Despacho económico en centrales de generación térmicas considerando restricciones económicas y ambientales para la operación en isla de una red eléctrica industrial / Despacho econômico em usinas térmicas considerando restrições econômicas e ambientais para a operação insular de uma rede elétrica industrial

Fausto Ruiz-Tipán, Alex Valenzuela

Abstract


El despacho económico ambiental (DEA) es un trabajo de optimización sumamente relevante en la operación de una planta de energía que opera con combustibles fósiles, el primordial objetivo es que el costo de combustible y el nivel de emisiones se optimicen mientras se satisfacen todas las restricciones operativas del sistema. Este problema de optimización es multiobjetivo, donde se deben tomar en consideración varias restricciones. En el presente documento, se ha propuesto el algoritmo de la libélula (AL) para resolver el problema de (DEA). El algoritmo se ha validado en dos sistemas de prueba, uno de diez unidades generadoras y el otro de un sistema aislado de quince generadores (red industrial), además, los resultados han sido comparados con otros algoritmos, algoritmo genético (AG) y optimización por enjambre de partículas (PSO), así, se ha determinado la eficacia del AL para sistemas eléctricos de potencia.


Keywords


despacho económico ambiental, optimización multiobjetivo, algoritmo de la libélula, red industrial.

References


S. Sharifi, M. Sedaghat, P. Farhadi, N. Ghadimi, and B. Taheri, “Environmental economic dispatch using improved artificial bee colony algorithm,” Evolving Systems, vol. 8, no. 3, pp. 233–242, 2017.

F. Ruiz-Tipán and A. Valenzuela, “Literary review of economic environmental dispatch considering bibliometric analysis,” Iteckne, 2021.

S. Hemamalini and S. P. Simon, “Maclaurin series-based Lagrangian method for economic dispatch with valve-point effect,” IET Generation, Transmission and Distribution, vol. 3, no. 9, pp. 859–871, 2009.

D. Dike, “Economic Dispatch of Generated Power Using Modified LambdaIteration Method,” IOSR Journal of Electrical and Electronics Engineering, vol. 7, no. 1, pp. 49–54, 2013.

C. Li, X. Yu, and W. Yu, “Optimal economic dispatch by fast distributed gradient,” 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, vol. 2014, no. December, pp. 571– 576, 2014.

J. H. Talaq, F. Ferial, and M. E. El-Hawary, “A Summary of Environmental/Economic Dispatch Algorithms,” IEEE Transactions on Power Systems, vol. 9, no. 3, pp. 1508–1516, 1994.

M. A. Abido, “Multiobjective particle swarm optimization for environmental/economic dispatch problem,” Electric Power Systems Research, vol. 79, no. 7, pp. 1105–1113, 2009.

M. Abido, “A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch,” International Journal of Electrical Power and Energy Systems, vol. 25, no. 2, pp. 97–105, 2003.

K. Xu, J. Zhou, Y. Zhang, and R. Gu, “Differential evolution based on ϵ-domination and orthogonal design method for power environmentallyfriendly dispatch,” Expert Systems with Applications, vol. 39, no. 4, pp. 3956–3963, 2012.

T. Jayabarathi, K. Jayaprakash, D. N. Jeyakumar, and T. Raghunathan, “Evolutionary programming techniques for different kinds of economic dispatch problems,” Electric Power Systems Research, vol. 73, no. 2, pp. 169–176, 2005.

B. Xing, Intelligent Systems Reference Library 62 Innovative Computational Intelligence : A Rough Guide to 134 Clever Algorithms.

K. K. Vishwakarma, H. M. Dubey, M. Pandit, and B. Panigrahi, “Simulated annealing approach for solving economic load dispatch problems with valve point loading effects,” International Journal of Engineering, Science and Technology, vol. 4, no. 4, pp. 60–72, 2018.

