Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion

This paper shows that in order to solve a probabilistic load flow in radial distribution networks, it is necessary to apply effective techniques that take into account their technical constraints. Among these constraints, voltage regulation is one of the principal problems to be addressed in photovo...

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Detalles Bibliográficos
Autores: Ruiz Rodríguez, Francisco Javier, Hernández, J. C., Jurado, F.
Tipo de recurso: artículo
Fecha de publicación:2012
País:España
Institución:Universidad de Huelva (UHU)
Repositorio:Arias Montano. Repositorio Institucional de la Universidad de Huelva
Idioma:inglés
OAI Identifier:oai:ariasmontano.uhu.es:10272/24061
Acceso en línea:https://hdl.handle.net/10272/24061
Access Level:acceso abierto
Palabra clave:Shuffled frog-leaping algorithm
Monte Carlo method
Biomass
Gas engine
Probabilistic load flow
Three-phase load flow
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spelling Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansionRuiz Rodríguez, Francisco JavierHernández, J. C.Jurado, F.Shuffled frog-leaping algorithmMonte Carlo methodBiomassGas engineProbabilistic load flowThree-phase load flowThis paper shows that in order to solve a probabilistic load flow in radial distribution networks, it is necessary to apply effective techniques that take into account their technical constraints. Among these constraints, voltage regulation is one of the principal problems to be addressed in photovoltaic distributed generation. Probabilistic load flows can be solved by analytical techniques as well as the Monte Carlo method. Our research study applied an analytical method that combined the cumulant method with the Cornish-Fisher expansion to solve this problem. The Monte Carlo method is used to compare the results of analytical method proposed. To evaluate the performance of photovoltaic distributed generation, this paper describes a probabilistic model that takes into account the random nature of solar irradiance. Therefore, load and photovoltaic distributed generation are modelled as independent/dependent random variables. The results obtained show that the technique proposed gave a better performance than the Monte Carlo method. This technique provided satisfactory solutions with a smaller number of iterations. Therefore, convergence was rapidly attained and computational cost was lower than that required for the Monte Carlo method. Besides, the results revealed how the Cornish-Fisher expansion had a better performance than the Gram-Charlier expansion, when input random variables were non-Gaussian.Elsevier20122012-01-0120122012-01-01journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10272/24061reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelvainstname:Universidad de Huelva (UHU)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ariasmontano.uhu.es:10272/240612026-06-02T14:58:11Z
dc.title.none.fl_str_mv Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion
title Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion
spellingShingle Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion
Ruiz Rodríguez, Francisco Javier
Shuffled frog-leaping algorithm
Monte Carlo method
Biomass
Gas engine
Probabilistic load flow
Three-phase load flow
title_short Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion
title_full Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion
title_fullStr Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion
title_full_unstemmed Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion
title_sort Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion
dc.creator.none.fl_str_mv Ruiz Rodríguez, Francisco Javier
Hernández, J. C.
Jurado, F.
author Ruiz Rodríguez, Francisco Javier
author_facet Ruiz Rodríguez, Francisco Javier
Hernández, J. C.
Jurado, F.
author_role author
author2 Hernández, J. C.
Jurado, F.
author2_role author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Shuffled frog-leaping algorithm
Monte Carlo method
Biomass
Gas engine
Probabilistic load flow
Three-phase load flow
topic Shuffled frog-leaping algorithm
Monte Carlo method
Biomass
Gas engine
Probabilistic load flow
Three-phase load flow
description This paper shows that in order to solve a probabilistic load flow in radial distribution networks, it is necessary to apply effective techniques that take into account their technical constraints. Among these constraints, voltage regulation is one of the principal problems to be addressed in photovoltaic distributed generation. Probabilistic load flows can be solved by analytical techniques as well as the Monte Carlo method. Our research study applied an analytical method that combined the cumulant method with the Cornish-Fisher expansion to solve this problem. The Monte Carlo method is used to compare the results of analytical method proposed. To evaluate the performance of photovoltaic distributed generation, this paper describes a probabilistic model that takes into account the random nature of solar irradiance. Therefore, load and photovoltaic distributed generation are modelled as independent/dependent random variables. The results obtained show that the technique proposed gave a better performance than the Monte Carlo method. This technique provided satisfactory solutions with a smaller number of iterations. Therefore, convergence was rapidly attained and computational cost was lower than that required for the Monte Carlo method. Besides, the results revealed how the Cornish-Fisher expansion had a better performance than the Gram-Charlier expansion, when input random variables were non-Gaussian.
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-01-01
2012
2012-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10272/24061
url https://hdl.handle.net/10272/24061
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelva
instname:Universidad de Huelva (UHU)
instname_str Universidad de Huelva (UHU)
reponame_str Arias Montano. Repositorio Institucional de la Universidad de Huelva
collection Arias Montano. Repositorio Institucional de la Universidad de Huelva
repository.name.fl_str_mv
repository.mail.fl_str_mv
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