Optimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genético

En este proyecto se mostraron los resultados de la investigación obtenidos en la implementación de un algoritmo genético para obtener una óptima reconfiguración topológica de sistemas eléctricos de potencia. Se buscó reducir el deslastre de carga y las pérdidas económicas en situaciones de eventos d...

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Autores:
Diaz Vargas, Giovanny Andrés
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2023
Institución:
Universidad Distrital Francisco José de Caldas
Repositorio:
RIUD: repositorio U. Distrital
Idioma:
spa
OAI Identifier:
oai:repository.udistrital.edu.co:11349/39721
Acceso en línea:
http://hdl.handle.net/11349/39721
Palabra clave:
Optimización
Evento disruptivo
DER
Deslastre de carga
Algoritmo genético
Sistemas de potencia
Tecnología en Electricidad -- Tesis y disertaciones académicas
Energía eléctrica -- Energía
Líneas de transmisión -- Líneas eléctricas
Conductores eléctricos -- Electricidad
Genetic algorithm
Optimization
Disruptive event
DER (Distributed Energy Resources)
Load shedding
Power systems
Rights
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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repository_id_str
dc.title.spa.fl_str_mv Optimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genético
dc.title.titleenglish.spa.fl_str_mv Optimization of Electric Power Systems After Disruptive Events Through the Implementation of a Genetic Algorithm
title Optimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genético
spellingShingle Optimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genético
Optimización
Evento disruptivo
DER
Deslastre de carga
Algoritmo genético
Sistemas de potencia
Tecnología en Electricidad -- Tesis y disertaciones académicas
Energía eléctrica -- Energía
Líneas de transmisión -- Líneas eléctricas
Conductores eléctricos -- Electricidad
Genetic algorithm
Optimization
Disruptive event
DER (Distributed Energy Resources)
Load shedding
Power systems
title_short Optimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genético
title_full Optimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genético
title_fullStr Optimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genético
title_full_unstemmed Optimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genético
title_sort Optimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genético
dc.creator.fl_str_mv Diaz Vargas, Giovanny Andrés
dc.contributor.advisor.none.fl_str_mv Mosquera Palacios, Darin Jairo
dc.contributor.author.none.fl_str_mv Diaz Vargas, Giovanny Andrés
dc.contributor.orcid.none.fl_str_mv Mosquera Palacios, Darin Jairo [0000-0002-4526-2683]
dc.subject.spa.fl_str_mv Optimización
Evento disruptivo
DER
Deslastre de carga
Algoritmo genético
Sistemas de potencia
topic Optimización
Evento disruptivo
DER
Deslastre de carga
Algoritmo genético
Sistemas de potencia
Tecnología en Electricidad -- Tesis y disertaciones académicas
Energía eléctrica -- Energía
Líneas de transmisión -- Líneas eléctricas
Conductores eléctricos -- Electricidad
Genetic algorithm
Optimization
Disruptive event
DER (Distributed Energy Resources)
Load shedding
Power systems
dc.subject.lemb.none.fl_str_mv Tecnología en Electricidad -- Tesis y disertaciones académicas
Energía eléctrica -- Energía
Líneas de transmisión -- Líneas eléctricas
Conductores eléctricos -- Electricidad
dc.subject.keyword.spa.fl_str_mv Genetic algorithm
Optimization
Disruptive event
DER (Distributed Energy Resources)
Load shedding
Power systems
description En este proyecto se mostraron los resultados de la investigación obtenidos en la implementación de un algoritmo genético para obtener una óptima reconfiguración topológica de sistemas eléctricos de potencia. Se buscó reducir el deslastre de carga y las pérdidas económicas en situaciones de eventos disruptivos mediante la adición de líneas de transmisión y generación distribuida. para el análisis y el estudio de casos se realiza la simulación en los sistemas de prueba IEEE de 9 nodos y 30 nodos, Para ambos casos se estudió la solución del software en respuesta al evento disruptivo, Los resultados permitieron cuantificar los beneficios de la reconfiguración topológica propuesta por el algoritmo genético en términos de reducción del deslastre de carga y las pérdidas económicas.
