Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models

Autores
Montoya, Oscar Danilo; Gil González, Walter; Grisales Norena, Luis; Orozco Henao, César; Serra, Federico Martin
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used.
Fil: Montoya, Oscar Danilo. Universidad Tecnologica de Bolivar; Colombia
Fil: Gil González, Walter. Universidad Tecnológica de Pereira; Colombia
Fil: Grisales Norena, Luis. Instituto Tecnológico Metropolitano; Colombia
Fil: Orozco Henao, César. Universidad del Norte; Colombia
Fil: Serra, Federico Martin. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias. Laboratorio de Control Automático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Investigaciones en Tecnología Química. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Investigaciones en Tecnología Química; Argentina
Materia
ARTIFICIAL NEURAL NETWORKS
BATTERY ENERGY STORAGE SYSTEM
ECONOMIC DISPATCH PROBLEM
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:11336/124816
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load ModelsMontoya, Oscar DaniloGil González, WalterGrisales Norena, LuisOrozco Henao, CésarSerra, Federico MartinARTIFICIAL NEURAL NETWORKSBATTERY ENERGY STORAGE SYSTEMECONOMIC DISPATCH PROBLEMhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used.Fil: Montoya, Oscar Danilo. Universidad Tecnologica de Bolivar; ColombiaFil: Gil González, Walter. Universidad Tecnológica de Pereira; ColombiaFil: Grisales Norena, Luis. Instituto Tecnológico Metropolitano; ColombiaFil: Orozco Henao, César. Universidad del Norte; ColombiaFil: Serra, Federico Martin. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias. Laboratorio de Control Automático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Investigaciones en Tecnología Química. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Investigaciones en Tecnología Química; ArgentinaMolecular Diversity Preservation International2019-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/124816Montoya, Oscar Danilo; Gil González, Walter; Grisales Norena, Luis; Orozco Henao, César; Serra, Federico Martin; Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models; Molecular Diversity Preservation International; Energies; 12; 23; 11-2019; 1-101996-1073CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1996-1073/12/23/4494/htminfo:eu-repo/semantics/altIdentifier/doi/10.3390/en12234494info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:08:31Zoai:ri.conicet.gov.ar:11336/124816instacron: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:34982025-09-29 10:08:32.11CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models
title Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models
spellingShingle Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models
Montoya, Oscar Danilo
ARTIFICIAL NEURAL NETWORKS
BATTERY ENERGY STORAGE SYSTEM
ECONOMIC DISPATCH PROBLEM
title_short Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models
title_full Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models
title_fullStr Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models
title_full_unstemmed Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models
title_sort Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models
dc.creator.none.fl_str_mv Montoya, Oscar Danilo
Gil González, Walter
Grisales Norena, Luis
Orozco Henao, César
Serra, Federico Martin
author Montoya, Oscar Danilo
author_facet Montoya, Oscar Danilo
Gil González, Walter
Grisales Norena, Luis
Orozco Henao, César
Serra, Federico Martin
author_role author
author2 Gil González, Walter
Grisales Norena, Luis
Orozco Henao, César
Serra, Federico Martin
author2_role author
author
author
author
dc.subject.none.fl_str_mv ARTIFICIAL NEURAL NETWORKS
BATTERY ENERGY STORAGE SYSTEM
ECONOMIC DISPATCH PROBLEM
topic ARTIFICIAL NEURAL NETWORKS
BATTERY ENERGY STORAGE SYSTEM
ECONOMIC DISPATCH PROBLEM
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used.
Fil: Montoya, Oscar Danilo. Universidad Tecnologica de Bolivar; Colombia
Fil: Gil González, Walter. Universidad Tecnológica de Pereira; Colombia
Fil: Grisales Norena, Luis. Instituto Tecnológico Metropolitano; Colombia
Fil: Orozco Henao, César. Universidad del Norte; Colombia
Fil: Serra, Federico Martin. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias. Laboratorio de Control Automático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Investigaciones en Tecnología Química. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Investigaciones en Tecnología Química; Argentina
description This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used.
publishDate 2019
dc.date.none.fl_str_mv 2019-11
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/11336/124816
Montoya, Oscar Danilo; Gil González, Walter; Grisales Norena, Luis; Orozco Henao, César; Serra, Federico Martin; Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models; Molecular Diversity Preservation International; Energies; 12; 23; 11-2019; 1-10
1996-1073
CONICET Digital
CONICET
url http://hdl.handle.net/11336/124816
identifier_str_mv Montoya, Oscar Danilo; Gil González, Walter; Grisales Norena, Luis; Orozco Henao, César; Serra, Federico Martin; Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models; Molecular Diversity Preservation International; Energies; 12; 23; 11-2019; 1-10
1996-1073
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://www.mdpi.com/1996-1073/12/23/4494/htm
info:eu-repo/semantics/altIdentifier/doi/10.3390/en12234494
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 Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
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
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score 13.070432

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