Optimal power flow application for active distribution networks using ant colony system algorithm
This study presents an optimal power flow algorithm based on the ant colony optimization metaheuristic (ACO) for network loss minimization in active distribution networks. The algorithm based on the coupling between an estimated power flow and construction a graph which represents a finite set of di...
- Autores:
-
Tapia Arce, Hugo Alexander
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2017
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/39935
- Acceso en línea:
- http://hdl.handle.net/1992/39935
- Palabra clave:
- Sistemas de energía eléctrica
Optimización matemática
Algoritmo hormiga
Algoritmos inspirados en la naturaleza
Ingeniería
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
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dc.title.es_CO.fl_str_mv |
Optimal power flow application for active distribution networks using ant colony system algorithm |
title |
Optimal power flow application for active distribution networks using ant colony system algorithm |
spellingShingle |
Optimal power flow application for active distribution networks using ant colony system algorithm Sistemas de energía eléctrica Optimización matemática Algoritmo hormiga Algoritmos inspirados en la naturaleza Ingeniería |
title_short |
Optimal power flow application for active distribution networks using ant colony system algorithm |
title_full |
Optimal power flow application for active distribution networks using ant colony system algorithm |
title_fullStr |
Optimal power flow application for active distribution networks using ant colony system algorithm |
title_full_unstemmed |
Optimal power flow application for active distribution networks using ant colony system algorithm |
title_sort |
Optimal power flow application for active distribution networks using ant colony system algorithm |
dc.creator.fl_str_mv |
Tapia Arce, Hugo Alexander |
dc.contributor.advisor.none.fl_str_mv |
Oliveira de Jesus, Paulo Manuel de |
dc.contributor.author.none.fl_str_mv |
Tapia Arce, Hugo Alexander |
dc.contributor.jury.none.fl_str_mv |
Ríos Mesías, Mario Alberto |
dc.subject.keyword.es_CO.fl_str_mv |
Sistemas de energía eléctrica Optimización matemática Algoritmo hormiga Algoritmos inspirados en la naturaleza |
topic |
Sistemas de energía eléctrica Optimización matemática Algoritmo hormiga Algoritmos inspirados en la naturaleza Ingeniería |
dc.subject.themes.none.fl_str_mv |
Ingeniería |
description |
This study presents an optimal power flow algorithm based on the ant colony optimization metaheuristic (ACO) for network loss minimization in active distribution networks. The algorithm based on the coupling between an estimated power flow and construction a graph which represents a finite set of discrete states in which the algorithm looks for the best solution for the objective function of the system. The control variables of the optimization problem comprise all transformer taps, and reactive load compensation system. The compensation system represents the addition of reactive power injections associated to devices able to provide volt/var support such as variable capacitors, distributed generation and electronic loads as electrical vehicles. These control variables are modeled with a set of discrete states that are modified according to the objective function evaluated, in this case network losses. Nodal voltages and angles in all system buses correspond to the state variables of the optimization problem. The proposed application has been successfully tested in the 4-bus and 13-bus IEEE test cases and results were validated using a standard Newton-based Optimal Power Flow. As an additional contribution the proposed Ant Colony-based OPF has been applied under an multiobjective approach considering also the investment cost of the reactive support |
publishDate |
2017 |
dc.date.issued.none.fl_str_mv |
2017 |
dc.date.accessioned.none.fl_str_mv |
2020-06-10T16:40:07Z |
dc.date.available.none.fl_str_mv |
2020-06-10T16:40:07Z |
dc.type.spa.fl_str_mv |
Trabajo de grado - Pregrado |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
dc.type.content.spa.fl_str_mv |
Text |
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http://purl.org/redcol/resource_type/TP |
format |
http://purl.org/coar/resource_type/c_7a1f |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/1992/39935 |
dc.identifier.pdf.none.fl_str_mv |
u807449.pdf |
dc.identifier.instname.spa.fl_str_mv |
instname:Universidad de los Andes |
dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional Séneca |
dc.identifier.repourl.spa.fl_str_mv |
repourl:https://repositorio.uniandes.edu.co/ |
url |
http://hdl.handle.net/1992/39935 |
identifier_str_mv |
u807449.pdf instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
dc.language.iso.es_CO.fl_str_mv |
eng |
language |
eng |
dc.rights.uri.*.fl_str_mv |
https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.extent.es_CO.fl_str_mv |
32 hojas |
dc.format.mimetype.es_CO.fl_str_mv |
application/pdf |
dc.publisher.es_CO.fl_str_mv |
Universidad de los Andes |
dc.publisher.program.es_CO.fl_str_mv |
