico. Ingeniería Y Ciencia, 13(26), 39–63. https://doi.org/10.17230/ingciencia.13.26.2
Número
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Artículos
Agencias de apoyo
Colciencias
Biografía del autor/a
Oscar Danilo Montoya Giraldo, Mag., Universidad Tecnológica de Pereira
Ingeniero electricista, Joven investigador de Colciencias, Profesor del programa de Ingeniería Eléctrica y estudiante de doctorado en Ingeniería.
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