Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems

Autores
Alvarez, Gonzalo Exequiel
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Climate change demands clean energy solutions, and renewable sources such as solar and wind are prime candidates. However, their variability poses challenges for their integration into large-scale power systems. This paper addresses this issue by proposing a novel hybrid mathematical model. The proposal integrates both fossil and renewable sources, considering real-world constraints such as system demand, reserves, and transmission dynamics. The model combines several approaches. By using a novel block composition technique, the computational complexity is reduced, making the model applicable to large-scale systems. A neural network is also developed to improve the forecasting of renewable energy production, which is crucial for managing its intermittency. The effectiveness of the proposed model is tested by considering the large Argentinean electricity system, demonstrating its practical applicability. The results show that acceptable forecasts can be obtained for the generation and transmission scheduling of the whole system.
Fil: Alvarez, Gonzalo Exequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Materia
Renewable energy integration
Large-scale power systems
Intermittency
Hybrid modeling
Neural networks
Argentina Electric System
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/244559
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/244559
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systemsAlvarez, Gonzalo ExequielRenewable energy integrationLarge-scale power systemsIntermittencyHybrid modelingNeural networksArgentina Electric Systemhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Climate change demands clean energy solutions, and renewable sources such as solar and wind are prime candidates. However, their variability poses challenges for their integration into large-scale power systems. This paper addresses this issue by proposing a novel hybrid mathematical model. The proposal integrates both fossil and renewable sources, considering real-world constraints such as system demand, reserves, and transmission dynamics. The model combines several approaches. By using a novel block composition technique, the computational complexity is reduced, making the model applicable to large-scale systems. A neural network is also developed to improve the forecasting of renewable energy production, which is crucial for managing its intermittency. The effectiveness of the proposed model is tested by considering the large Argentinean electricity system, demonstrating its practical applicability. The results show that acceptable forecasts can be obtained for the generation and transmission scheduling of the whole system.Fil: Alvarez, Gonzalo Exequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaGrowing Science2024-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/244559Alvarez, Gonzalo Exequiel; Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems; Growing Science; Management Science Letters; 14; 4; 2-2024; 247-2641923-93351923-9343CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://growingscience.com/beta/msl/6797-hybrid-optimization-model-with-neural-network-approach-for-renewable-energy-prediction-and-scheduling-in-large-scale-systems.htmlinfo:eu-repo/semantics/altIdentifier/doi/10.5267/j.msl.2024.2.003info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:42:41Zoai:ri.conicet.gov.ar:11336/244559instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:42:41.244CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems
title Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems
spellingShingle Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems
Alvarez, Gonzalo Exequiel
Renewable energy integration
Large-scale power systems
Intermittency
Hybrid modeling
Neural networks
Argentina Electric System
title_short Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems
title_full Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems
title_fullStr Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems
title_full_unstemmed Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems
title_sort Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems
dc.creator.none.fl_str_mv Alvarez, Gonzalo Exequiel
author Alvarez, Gonzalo Exequiel
author_facet Alvarez, Gonzalo Exequiel
author_role author
dc.subject.none.fl_str_mv Renewable energy integration
Large-scale power systems
Intermittency
Hybrid modeling
Neural networks
Argentina Electric System
topic Renewable energy integration
Large-scale power systems
Intermittency
Hybrid modeling
Neural networks
Argentina Electric System
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Climate change demands clean energy solutions, and renewable sources such as solar and wind are prime candidates. However, their variability poses challenges for their integration into large-scale power systems. This paper addresses this issue by proposing a novel hybrid mathematical model. The proposal integrates both fossil and renewable sources, considering real-world constraints such as system demand, reserves, and transmission dynamics. The model combines several approaches. By using a novel block composition technique, the computational complexity is reduced, making the model applicable to large-scale systems. A neural network is also developed to improve the forecasting of renewable energy production, which is crucial for managing its intermittency. The effectiveness of the proposed model is tested by considering the large Argentinean electricity system, demonstrating its practical applicability. The results show that acceptable forecasts can be obtained for the generation and transmission scheduling of the whole system.
Fil: Alvarez, Gonzalo Exequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
description Climate change demands clean energy solutions, and renewable sources such as solar and wind are prime candidates. However, their variability poses challenges for their integration into large-scale power systems. This paper addresses this issue by proposing a novel hybrid mathematical model. The proposal integrates both fossil and renewable sources, considering real-world constraints such as system demand, reserves, and transmission dynamics. The model combines several approaches. By using a novel block composition technique, the computational complexity is reduced, making the model applicable to large-scale systems. A neural network is also developed to improve the forecasting of renewable energy production, which is crucial for managing its intermittency. The effectiveness of the proposed model is tested by considering the large Argentinean electricity system, demonstrating its practical applicability. The results show that acceptable forecasts can be obtained for the generation and transmission scheduling of the whole system.
publishDate 2024
dc.date.none.fl_str_mv 2024-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/244559
Alvarez, Gonzalo Exequiel; Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems; Growing Science; Management Science Letters; 14; 4; 2-2024; 247-264
1923-9335
1923-9343
CONICET Digital
CONICET
url http://hdl.handle.net/11336/244559
identifier_str_mv Alvarez, Gonzalo Exequiel; Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems; Growing Science; Management Science Letters; 14; 4; 2-2024; 247-264
1923-9335
1923-9343
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://growingscience.com/beta/msl/6797-hybrid-optimization-model-with-neural-network-approach-for-renewable-energy-prediction-and-scheduling-in-large-scale-systems.html
info:eu-repo/semantics/altIdentifier/doi/10.5267/j.msl.2024.2.003
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Growing Science
publisher.none.fl_str_mv Growing Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083534445346816
score 13.22299

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