The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method

This paper is concerned with the economic generation dispatch problem. It is a well-known fact that practical aspects of power plant equipment, as well as the objectives to be met, may result in a nonconvex, nondifferentiable model that poses difficulties to conventional mathematical programming met...

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Autores:
Castro, Carlos
Silva, Fernanda L.
Tipo de recurso:
Article of journal
Fecha de publicación:
2023
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/13510
Acceso en línea:
https://hdl.handle.net/20.500.12585/13510
https://doi.org/10.32397/tesea.vol4.n1.510
Palabra clave:
Economic Dispatch Problem
Power Generation Optimization
Teaching-Learning-Based Optimization
Metaheuristic Algorithms
Nonconvex Model
Parameter-Free Algorithm
Power System Constraints
Power Systems Simulation
Rights
openAccess
License
Carlos Castro, Fernanda L. Silva - 2023
id UTB2_98995c47226fb4b6841b08cf8f9e9a87
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/13510
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method
dc.title.translated.spa.fl_str_mv The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method
title The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method
spellingShingle The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method
Economic Dispatch Problem
Power Generation Optimization
Teaching-Learning-Based Optimization
Metaheuristic Algorithms
Nonconvex Model
Parameter-Free Algorithm
Power System Constraints
Power Systems Simulation
title_short The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method
title_full The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method
title_fullStr The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method
title_full_unstemmed The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method
title_sort The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method
dc.creator.fl_str_mv Castro, Carlos
Silva, Fernanda L.
dc.contributor.author.eng.fl_str_mv Castro, Carlos
Silva, Fernanda L.
dc.subject.eng.fl_str_mv Economic Dispatch Problem
Power Generation Optimization
Teaching-Learning-Based Optimization
Metaheuristic Algorithms
Nonconvex Model
Parameter-Free Algorithm
Power System Constraints
Power Systems Simulation
topic Economic Dispatch Problem
Power Generation Optimization
Teaching-Learning-Based Optimization
Metaheuristic Algorithms
Nonconvex Model
Parameter-Free Algorithm
Power System Constraints
Power Systems Simulation
description This paper is concerned with the economic generation dispatch problem. It is a well-known fact that practical aspects of power plant equipment, as well as the objectives to be met, may result in a nonconvex, nondifferentiable model that poses difficulties to conventional mathematical programming methods. This paper proposes the use of metaheuristic Teaching-Learning-Based Optimization to overcome such difficulties. This metaheuristic is well known for requiring a few parameters and, most importantly, it does not require the tuning of problem-dependent parameters. The algorithm proposed in this work is parameter-free; that is, the few parameters required by the Teaching-Learning-Based Optimization method are set automatically based on the power system’s data. In addition, the handling of constraints, such as generators’ prohibited zones and the generator-load-loss power balance, is performed in a very efficient way. Simulation results are shown for power systems containing 3 to 40 generation units, and the results provided by the proposed method are shown and discussed based on comparisons with other metaheuristics and a mathematical programming technique.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-06-28 00:00:00
2025-05-21T19:15:46Z
dc.date.available.none.fl_str_mv 2023-06-28 00:00:00
dc.date.issued.none.fl_str_mv 2023-06-28
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.local.eng.fl_str_mv Journal article
dc.type.content.eng.fl_str_mv Text
dc.type.version.eng.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.eng.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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status_str publishedVersion
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/13510
dc.identifier.url.none.fl_str_mv https://doi.org/10.32397/tesea.vol4.n1.510
dc.identifier.doi.none.fl_str_mv 10.32397/tesea.vol4.n1.510
