A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generation

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The rapid growth of wind and solar generation has introduced significant variability and uncertainty into the operation of electric power systems (SEP), challenging traditional methods for sizing operating reserves. This article presents a dynamic probabilistic method based on the convolution of pro...

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Detalles Bibliográficos
Autor: Aracayo, Javier
Formato: artículo
Fecha de Publicación:2025
Institución:Universidad de San Martín de Porres
Repositorio:Revistas - Universidad de San Martín de Porres
Lenguaje:español
OAI Identifier:oai:revistas.usmp.edu.pe:article/3161
Enlace del recurso:https://portalrevistas.aulavirtualusmp.pe/index.php/rc/article/view/3161
Nivel de acceso:acceso abierto
Materia:Wind and Solar Generation
Dynamic Probabilistic Method
Convolution
Secondary Frequency Regulation
Electric Power System
Demand
Generación Eólica y Solar
Método Probabilístico Dinámico
Convolución
Regulación Secundaria de Frecuencia
Sistema Eléctrico de Potencia
Demanda
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spelling A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generationUn método probabilístico dinámico para determinarla reserva para regulación secundaria de frecuencia ensistemas eléctricos de potencia con alta integraciónde generación eólica y solarAracayo, JavierWind and Solar GenerationDynamic Probabilistic MethodConvolutionSecondary Frequency RegulationElectric Power SystemDemandGeneración Eólica y SolarMétodo Probabilístico DinámicoConvoluciónRegulación Secundaria de FrecuenciaSistema Eléctrico de PotenciaDemandaThe rapid growth of wind and solar generation has introduced significant variability and uncertainty into the operation of electric power systems (SEP), challenging traditional methods for sizing operating reserves. This article presents a dynamic probabilistic method based on the convolution of probability distribution functions, aimed at accurately estimating the reserve required for Secondary Frequency Regulation (RSF) in contexts with high penetration of intermittent renewable sources. The proposed approach employs historical time series data with halfhour resolution for electricity demand, wind generation, and solar generation, applying a discrete convolution technique to combine their respectiveprobability distributions. Unlike conventional methods, this methodology allows for differentiated reserve estimation according to the hour of the dayand the type of day (weekday or non-weekday), enhancing the precision and operational efficiency of the system. The validity of the method was assessed using data from the Peruvian power system, demonstrating its scalability and adaptability to other systems with increasing renewable energy participation.The results show significant improvements in the sizing and allocation of SFR, contributing to a more secure, efficient, and reliable operation of modernpower systems.El crecimiento acelerado de la generación eólica y solar ha introducido una considerable variabilidad e incertidumbre en la operación de los sistemas eléctricos de potencia (SEP), desafiando los métodos tradicionales de dimensionamiento de reservas operativas. Este artículo presenta un método probabilístico dinámico basado en la convolución de funciones de distribución de probabilidad, orientado a estimar con mayor precisión la reserva requerida para la Regulación Secundaria de Frecuencia (RSF) en contextoscon alta penetración de fuentes renovables intermitentes. El enfoque propuesto emplea series históricas con resolución media horaria de demanda eléctrica, generación eólica y solar, aplicando una técnica de convolución discreta para combinar sus distribuciones de probabilidad respectivas.A diferencia de métodos convencionales, esta metodología permite estimar reservas diferenciadas según la hora del día y el tipo de día (laborable o no laborable), mejorando la precisión y eficiencia del sistema. La validez del método fue evaluada utilizando datos del sistema eléctrico peruano, demostrando su escalabilidad y adaptabilidad a otroscontextos. Los resultados evidencian mejoras significativas en el dimensionamiento y distribución de la RSF, lo que contribuye a una operación segura, eficiente y confiable de los SEP modernos.Universidad de San Martín de Porres2025-08-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://portalrevistas.aulavirtualusmp.pe/index.php/rc/article/view/3161Campus; Vol. 30 No. 39 (2025): Campus XXXIXCampus; Vol. 30 Núm. 39 (2025): Campus XXXIXCampus; v. 30 n. 39 (2025): Campus XXXIX2523-18201812-6049reponame:Revistas - Universidad de San Martín de Porresinstname:Universidad de San Martín de Porresinstacron:USMPspahttps://portalrevistas.aulavirtualusmp.pe/index.php/rc/article/view/3161/3996Derechos de autor 2025 Javier Aracayohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessoai:revistas.usmp.edu.pe:article/31612025-08-25T14:21:35Z
