Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning
En el presente artículo se muestra la evaluación de dos métodos de optimización heurística, denominados algoritmos genéticos AG y temperado simulado AS (Simulated Annealing), aplicados con el fin de encontrar la mejor solución con el menor costo en la planificación de la expansión de una red de tran...
- Autores:
-
Martínez Campo, Sergio Daniel
Burgos Rodríguez, Arthur José
Valdez Cervantes, Libis
Rodríguez Arias, Harold
- Tipo de recurso:
- Investigation report
- Fecha de publicación:
- 2022
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/47628
- Acceso en línea:
- http://dx.doi.org/10.18687/LACCEI2022.1.1.826
https://hdl.handle.net/20.500.12494/47628
- Palabra clave:
- AG genetic algorithms
Simulated Annealing AS
Heuristic Optimization
Planning
Network expansion
Electric power transmission
- Rights
- openAccess
- License
- NINGUNA
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Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning |
| title |
Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning |
| spellingShingle |
Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning AG genetic algorithms Simulated Annealing AS Heuristic Optimization Planning Network expansion Electric power transmission |
| title_short |
Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning |
| title_full |
Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning |
| title_fullStr |
Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning |
| title_full_unstemmed |
Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning |
| title_sort |
Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning |
| dc.creator.fl_str_mv |
Martínez Campo, Sergio Daniel Burgos Rodríguez, Arthur José Valdez Cervantes, Libis Rodríguez Arias, Harold |
| dc.contributor.author.none.fl_str_mv |
Martínez Campo, Sergio Daniel Burgos Rodríguez, Arthur José Valdez Cervantes, Libis Rodríguez Arias, Harold |
| dc.subject.spa.fl_str_mv |
AG genetic algorithms Simulated Annealing AS Heuristic Optimization Planning Network expansion Electric power transmission |
| topic |
AG genetic algorithms Simulated Annealing AS Heuristic Optimization Planning Network expansion Electric power transmission |
| description |
En el presente artículo se muestra la evaluación de dos métodos de optimización heurística, denominados algoritmos genéticos AG y temperado simulado AS (Simulated Annealing), aplicados con el fin de encontrar la mejor solución con el menor costo en la planificación de la expansión de una red de transmisión. de energía eléctrica, que además de atender la demanda esperada, considera una lista de alternativas candidatas con costo y capacidad de transporte conocidos. Con el desarrollo de los algoritmos AG y AS es posible garantizar la mejor solución de optimización, midiendo el costo computacional de los algoritmos. Se verificó que el método de optimización del Algoritmo Genético es capaz de encontrar la mejor solución óptima a un menor costo computacional, en comparación con el algoritmo de Quenching Simulado. |
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2022 |
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2022-12-21T11:49:12Z |
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2022-12-21T11:49:12Z |
| dc.date.issued.none.fl_str_mv |
2022-08-28 |
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Avance de investigación no financiada |
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http://purl.org/coar/resource_type/c_93fc |
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info:eu-repo/semantics/report |
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acceptedVersion |
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978-628-95207-0-5 |
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2414-6390 |
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http://dx.doi.org/10.18687/LACCEI2022.1.1.826 |
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https://hdl.handle.net/20.500.12494/47628 |
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Martínez Campo, S. D., Valdez Cervantes, L., Burgos Rodríguez, A. J., y Rodríguez Arias, H. (2022). Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning. "Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions", 1-8. http://dx.doi.org/10.18687/LACCEI2022.1.1.826 |
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978-628-95207-0-5 2414-6390 Martínez Campo, S. D., Valdez Cervantes, L., Burgos Rodríguez, A. J., y Rodríguez Arias, H. (2022). Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning. "Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions", 1-8. http://dx.doi.org/10.18687/LACCEI2022.1.1.826 |
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http://dx.doi.org/10.18687/LACCEI2022.1.1.826 https://hdl.handle.net/20.500.12494/47628 |
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https://laccei.org/LACCEI2022-BocaRaton/full_papers/FP826.pdf |
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Hybrid Event, Boca Raton, Florida- USA |
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20th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions” |
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Education, Research and Leadership in Post-Pandemic Engineering: Resilient Inclusive and Sustainable Actions |
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J. H. Holland, “Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence”. The University of Michigan Press, 1975. H.A.Romero, “Optimización de flujo de carga en los sitemas eléctricos de potencia utilizando algoritmos geneticos” Univerzidad de los Andes Venezuela, 2008. Algoritmos genéticos UFRJ. S.Haffner, Otimização Heurística-Recozimento simulado, UFRGS, 2020. Maneiro, N. (2001). Algoritmos genéticos aplicados a problemas de localización de facilidades. Ph.d. thesis, Universidad de Carabobo. DE OLIVEIRA, S.A.; DE ALMEIDA, C.R.T.; MONTICELLI, A. Times assíncronos aplicados a métodos heurísticos construtivos de planejamento da expansão da transmissão. In: CONGRESSO BRASILEIRO DE AUTOMATICA - CBA, 12., 1998, Uberlândia. Proceedins... Uberlândia: SBA/UFU, v.III, 1998. p.1029- 1034. Henderson, S. G., & Nelson, B. L. (2006). Stochastic computer simulation. Handbooks in operations research and management science: simulation, 1-18 DE OLIVEIRA, S.A.; DE ALMEIDA, C.R.T.; MONTICELLI, A. Time assíncrono inicializador de métodos combinatoriais para planejamento da expansão da transmissão. In: SEMINARIO NACIONAL DE PRODUC ´ ¸AO ˜ E TRANSMISSAO DE ENERGIA EL ˜ ETRICA - SNPTEE, 15., 1999, Foz do Iguaçu. Anais... Foz do Iguaçu: CIGRE - Itaipu Binacional, 1999. Grupo VII-GPL/04. |