S. Pothiya, I. Ngamroo, and W. Kongprawechnon, “Ant colony optimisation for economic dispatch problem with non-smooth cost functions,” International Journal of Electrical Power and Energy Systems, vol. 32, no. 5, pp. 478–487, 2010. [Online]. Available: http://dx.doi.org/10.1016/j.ijepes.2009.09.016

T. Niknam, H. D. Mojarrad, H. Z. Meymand, and B. B. Firouzi, “A new honey bee mating optimization algorithm for non-smooth economic dispatch,” Energy, vol. 36, no. 2, pp. 896–908, 2011. [Online]. Available: http://dx.doi.org/10.1016/j.energy.2010.12.021

B. K. Panigrahi and V. Ravikumar Pandi, “Bacterial foraging optimisation: Nelder-Mead hybrid algorithm for economic load dispatch,” IET Generation, Transmission and Distribution, vol. 2, no. 4, pp. 556–565, 2008.

M. H. Sulaiman, M. W. Mustafa, Z. N. Zakaria, O. Aliman, and S. R. Abdul Rahim, “Firefly algorithm technique for solving economic dispatch problem,” 2012 IEEE International Power Engineering and Optimization Conference, PEOCO 2012 - Conference Proceedings, no. June, pp. 90–95, 2012.

A. H. Bindu and M. D. Reddy, “Economic Load Dispatch Using Cuckoo Search Algorithm,” International Journal of Engineering Research and Applications, vol. 3, no. 4, pp. 498–502, 2013.

B. Mallikarjuna, M. T. Student, K. H. Reddy, and O. Hemakesavulu, “Economic Load Dispatch Problem with Valve – Point Effect Using a Binary Bat Algorithm,” ACEEE Int. J. on Electrical and Power Engineering, vol. 4, no. 3, pp. 33–38, 2013.

T. Jayabarathi, T. Raghunathan, B. R. Adarsh, and P. N. Suganthan, “Economic dispatch using hybrid grey wolf optimizer,” Energy, vol. 111, pp. 630–641, 2016. [Online]. Available: http://dx.doi.org/10.1016/j.energy.2016.05.105

E. Sayedi, M. M. Farsangi, M. Barati, and K. Y. Lee, “A modified Shuffled frog leaping algorithm for nonconvex economic dispatch problem,” IEEE Power and Energy Society General Meeting, pp. 1–8, 2012.

A. Y. Abdelaziz, E. S. Ali, and S. M. Abd Elazim, “Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems,” Energy, vol. 101, pp. 506–518, 2016. [Online]. Available: http://dx.doi.org/10.1016/j.energy.2016.02.041

T. Niknam, F. Golestaneh, and M. S. Sadeghi, “ θ-Multiobjective teaching-learning-based optimization for dynamic economic emission dispatch,” IEEE Systems Journal, vol. 6, no. 2, pp. 341–352, 2012.

Z. Xin-gang, Z. Ze-qi, X. Yi-min, and M. Jin, “Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization,” Energy, vol. 195, p. 117014, 2020. [Online]. Available: https://doi.org/10.1016/j.energy.2020.117014

D. C. Walters and G. B. Sheble, “Genetic algorithm solution of economic dispatch with valve point loading,” IEEE Transactions on Power Systems, vol. 8, no. 3, pp. 1325–1332, 1993.

G. Aydin, “The development and validation of regression models to predict energy-related CO2 emissions in Turkey,” Energy Sources, Part B: Economics, Planning and Policy, vol. 10, no. 2, pp. 176–182, 2015.

P. Venkatesh, R. Gnanadass, and N. P. Padhy, “Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints,” IEEE Transactions on Power Systems, vol. 18, no. 2, pp. 688–697, 2003.

S. Mirjalili, “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Computing and Applications, vol. 27, no. 4, pp. 1053– 1073, 2016.

M. Wikelski, D. Moskowitz, J. S. Adelman, J. Cochran, D. S. Wilcove, and M. L. May, “Simple rules guide dragonfly migration,” Biology Letters, vol. 2, no. 3, pp. 325–329, 2006.

R. W. Russell, M. L. May, K. L. Soltesz, and J. W. Fitzpatrick, “Massive swarm migrations of dragonflies (Odonata) in eastern North America,” American Midland Naturalist, vol. 140, no. 2, pp. 325–342, 1998.




DOI: https://doi.org/10.34115/basrv6n3-002

Refbacks

  • There are currently no refbacks.