publishDate 2023
dc.date.created.none.fl_str_mv 2023-10-25
dc.date.accessioned.none.fl_str_mv 2024-08-14T21:31:43Z
dc.date.available.none.fl_str_mv 2024-08-14T21:31:43Z
dc.type.spa.fl_str_mv bachelorThesis
dc.type.degree.spa.fl_str_mv Monografía
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
format http://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11349/39721
url http://hdl.handle.net/11349/39721
dc.language.iso.none.fl_str_mv spa
language spa
dc.relation.references.none.fl_str_mv Determinación del impacto de la generación distribuida en los sistemas de energía. i. sistemas de distribución radial. In 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).
Y. Abdel-Magid and M. Abido. Optimal multiobjective design of robust power system stabilizers using genetic algorithms. IEEE Transactions on power systems, 18(3):1125–1132, 2003.
Y. Abdel-Magid, M. Abido, S. Al-Baiyat, and A. Mantawy. Simultaneous stabilization of multimachine power systems via genetic algorithms. IEEE Transactions on Power Systems, 14(4):1428– 1439, 1999.
M. Arun and P. Aravindhababu. A new reconfiguration scheme for voltage stability enhancement of radial distribution systems. Energy Conversion and Management, 50:2148–2151, 9 2009.
A. Barbadilla. La evolución biológica. Departamento de genética y microbiología. Universidad Autónoma de Barcelona, 8193, 2013.
L. A. C. Durán. Resiliencia y vulnerabilidad de sistemas eléctricos. Encuentro Internacional de Educación en Ingeniería, 2021.
S. Gerbex, R. Cherkaoui, and A. J. Germond. Optimal location of multi-type facts devices in a power system by means of genetic algorithms. IEEE transactions on power systems, 16(3):537–544, 2001.
R. Ildarabadi, H. Lotfi, and M. E. Hajiabadi. Resilience enhancement of distribution grids based on the construction of tie-lines using a novel genetic algorithm. Energy Systems, pages 1–31, 2023.
K. Jasthi and D. Das. Simultaneous distribution system reconfiguration and dg sizing algorithm without load flow solution. IET Generation, Transmission Distribution, 12:1303–1313, 3 2018.
X. Ji, Q. Liu, Y. Yu, S. Fan, and N. Wu. Distribution network reconfiguration based on vector shift operation. IET Generation, Transmission and Distribution, 12:3339–3345, 7 2018.
O. Kahouli, H. Alsaif, Y. Bouteraa, N. Ben Ali, and M. Chaabene. Power system reconfiguration in distribution network for improving reliability using genetic algorithm and particle swarm optimization. Applied Sciences, 11(7):3092, 2021.
M. A. Kashem, V. Ganapathy, and G. B. Jasmon. A geometrical approach for network reconfiguration based loss minimization in distribution systems. International Journal of Electrical Power Energy Systems, 23:295–304, 5 2001.
H. M. Khodr, J. Martinez-Crespo, M. A. Matos, and J. Pereira. Distribution systems reconfiguration based on opf using benders decomposition. IEEE Transactions on Power Delivery, 24:2166–2176, 2009.
D. P. Kothari. Power system optimization. In 2012 2nd National conference on computational intelligence and signal processing (CISP), pages 18–21. IEEE, 2012.
F. Li, Y. Song, R. Morgan, and D. Cheng. Genetic algorithms in electric power system optimization”. In Proc Adaptive Computing in Engineering Design and Control, pages 77–83, 1994.
W.-M. Lin, F.-S. Cheng, and M.-T. Tsay. Distribution feeder reconfiguration with refined genetic algorithm. IEE Proceedings-Generation, Transmission and Distribution, 147(6):349–354, 2000.
M. Mahdavi, H. H. Alhelou, N. D. Hatziargyriou, and F. Jurado. Reconfiguration of electric power distribution systems: Comprehensive review and classification. IEEE Access, 9:118502–118527, 2021.
A. Merlin and H. Back. Search for a minimal-loss operating spanning tree configuration in an urban power distribution system. In Proc. 5th Power System Computation Conf., Cambridge, UK, pages 1–18, 1975.