dc.identifier.eissn.none.fl_str_mv 2745-0120
url https://hdl.handle.net/20.500.12585/13510
https://doi.org/10.32397/tesea.vol4.n1.510
identifier_str_mv 10.32397/tesea.vol4.n1.510
2745-0120
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.references.eng.fl_str_mv A. J. Wood and B. F. Wollenberg. Power generation, operation, and control. John Wiley & Sons, 2nd edition, 1996. [2] Z. L. Gaing. Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Transactions on Power Systems, 18(3):1187–1197, 2003. [3] J. J. Grainger and W. D. Stevenson. Power system analysis. McGraw-Hill, 1994. [4] L. K. Kirchmayer. Economic operation of power systems. John Wiley & Sons, 1958. [5] H. R. E. H. Bouchekaraa, M. A. Abido, and M. Boucherma. Optimal power flow using teaching-learning-based optimization technique. Electric Power Systems Research, 114:49–59, 2014. [6] P. H. Chen and H. C. Chang. Large-scale economic dispatch by genetic algorithm. IEEE Transactions on Power Systems, 10(4):117–124, 1995. [7] D. C. Walters and G. B. Sheble. Genetic algorithm solution of economic dispatch with valve point loading. IEEE Transactions on Power Systems, 8(3):1325–1332, 1993. [8] H. V. Valluru, S. Khandavilli, N. V. S. K. C. Sela, P. R. Thota, and L. N. V. Muktevi. Modified tlbo technique for economic dispatch problem. In Second International Conference on Intelligent Computing and Control Systems (ICICCS), pages 1970–1973, 2018. [9] V. S. Aragón, S. C. Esquivel, and C. A. C. Coello. An immune algorithm with power redistribution for solving economic dispatch problem. Information Sciences, 295(C):609–632, 2015. [10] K. Kapelinski, J. O. Santos, G. Andrade, E. M. Santos, and J. P. Juchem Neto. Non-homogenous firefly algorithm for optimization: application to an economic load dispatch problem. In Brazilian Seminar on Electric Systems, 2018. [11] X. S. Yang. Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2(2):78–84, 2010. [12] R. V. Rao, V. J. Savsani, and D. P. Vakharia. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3):303–315, 2011. [13] R. Xue and Z. Wu. A survey of application and classification on teaching-learning-based optimization algorithm. IEEE Access, 8:1062–1079, 2019. [14] R. V. Rao and V. Patel. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4):535–560, 2012. [15] R. V. Rao and V. Patel. An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3):710–720, 2013. [16] R. Ghanizadeh, S. M. H. Kalali, and H. Farshi. Teaching-learning-based optimization for economic load dispatch. In 5th Conference on Knowledge-Based Engineering and Innovation, pages 851–856, 2019. [17] S. Sultana and P. K. Roy. Optimal capacitor placement in radial distribution systems using teaching learning based optimization. International Journal of Electrical Power and Energy Systems, 54:387–398, 2014. [18] D. K. Archana and K. V. Gupta. Optimal reconfiguration of primary power distribution system using modified teaching learning based optimization algorithm. In 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, pages 1444–1448, 2016. [19] US Department of Energy. 2011/2012 economic dispatch and technological change. Technical report, Report to Congress, 2012. [20] E. B. Elanchezhian, S. Subramanian, and S. Ganesan. Economic power dispatch with cubic cost models using teaching learning algorithm. IET Generation, Transmission, and Distribution, 8(7):1187–1202, 2014. [21] P. Kotecha. Computer aided applied single objective optimization, Accessed 2023. [22] E. Mezura-Montesa and C. A. C. Coello. Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm and Evolutionary Computation, 1(4):173–194, 2011. [23] K. Deb. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2-4):311–338, 2000. [24] GNU Octave. https://www.gnu.org/. [25] K. Zare and T. G. Bolandi. Modified iteration particle swarm optimization procedure for economic dispatch solving with non-smooth and non-convex fuel cost function. In 3rd IET International Conference on Clean Energy and Technology (CEAT), 2014. [26] U. A. Salaria, M. I. Menhas, and S. Manzoor. Quasi oppositional population based global particle swarm optimizer with inertial weights (qpgpso-w) for solving economic load dispatch problem. IEEE Access, 9:134081–134095, 2021. [27] S. Sahoo, K. M. Dash, R. C. Prusty, and A. K. Barisal. Comparative analysis of optimal load dispatch through evolutionary algorithms. Ain Shams Engineering Journal, 6(1):107–120, 2015.