Ingeniería Eléctrica |
dc.publisher.faculty.es_CO.fl_str_mv |
Facultad de Ingeniería |
dc.publisher.department.es_CO.fl_str_mv |
Departamento de Ingeniería Eléctrica y Electrónica |
dc.source.es_CO.fl_str_mv |
instname:Universidad de los Andes reponame:Repositorio Institucional Séneca |
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Universidad de los Andes |
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Universidad de los Andes |
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Repositorio Institucional Séneca |
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Repositorio Institucional Séneca |
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Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Oliveira de Jesus, Paulo Manuel de8779285d-6641-4859-abc5-dadc52816dbb600Tapia Arce, Hugo Alexanderf1e9bbe6-f926-43b2-9d69-d6731eefb643600Ríos Mesías, Mario Alberto2020-06-10T16:40:07Z2020-06-10T16:40:07Z2017http://hdl.handle.net/1992/39935u807449.pdfinstname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/This study presents an optimal power flow algorithm based on the ant colony optimization metaheuristic (ACO) for network loss minimization in active distribution networks. The algorithm based on the coupling between an estimated power flow and construction a graph which represents a finite set of discrete states in which the algorithm looks for the best solution for the objective function of the system. The control variables of the optimization problem comprise all transformer taps, and reactive load compensation system. The compensation system represents the addition of reactive power injections associated to devices able to provide volt/var support such as variable capacitors, distributed generation and electronic loads as electrical vehicles. These control variables are modeled with a set of discrete states that are modified according to the objective function evaluated, in this case network losses. Nodal voltages and angles in all system buses correspond to the state variables of the optimization problem. The proposed application has been successfully tested in the 4-bus and 13-bus IEEE test cases and results were validated using a standard Newton-based Optimal Power Flow. As an additional contribution the proposed Ant Colony-based OPF has been applied under an multiobjective approach considering also the investment cost of the reactive supportEn el documento se desarrolla la implementación de un algoritmo de flujo de carga optimo basado en la metaheurística de optimización de colonia de hormigas (ACO). El algoritmo funciona a manera de acople entre un sistema estimado mediante flujo de carga de barrido y la construcción de un grafo a partir de los elementos con variaciones discretas de generación, carga activa o reactiva y tensión; grafo en el cual se desenvuelve la metaheurística. El algoritmo que se plantea funciona en distintas redes de distribución, especialmente en los casos de estudio IEEE feeder de 4 y 13 nodos, a partir de sus datos de generación, carga, voltajes e impedancias. Adicionalmente, dispone del control de elementos como taps de transformadores, compensación reactivaes y demás herramientas con variaciones discretas de carga y las modifica acorde a la función objetivo evaluada, para el caso se busca la reducción de pérdidas activas del sistema similares con respecto a los valores encontrados en un flujo de potencia optimo resuelto con métodos tipo newton. A su vez, se hace un estudio de validación y una evaluación para aproximar el sistema diseñado al caso de múltiples objetivos. el algoritmo resulta una opción viable para estimar los parámetros de transformadores, compensación reactivaes y generadores que reducen la energía perdida en el sistema, y como extensión puede acoplarse a otras funciones objetivo características del OPF ya sea individualmente, o en un problema multiobjetvo con estas funciones.Ingeniero EléctricoPregrado32 hojasapplication/pdfengUniversidad de los AndesIngeniería EléctricaFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y Electrónicainstname:Universidad de los Andesreponame:Repositorio Institucional SénecaOptimal power flow application for active distribution networks using ant colony system algorithmTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TPSistemas de energía eléctricaOptimización matemáticaAlgoritmo hormigaAlgoritmos inspirados en la naturalezaIngenieríaPublicationTEXTu807449.pdf.txtu807449.pdf.txtExtracted texttext/plain56482https://repositorio.uniandes.edu.co/bitstreams/ae03f5ce-70c8-4c52-86f0-51d1c770b9fc/download06a6d33aa08bda238824cfb8a5cdda1dMD54THUMBNAILu807449.pdf.jpgu807449.pdf.jpgIM Thumbnailimage/jpeg10340https://repositorio.uniandes.edu.co/bitstreams/7316adb6-5338-4392-90c3-61de6b491ed3/download684872e9a8f184c8306b7746bc94093aMD55ORIGINALu807449.pdfapplication/pdf848165https://repositorio.uniandes.edu.co/bitstreams/db8e2f0f-2fae-4f50-b9da-f8c0df363f0d/downloadc52ed95695d6d558ac4cab42c3acabccMD511992/39935oai:repositorio.uniandes.edu.co:1992/399352023-10-10 16:25:20.245https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfopen.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.co |