Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids

Autores
Salazar, Eduardo Javier; Jurado Egas, Mauro Fabricio; Samper, Mauricio Eduardo
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
International agreements support the modernization of electricity networks and renewable energy resources (RES). However, these RES affect market prices due to resource variability (e.g., solar). Among the alternatives, Demand Response (DR) is presented as a tool to improve the balance between electricity supply and demand by adapting consumption to available production. In this sense, this work focuses on developing a DR model that combines price and incentive-based demand response models (P-B and I-B) to efficiently manage consumer demand with data from a real San Juan—Argentina distribution network. In addition, a price scheme is proposed in real time and by the time of use in relation to the consumers’ influence in the peak demand of the system. The proposed schemes increase load factor and improve demand displacement compared to a demand response reference model. In addition, the proposed reinforcement learning model improves short-term and long-term price search. Finally, a description and formulation of the market where the work was implemented is presented.
Fil: Salazar, Eduardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
Fil: Jurado Egas, Mauro Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
Fil: Samper, Mauricio Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
Materia
DEMAND COINCIDENCE FACTOR
INCENTIVE-BASED DEMAND RESPONSE
PRICE-BASED DEMAND RESPONSE
REINFORCEMENT Q-LEARNING
REPLAY MEMORY EXCHANGE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/227342
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart GridsSalazar, Eduardo JavierJurado Egas, Mauro FabricioSamper, Mauricio EduardoDEMAND COINCIDENCE FACTORINCENTIVE-BASED DEMAND RESPONSEPRICE-BASED DEMAND RESPONSEREINFORCEMENT Q-LEARNINGREPLAY MEMORY EXCHANGEhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2International agreements support the modernization of electricity networks and renewable energy resources (RES). However, these RES affect market prices due to resource variability (e.g., solar). Among the alternatives, Demand Response (DR) is presented as a tool to improve the balance between electricity supply and demand by adapting consumption to available production. In this sense, this work focuses on developing a DR model that combines price and incentive-based demand response models (P-B and I-B) to efficiently manage consumer demand with data from a real San Juan—Argentina distribution network. In addition, a price scheme is proposed in real time and by the time of use in relation to the consumers’ influence in the peak demand of the system. The proposed schemes increase load factor and improve demand displacement compared to a demand response reference model. In addition, the proposed reinforcement learning model improves short-term and long-term price search. Finally, a description and formulation of the market where the work was implemented is presented.Fil: Salazar, Eduardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Jurado Egas, Mauro Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Samper, Mauricio Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaMDPI2023-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/227342Salazar, Eduardo Javier; Jurado Egas, Mauro Fabricio; Samper, Mauricio Eduardo; Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids; MDPI; Energies; 16; 3; 2-2023; 1-331996-1073CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1996-1073/16/3/1466info:eu-repo/semantics/altIdentifier/doi/10.3390/en16031466info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:39:03Zoai:ri.conicet.gov.ar:11336/227342instacron: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 09:39:03.726CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids
title Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids
spellingShingle Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids
Salazar, Eduardo Javier
DEMAND COINCIDENCE FACTOR
INCENTIVE-BASED DEMAND RESPONSE
PRICE-BASED DEMAND RESPONSE
REINFORCEMENT Q-LEARNING
REPLAY MEMORY EXCHANGE
title_short Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids
title_full Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids
title_fullStr Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids
title_full_unstemmed Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids
title_sort Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids
dc.creator.none.fl_str_mv Salazar, Eduardo Javier
Jurado Egas, Mauro Fabricio
Samper, Mauricio Eduardo
author Salazar, Eduardo Javier
author_facet Salazar, Eduardo Javier
Jurado Egas, Mauro Fabricio
Samper, Mauricio Eduardo
author_role author
author2 Jurado Egas, Mauro Fabricio
Samper, Mauricio Eduardo
author2_role author
author
dc.subject.none.fl_str_mv DEMAND COINCIDENCE FACTOR
INCENTIVE-BASED DEMAND RESPONSE
PRICE-BASED DEMAND RESPONSE
REINFORCEMENT Q-LEARNING
REPLAY MEMORY EXCHANGE
topic DEMAND COINCIDENCE FACTOR
INCENTIVE-BASED DEMAND RESPONSE
PRICE-BASED DEMAND RESPONSE
REINFORCEMENT Q-LEARNING
REPLAY MEMORY EXCHANGE
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv International agreements support the modernization of electricity networks and renewable energy resources (RES). However, these RES affect market prices due to resource variability (e.g., solar). Among the alternatives, Demand Response (DR) is presented as a tool to improve the balance between electricity supply and demand by adapting consumption to available production. In this sense, this work focuses on developing a DR model that combines price and incentive-based demand response models (P-B and I-B) to efficiently manage consumer demand with data from a real San Juan—Argentina distribution network. In addition, a price scheme is proposed in real time and by the time of use in relation to the consumers’ influence in the peak demand of the system. The proposed schemes increase load factor and improve demand displacement compared to a demand response reference model. In addition, the proposed reinforcement learning model improves short-term and long-term price search. Finally, a description and formulation of the market where the work was implemented is presented.
Fil: Salazar, Eduardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
Fil: Jurado Egas, Mauro Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
Fil: Samper, Mauricio Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
description International agreements support the modernization of electricity networks and renewable energy resources (RES). However, these RES affect market prices due to resource variability (e.g., solar). Among the alternatives, Demand Response (DR) is presented as a tool to improve the balance between electricity supply and demand by adapting consumption to available production. In this sense, this work focuses on developing a DR model that combines price and incentive-based demand response models (P-B and I-B) to efficiently manage consumer demand with data from a real San Juan—Argentina distribution network. In addition, a price scheme is proposed in real time and by the time of use in relation to the consumers’ influence in the peak demand of the system. The proposed schemes increase load factor and improve demand displacement compared to a demand response reference model. In addition, the proposed reinforcement learning model improves short-term and long-term price search. Finally, a description and formulation of the market where the work was implemented is presented.
publishDate 2023
dc.date.none.fl_str_mv 2023-02
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/11336/227342
Salazar, Eduardo Javier; Jurado Egas, Mauro Fabricio; Samper, Mauricio Eduardo; Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids; MDPI; Energies; 16; 3; 2-2023; 1-33
1996-1073
CONICET Digital
CONICET
url http://hdl.handle.net/11336/227342
identifier_str_mv Salazar, Eduardo Javier; Jurado Egas, Mauro Fabricio; Samper, Mauricio Eduardo; Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids; MDPI; Energies; 16; 3; 2-2023; 1-33
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/1996-1073/16/3/1466
info:eu-repo/semantics/altIdentifier/doi/10.3390/en16031466
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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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