C. A. Mora, O. D. Montoya, and E. R. Trujillo. Mixed-integer programming model for transmission network expansion planning with battery energy storage systems (bess). Energies, 13(17):4386, 2020.
S. Orero and M. Irving. A genetic algorithm for generator scheduling in power systems. International journal of electrical power & energy systems, 18(1):19–26, 1996.
P.-F. Pai and W.-C. Hong. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric Power Systems Research, 74(3):417–425, 2005.
] G. Pepermans, J. Driesen, D. Haeseldonckx, R. Belmans, and W. D’haeseleer. Distributed generation: definition, benefits and issues. Energy policy, 33(6):787–798, 2005.
G. K. Raju and P. R. Bijwe. An efficient algorithm for minimum loss reconfiguration of distribution system based on sensitivity and heuristics. IEEE Transactions on Power Systems, 23:1280–1287, 2008.
M. Reformat, E. Kuffel, D. Woodford, and W. Pedrycz. Application of genetic algorithms for control design in power systems. IEE Proceedings-Generation, Transmission and Distribution, 145(4):345–354, 1998.
R. Sarfi, M. Salama, and A. Chikhani. Distribution system reconfiguration for loss reduction: an algorithm based on network partitioning theory. In Proceedings of Power Industry Computer Applications Conference, pages 503–509. IEEE, 1995.
N. D. Sarma and K. S. P. Rao. A new 0–1 integer programming method of feeder reconfiguration for loss minimization in distribution systems. Electric Power Systems Research, 33:125–131, 5 1995.
H. P. Schmidt, N. Ida, N. Kagan, and J. C. Guaraldo. Fast reconfiguration of distribution systems considering loss minimization. IEEE Transactions on Power Systems, 20:1311–1319, 8 2005.
A. Sowa and J. Wiater. Overvoltages in low-voltage power distribution systems caused by direct lightning strokes to medium voltage lines. Technical University of Bialystok: Area published papers. http://teleinfo. pb. bialystok. pl/emc/index_ang_pliki P, page 7, 2003.
T. Tran The, D. Vo Ngoc, and N. Tran Anh. Distribution network reconfiguration for power loss reduction and voltage profile improvement using chaotic stochastic fractal search algorithm. Complexity, 2020:1–15, 2020.
F. Wen and Z. Han. Fault section estimation in power systems using a genetic algorithm. Electric Power Systems Research, 34(3):165–172, 1995.
J. Z. Zhu. Optimal reconfiguration of electrical distribution network using the refined genetic algorithm. Electric Power Systems Research, 62(1):37–42, 2002.
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spelling Mosquera Palacios, Darin JairoDiaz Vargas, Giovanny AndrésMosquera Palacios, Darin Jairo [0000-0002-4526-2683]2024-08-14T21:31:43Z2024-08-14T21:31:43Z2023-10-25http://hdl.handle.net/11349/39721En este proyecto se mostraron los resultados de la investigación obtenidos en la implementación de un algoritmo genético para obtener una óptima reconfiguración topológica de sistemas eléctricos de potencia. Se buscó reducir el deslastre de carga y las pérdidas económicas en situaciones de eventos disruptivos mediante la adición de líneas de transmisión y generación distribuida. para el análisis y el estudio de casos se realiza la simulación en los sistemas de prueba IEEE de 9 nodos y 30 nodos, Para ambos casos se estudió la solución del software en respuesta al evento disruptivo, Los resultados permitieron cuantificar los beneficios de la reconfiguración topológica propuesta por el algoritmo genético en términos de reducción del deslastre de carga y las pérdidas económicas.This project presents the research results obtained through the implementation of a genetic algorithm to achieve an optimal topological reconfiguration of electric power systems. The goal was to reduce load shedding and economic losses in disruptive event scenarios by adding transmission lines and distributed generation. For the analysis and case studies, simulations were conducted on the IEEE test systems with 9 and 30 nodes. In both cases, the software