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1-8 p. |
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2022-July |
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Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería Electrónica, Santa Marta Latin American and Caribbean Consortium of Engineering Institutions |
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Ingeniería Electrónica |
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Santa Marta |
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Universidad Cooperativa de Colombia |
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Martínez Campo, Sergio DanielBurgos Rodríguez, Arthur JoséValdez Cervantes, LibisRodríguez Arias, Harold2022-July2022-12-21T11:49:12Z2022-12-21T11:49:12Z2022-08-28978-628-95207-0-52414-6390http://dx.doi.org/10.18687/LACCEI2022.1.1.826https://hdl.handle.net/20.500.12494/47628Martínez Campo, S. D., Valdez Cervantes, L., Burgos Rodríguez, A. J., y Rodríguez Arias, H. (2022). Genetic Algorithm and Simulated Annealing in EE Transmission Expansion Planning. "Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions", 1-8. http://dx.doi.org/10.18687/LACCEI2022.1.1.826En el presente artículo se muestra la evaluación de dos métodos de optimización heurística, denominados algoritmos genéticos AG y temperado simulado AS (Simulated Annealing), aplicados con el fin de encontrar la mejor solución con el menor costo en la planificación de la expansión de una red de transmisión. de energía eléctrica, que además de atender la demanda esperada, considera una lista de alternativas candidatas con costo y capacidad de transporte conocidos. Con el desarrollo de los algoritmos AG y AS es posible garantizar la mejor solución de optimización, midiendo el costo computacional de los algoritmos. Se verificó que el método de optimización del Algoritmo Genético es capaz de encontrar la mejor solución óptima a un menor costo computacional, en comparación con el algoritmo de Quenching Simulado.In the present article, it shows the evaluation of two heuristic optimization methods, called genetic algorithms AG and simulated tempering AS (Simulated Annealing), applied to find the best solution with the lowest cost in planning the expansion of a network transmission system, which, in addition to meeting the expected demand, considers a list of candidate alternatives with known cost and transport capacity. With the development of the AG and AS algorithms, it is possible to guarantee the best optimization solution, measuring the computational cost of the algorithms. It was verified that the Genetic Algorithm optimization method can find the best optimal solution at a lower computational cost, compared to the Simulated Annealing algorithm. All results obtained in this work for the expansion of the system in an optimal way are satisfactory as they also meet all restrictions.https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001554748https://orcid.org/0000-0001-9537-0650sergio.martinezc@campusucc.edu.co1-8 p.Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería Electrónica, Santa MartaLatin American and Caribbean Consortium of Engineering InstitutionsIngeniería ElectrónicaSanta Martahttps://laccei.org/LACCEI2022-BocaRaton/full_papers/FP826.pdfHybrid Event, Boca Raton, Florida- USA20th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions”Education, Research and Leadership in Post-Pandemic Engineering: Resilient Inclusive and Sustainable ActionsJ. H. Holland, “Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence”. The University of Michigan Press, 1975.H.A.Romero, “Optimización de flujo de carga en los sitemas eléctricos de potencia utilizando algoritmos geneticos” Univerzidad de los Andes Venezuela, 2008.Algoritmos genéticos UFRJ.S.Haffner, Otimização Heurística-Recozimento simulado, UFRGS, 2020.Maneiro, N. (2001). Algoritmos genéticos aplicados a problemas de localización de facilidades. Ph.d. thesis, Universidad de Carabobo.DE OLIVEIRA, S.A.; DE ALMEIDA, C.R.T.; MONTICELLI, A. Times assíncronos aplicados a métodos heurísticos construtivos de planejamento da expansão da transmissão. In: CONGRESSO BRASILEIRO DE AUTOMATICA - CBA, 12., 1998, Uberlândia. Proceedins... Uberlândia: SBA/UFU, v.III, 1998. p.1029- 1034.Henderson, S. G., & Nelson, B. L. (2006). Stochastic computer simulation. Handbooks in operations research and management science: simulation, 1-18DE OLIVEIRA, S.A.; DE ALMEIDA, C.R.T.; MONTICELLI, A. Time assíncrono inicializador de métodos combinatoriais para planejamento da expansão da transmissão. In: SEMINARIO NACIONAL DE PRODUC ´ ¸AO ˜ E TRANSMISSAO DE ENERGIA EL ˜ ETRICA - SNPTEE, 15., 1999, Foz do Iguaçu. Anais... Foz do Iguaçu: CIGRE - Itaipu Binacional, 1999. Grupo VII-GPL/04.AG genetic algorithmsSimulated Annealing ASHeuristic OptimizationPlanningNetwork expansionElectric power transmissionGenetic Algorithm and Simulated Annealing in EE Transmission Expansion PlanningAvance de investigación no financiadahttp://purl.org/coar/resource_type/c_18wshttp://purl.org/coar/resource_type/c_93fcinfo:eu-repo/semantics/reportinfo:eu-repo/semantics/acceptedVersionNINGUNAinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repository.ucc.edu.co/bitstreams/47df53f6-351a-433a-88fb-16e4e4c22d7d/download8a4605be74aa9ea9d79846c1fba20a33MD5120.500.12494/47628oai:repository.ucc.edu.co:20.500.12494/476282024-08-10 21:03:23.753metadata.onlyhttps://repository.ucc.edu.coRepositorio Institucional Universidad Cooperativa de Colombiabdigital@metabiblioteca.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 |