Solving a Classical Optimization Problem Using GAMS Optimizer Package: Economic Dispatch Problem Implementation

Main Article Content

Oscar Danilo Montoya Giraldo, Mag.


Computational thinking, students experience., economic dispatch problem, mathematical modeling, GAMS optimization package, nonlinear optimization problem


This paper presents an effectiveness strategy to model and solve nonlinear mathematical optimization problems in electrical engineering using General Algebraic Modeling System (GAMS) for undergraduate students. A classical problem known as economic dispatch has been selected to show the need of using mathematical tools to solve a large scale optimization problem related with engineering. The economic dispatch is a classical optimization problem in operation of thermal electric systems, being the main idea to find an economical operation for thermal generators. This operation is based on a quadratic cost curve with some operating constraints, i.e., power balance and generation capacities. A numerical simulation is implemented using GAMS optimization package in demo version. This research has been developed with the support of 36 students of the course of Regulation and Operation of Electrical Systems (ROES) in the program
of Electrical Engineering at Universidad Tecnológica de Pereira (UTP).


Download data is not yet available.
Abstract 899 | PDF Downloads 668


[1] M. D. Shepherd and C. C. van de Sande, “Reading mathematics for understanding-from novice to expert,” The Journal of Mathematical Behavior, vol. 35, pp. 74 – 86, 2014.

[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:

[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.

[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.