solution's performance in response to the disruptive event was studied. The results allowed for quantifying the benefits of the topological reconfiguration proposed by the genetic algorithm in terms of load shedding reduction and economic losses.pdfspaAttribution-NonCommercial-NoDerivatives 4.0 InternacionalAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2OptimizaciónEvento disruptivoDERDeslastre de cargaAlgoritmo genéticoSistemas de potenciaTecnología en Electricidad -- Tesis y disertaciones académicasEnergía eléctrica -- EnergíaLíneas de transmisión -- Líneas eléctricasConductores eléctricos -- ElectricidadGenetic algorithmOptimizationDisruptive eventDER (Distributed Energy Resources)Load sheddingPower systemsOptimización de sistemas eléctricos de potencia tras eventos disruptivos mediante la implementación de un algoritmo genéticoOptimization of Electric Power Systems After Disruptive Events Through the Implementation of a Genetic AlgorithmbachelorThesisMonografíainfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fDeterminación del impacto de la generación distribuida en los sistemas de energía. i. sistemas de distribución radial. In 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).Y. Abdel-Magid and M. Abido. Optimal multiobjective design of robust power system stabilizers using genetic algorithms. IEEE Transactions on power systems, 18(3):1125–1132, 2003.Y. Abdel-Magid, M. Abido, S. Al-Baiyat, and A. Mantawy. Simultaneous stabilization of multimachine power systems via genetic algorithms. IEEE Transactions on Power Systems, 14(4):1428– 1439, 1999.M. Arun and P. Aravindhababu. A new reconfiguration scheme for voltage stability enhancement of radial distribution systems. Energy Conversion and Management, 50:2148–2151, 9 2009.A. Barbadilla. La evolución biológica. Departamento de genética y microbiología. Universidad Autónoma de Barcelona, 8193, 2013.L. A. C. Durán. Resiliencia y vulnerabilidad de sistemas eléctricos. Encuentro Internacional de Educación en Ingeniería, 2021.S. Gerbex, R. Cherkaoui, and A. J. Germond. Optimal location of multi-type facts devices in a power system by means of genetic algorithms. IEEE transactions on power systems, 16(3):537–544, 2001.R. Ildarabadi, H. Lotfi, and M. E. Hajiabadi. Resilience enhancement of distribution grids based on the construction of tie-lines using a novel genetic algorithm. Energy Systems, pages 1–31, 2023.K. Jasthi and D. Das. Simultaneous distribution system reconfiguration and dg sizing algorithm without load flow solution. IET Generation, Transmission Distribution, 12:1303–1313, 3 2018.X. Ji, Q. Liu, Y. Yu, S. Fan, and N. Wu. Distribution network reconfiguration based on vector shift operation. IET Generation, Transmission and Distribution, 12:3339–3345, 7 2018.O. Kahouli, H. Alsaif, Y. Bouteraa, N. Ben Ali, and M. Chaabene. Power system reconfiguration in distribution network for improving reliability using genetic algorithm and particle swarm optimization. Applied Sciences, 11(7):3092, 2021.M. A. Kashem, V. Ganapathy, and G. B. Jasmon. A geometrical approach for network reconfiguration based loss minimization in distribution systems. International Journal of Electrical Power Energy Systems, 23:295–304, 5 2001.H. M. Khodr, J. Martinez-Crespo, M. A. Matos, and J. Pereira. Distribution systems reconfiguration based on opf using benders decomposition. IEEE Transactions on Power Delivery, 24:2166–2176, 2009.D. P. Kothari. Power system optimization. In 2012 2nd National conference on computational intelligence and signal processing (CISP), pages 18–21. IEEE, 2012.F. Li, Y. Song, R. Morgan, and D. Cheng. Genetic algorithms in electric power system optimization”. In Proc Adaptive Computing in Engineering Design and Control, pages 77–83, 1994.W.-M. Lin, F.-S. Cheng, and M.