dc.title.none.fl_str_mv A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generation
Un método probabilístico dinámico para determinarla reserva para regulación secundaria de frecuencia ensistemas eléctricos de potencia con alta integraciónde generación eólica y solar
title A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generation
spellingShingle A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generation
Aracayo, Javier
Wind and Solar Generation
Dynamic Probabilistic Method
Convolution
Secondary Frequency Regulation
Electric Power System
Demand
Generación Eólica y Solar
Método Probabilístico Dinámico
Convolución
Regulación Secundaria de Frecuencia
Sistema Eléctrico de Potencia
Demanda
title_short A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generation
title_full A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generation
title_fullStr A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generation
title_full_unstemmed A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generation
title_sort A dynamic probabilistic method to determine the reserve forsecondary frequency regulation in electric power systems with highintegration of wind and solar generation
dc.creator.none.fl_str_mv Aracayo, Javier
author Aracayo, Javier
author_facet Aracayo, Javier
author_role author
dc.subject.none.fl_str_mv Wind and Solar Generation
Dynamic Probabilistic Method
Convolution
Secondary Frequency Regulation
Electric Power System
Demand
Generación Eólica y Solar
Método Probabilístico Dinámico
Convolución
Regulación Secundaria de Frecuencia
Sistema Eléctrico de Potencia
Demanda
topic Wind and Solar Generation
Dynamic Probabilistic Method
Convolution
Secondary Frequency Regulation
Electric Power System
Demand
Generación Eólica y Solar
Método Probabilístico Dinámico
Convolución
Regulación Secundaria de Frecuencia
Sistema Eléctrico de Potencia
Demanda
description The rapid growth of wind and solar generation has introduced significant variability and uncertainty into the operation of electric power systems (SEP), challenging traditional methods for sizing operating reserves. This article presents a dynamic probabilistic method based on the convolution of probability distribution functions, aimed at accurately estimating the reserve required for Secondary Frequency Regulation (RSF) in contexts with high penetration of intermittent renewable sources. The proposed approach employs historical time series data with halfhour resolution for electricity demand, wind generation, and solar generation, applying a discrete convolution technique to combine their respectiveprobability distributions. Unlike conventional methods, this methodology allows for differentiated reserve estimation according to the hour of the dayand the type of day (weekday or non-weekday), enhancing the precision and operational efficiency of the system. The validity of the method was assessed using data from the Peruvian power system, demonstrating its scalability and adaptability to other systems with increasing renewable energy participation.The results show significant improvements in the sizing and allocation of SFR, contributing to a more secure, efficient, and reliable operation of modernpower systems.
publishDate 2025
dc.date.none.fl_str_mv 2025-08-22
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://portalrevistas.aulavirtualusmp.pe/index.php/rc/article/view/3161
url https://portalrevistas.aulavirtualusmp.pe/index.php/rc/article/view/3161
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://portalrevistas.aulavirtualusmp.pe/index.php/rc/article/view/3161/3996
dc.rights.none.fl_str_mv Derechos de autor 2025 Javier Aracayo
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Derechos de autor 2025 Javier Aracayo
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad de San Martín de Porres
publisher.none.fl_str_mv Universidad de San Martín de Porres
dc.source.none.fl_str_mv Campus; Vol. 30 No. 39 (2025): Campus XXXIX
Campus; Vol. 30 Núm. 39 (2025): Campus XXXIX
Campus; v. 30 n. 39 (2025): Campus XXXIX
2523-1820
1812-6049
reponame:Revistas - Universidad de San Martín de Porres
instname:Universidad de San Martín de Porres
instacron:USMP
instname_str Universidad de San Martín de Porres
instacron_str USMP
institution USMP
reponame_str Revistas - Universidad de San Martín de Porres
collection Revistas - Universidad de San Martín de Porres
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 13.014218
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