dc.relation.ispartofjournal.eng.fl_str_mv Transactions on Energy Systems and Engineering Applications
dc.relation.citationvolume.eng.fl_str_mv 4
dc.relation.citationstartpage.none.fl_str_mv 35
dc.relation.citationendpage.none.fl_str_mv 55
dc.relation.bitstream.none.fl_str_mv https://revistas.utb.edu.co/tesea/article/download/510/376
dc.relation.citationedition.eng.fl_str_mv Núm. 1 , Año 2023 : Transactions on Energy Systems and Engineering Applications
dc.relation.citationissue.eng.fl_str_mv 1
dc.rights.eng.fl_str_mv Carlos Castro, Fernanda L. Silva - 2023
dc.rights.uri.eng.fl_str_mv https://creativecommons.org/licenses/by/4.0
dc.rights.accessrights.eng.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.creativecommons.eng.fl_str_mv This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.coar.eng.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Carlos Castro, Fernanda L. Silva - 2023
https://creativecommons.org/licenses/by/4.0
This work is licensed under a Creative Commons Attribution 4.0 International License.
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.eng.fl_str_mv application/pdf
dc.publisher.eng.fl_str_mv Universidad Tecnológica de Bolívar
dc.source.eng.fl_str_mv https://revistas.utb.edu.co/tesea/article/view/510
institution Universidad Tecnológica de Bolívar
repository.name.fl_str_mv Repositorio Digital Universidad Tecnológica de Bolívar
repository.mail.fl_str_mv bdigital@metabiblioteca.com
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spelling Castro, CarlosSilva, Fernanda L.2023-06-28 00:00:002025-05-21T19:15:46Z2023-06-28 00:00:002023-06-28https://hdl.handle.net/20.500.12585/13510https://doi.org/10.32397/tesea.vol4.n1.51010.32397/tesea.vol4.n1.5102745-0120This paper is concerned with the economic generation dispatch problem. It is a well-known fact that practical aspects of power plant equipment, as well as the objectives to be met, may result in a nonconvex, nondifferentiable model that poses difficulties to conventional mathematical programming methods. This paper proposes the use of metaheuristic Teaching-Learning-Based Optimization to overcome such difficulties. This metaheuristic is well known for requiring a few parameters and, most importantly, it does not require the tuning of problem-dependent parameters. The algorithm proposed in this work is parameter-free; that is, the few parameters required by the Teaching-Learning-Based Optimization method are set automatically based on the power system’s data. In addition, the handling of constraints, such as generators’ prohibited zones and the generator-load-loss power balance, is performed in a very efficient way. Simulation results are shown for power systems containing 3 to 40 generation units, and the results provided by the proposed method are shown and discussed based on comparisons with other metaheuristics and a mathematical programming technique.application/pdfengUniversidad Tecnológica de BolívarCarlos Castro, Fernanda L. Silva - 2023https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/510Economic Dispatch ProblemPower Generation OptimizationTeaching-Learning-Based OptimizationMetaheuristic AlgorithmsNonconvex ModelParameter-Free AlgorithmPower System ConstraintsPower Systems SimulationThe solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization methodThe solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization methodArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85A. J. Wood and B. F. Wollenberg. Power generation, operation, and control. John Wiley & Sons, 2nd edition, 1996. [2] Z. L. Gaing. Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Transactions on Power Systems, 18(3):1187–1197, 2003. [3] J. J. Grainger and W. D. Stevenson. Power system analysis. McGraw-Hill, 1994. [4] L. K. Kirchmayer. Economic operation of power systems. John Wiley & Sons, 1958. [5] H. R. E. H. Bouchekaraa, M. A. Abido, and M. Boucherma. Optimal power flow using teaching-learning-based optimization technique. Electric Power Systems Research, 114:49–59, 2014. [6] P. H. Chen and H. C. Chang. Large-scale economic dispatch by genetic algorithm. IEEE Transactions on Power Systems, 10(4):117–124, 1995. [7] D. C. Walters and G. B. Sheble. Genetic algorithm solution of economic dispatch with valve point loading. IEEE Transactions on Power Systems, 8(3):1325–1332, 1993. [8] H. V. Valluru, S. Khandavilli, N. V. S. K. C. Sela, P. R. Thota, and L. N. V. Muktevi. Modified tlbo technique for economic dispatch problem. In Second International Conference on Intelligent Computing and Control Systems (ICICCS), pages 1970–1973, 2018. [9] V. S. Aragón, S. C. Esquivel, and C. A. C. Coello. An immune algorithm with power redistribution for solving economic dispatch problem. Information Sciences, 295(C):609–632, 2015. [10] K. Kapelinski, J. O. Santos, G. Andrade, E. M. Santos, and J. P. Juchem Neto. Non-homogenous firefly algorithm for optimization: application to an economic load dispatch problem. In Brazilian Seminar on Electric Systems, 2018. [11] X. S. Yang. Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2(2):78–84, 2010. [12] R. V. Rao, V. J. Savsani, and D. P. Vakharia. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3):303–315, 2011. [13] R. Xue and Z. Wu. A survey of application and classification on teaching-learning-based optimization algorithm. IEEE Access, 8:1062–1079, 2019. [14] R. V. Rao and V. Patel. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4):535–560, 2012. [15] R. V. Rao and V. Patel. An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3):710–720, 2013. [16] R. Ghanizadeh, S. M. H. Kalali, and H. Farshi. Teaching-learning-based optimization for economic load dispatch. In 5th Conference on Knowledge-Based Engineering and Innovation, pages 851–856, 2019. [17] S. Sultana and P. K. Roy. Optimal capacitor placement in radial distribution systems using teaching learning based optimization. International Journal of Electrical Power and Energy Systems, 54:387–398, 2014. [18] D. K. Archana and K. V. Gupta. Optimal reconfiguration of primary power distribution system using modified teaching learning based optimization algorithm. In 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, pages 1444–1448, 2016. [19] US Department of Energy. 2011/2012 economic dispatch and technological change. Technical report, Report to Congress, 2012. [20] E. B. Elanchezhian, S. Subramanian, and S. Ganesan. Economic power dispatch with cubic cost models using teaching learning algorithm. IET Generation, Transmission, and Distribution, 8(7):1187–1202, 2014. [21] P. Kotecha. Computer aided applied single objective optimization, Accessed 2023. [22] E. Mezura-Montesa and C. A. C. Coello. Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm and Evolutionary Computation, 1(4):173–194, 2011. [23] K. Deb. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2-4):311–338, 2000. [24] GNU Octave. https://www.gnu.org/. [25] K. Zare and T. G. Bolandi. Modified iteration particle swarm optimization procedure for economic dispatch solving with non-smooth and non-convex fuel cost function. In 3rd IET International Conference on Clean Energy and Technology (CEAT), 2014. [26] U. A. Salaria, M. I. Menhas, and S. Manzoor. Quasi oppositional population based global particle swarm optimizer with inertial weights (qpgpso-w) for solving economic load dispatch problem. IEEE Access, 9:134081–134095, 2021. [27] S. Sahoo, K. M. Dash, R. C. Prusty, and A. K. Barisal. Comparative analysis of optimal load dispatch through evolutionary algorithms. Ain Shams Engineering Journal, 6(1):107–120, 2015.Transactions on Energy Systems and Engineering Applications43555https://revistas.utb.edu.co/tesea/article/download/510/376Núm. 1 , Año 2023 : Transactions on Energy Systems and Engineering Applications120.500.12585/13510oai:repositorio.utb.edu.co:20.500.12585/135102025-05-21 14:15:46.44https://creativecommons.org/licenses/by/4.0Carlos Castro, Fernanda L. Silva - 2023metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com

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