-T. Tsay. Distribution feeder reconfiguration with refined genetic algorithm. IEE Proceedings-Generation, Transmission and Distribution, 147(6):349–354, 2000.M. Mahdavi, H. H. Alhelou, N. D. Hatziargyriou, and F. Jurado. Reconfiguration of electric power distribution systems: Comprehensive review and classification. IEEE Access, 9:118502–118527, 2021.A. Merlin and H. Back. Search for a minimal-loss operating spanning tree configuration in an urban power distribution system. In Proc. 5th Power System Computation Conf., Cambridge, UK, pages 1–18, 1975.C. A. Mora, O. D. Montoya, and E. R. Trujillo. Mixed-integer programming model for transmission network expansion planning with battery energy storage systems (bess). Energies, 13(17):4386, 2020.S. Orero and M. Irving. A genetic algorithm for generator scheduling in power systems. International journal of electrical power & energy systems, 18(1):19–26, 1996.P.-F. Pai and W.-C. Hong. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric Power Systems Research, 74(3):417–425, 2005.] G. Pepermans, J. Driesen, D. Haeseldonckx, R. Belmans, and W. D’haeseleer. Distributed generation: definition, benefits and issues. Energy policy, 33(6):787–798, 2005.G. K. Raju and P. R. Bijwe. An efficient algorithm for minimum loss reconfiguration of distribution system based on sensitivity and heuristics. IEEE Transactions on Power Systems, 23:1280–1287, 2008.M. Reformat, E. Kuffel, D. Woodford, and W. Pedrycz. Application of genetic algorithms for control design in power systems. IEE Proceedings-Generation, Transmission and Distribution, 145(4):345–354, 1998.R. Sarfi, M. Salama, and A. Chikhani. Distribution system reconfiguration for loss reduction: an algorithm based on network partitioning theory. In Proceedings of Power Industry Computer Applications Conference, pages 503–509. IEEE, 1995.N. D. Sarma and K. S. P. Rao. A new 0–1 integer programming method of feeder reconfiguration for loss minimization in distribution systems. Electric Power Systems Research, 33:125–131, 5 1995.H. P. Schmidt, N. Ida, N. Kagan, and J. C. Guaraldo. Fast reconfiguration of distribution systems considering loss minimization. IEEE Transactions on Power Systems, 20:1311–1319, 8 2005.A. Sowa and J. Wiater. Overvoltages in low-voltage power distribution systems caused by direct lightning strokes to medium voltage lines. Technical University of Bialystok: Area published papers. http://teleinfo. pb. bialystok. pl/emc/index_ang_pliki P, page 7, 2003.T. Tran The, D. Vo Ngoc, and N. Tran Anh. Distribution network reconfiguration for power loss reduction and voltage profile improvement using chaotic stochastic fractal search algorithm. Complexity, 2020:1–15, 2020.F. Wen and Z. Han. Fault section estimation in power systems using a genetic algorithm. Electric Power Systems Research, 34(3):165–172, 1995.J. Z. Zhu. Optimal reconfiguration of electrical distribution network using the refined genetic algorithm. Electric Power Systems Research, 62(1):37–42, 2002.ORIGINALDiazVargasGiovannyAndres2023.pdfDiazVargasGiovannyAndres2023.pdfTrabajo de gradoapplication/pdf2384194https://repository.udistrital.edu.co/bitstreams/6201ba98-0b41-45d7-8eac-2c96cd794ed3/downloada46a36f0551fc1c57d0b0a0524fa6038MD51Licencia de uso y publicacion.pdfLicencia de uso y publicacion.pdfLicencia de uso y autorizaciónapplication/pdf368479https://repository.udistrital.edu.co/bitstreams/1d968c3a-5cfe-4bbb-baa8-bd2b6631ed68/download287ca169299709dc69a643ae1ba4011fMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-87167https://repository.udistrital.edu.co/bitstreams/51f53453-a4b8-4255-bf67-a78a0966460c/download997daf6c648c962d566d7b082dac908dMD56CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repository.udistrital.edu.co/bitstreams/e4710c8c-5b45-4549-9339-b73c14b9f44a/download4460e5956bc1d1639be9ae6146a50347MD5511349/39721oai:repository.udistrital.edu.co:11349/397212024-08-14 16:31:45.781http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalopen.accesshttps://repository.udistrital.edu.coRepositorio Universidad 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