Enhancing Flexibility in V2B Applications with Renewable Energy Resources

Autores
Trimboli, Maximiliano; Antonelli, Nicolás; Avila, Luis
Año de publicación
2024
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The incorporation of EV parking within vehicle-to-building (V2B) frameworks signifies not only a technological evolution but also a pivotal step towards constructing smarter and environmentally friendly urban environments. This initiative actively contributes to the optimization of system resources while also enabling the incorporation of renewable energy resources. In this study, we propose the development of reinforcement learning (RL) algorithms for the management of smart parking lots, aiming to minimize building energy purchases from the grid while ensuring efficient charging of EVs. The proposed methods obtained a 15% to 17% improvement in the evaluation reward in comparison with rule based method as a benchmark. In the realm of grid energy, they saved 9 to 11% in average purchase cost. In essence, these algorithms, after training, make more efficient decisions than more traditional control methods while ensuring electric vehicle (EV) charging.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Electric vehicles
Smart Charging
Renewable Energy
Reinforcement Learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/177188
Acceder
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spelling Enhancing Flexibility in V2B Applications with Renewable Energy ResourcesTrimboli, MaximilianoAntonelli, NicolásAvila, LuisCiencias InformáticasElectric vehiclesSmart ChargingRenewable EnergyReinforcement LearningThe incorporation of EV parking within vehicle-to-building (V2B) frameworks signifies not only a technological evolution but also a pivotal step towards constructing smarter and environmentally friendly urban environments. This initiative actively contributes to the optimization of system resources while also enabling the incorporation of renewable energy resources. In this study, we propose the development of reinforcement learning (RL) algorithms for the management of smart parking lots, aiming to minimize building energy purchases from the grid while ensuring efficient charging of EVs. The proposed methods obtained a 15% to 17% improvement in the evaluation reward in comparison with rule based method as a benchmark. In the realm of grid energy, they saved 9 to 11% in average purchase cost. In essence, these algorithms, after training, make more efficient decisions than more traditional control methods while ensuring electric vehicle (EV) charging.Sociedad Argentina de Informática e Investigación Operativa2024-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf223-236http://sedici.unlp.edu.ar/handle/10915/177188enginfo:eu-repo/semantics/altIdentifier/url/https://revistas.unlp.edu.ar/JAIIO/article/view/17907info:eu-repo/semantics/altIdentifier/issn/2451-7496info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:28:43Zoai:sedici.unlp.edu.ar:10915/177188Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:28:43.985SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Enhancing Flexibility in V2B Applications with Renewable Energy Resources
title Enhancing Flexibility in V2B Applications with Renewable Energy Resources
spellingShingle Enhancing Flexibility in V2B Applications with Renewable Energy Resources
Trimboli, Maximiliano
Ciencias Informáticas
Electric vehicles
Smart Charging
Renewable Energy
Reinforcement Learning
title_short Enhancing Flexibility in V2B Applications with Renewable Energy Resources
title_full Enhancing Flexibility in V2B Applications with Renewable Energy Resources
title_fullStr Enhancing Flexibility in V2B Applications with Renewable Energy Resources
title_full_unstemmed Enhancing Flexibility in V2B Applications with Renewable Energy Resources
title_sort Enhancing Flexibility in V2B Applications with Renewable Energy Resources
dc.creator.none.fl_str_mv Trimboli, Maximiliano
Antonelli, Nicolás
Avila, Luis
author Trimboli, Maximiliano
author_facet Trimboli, Maximiliano
Antonelli, Nicolás
Avila, Luis
author_role author
author2 Antonelli, Nicolás
Avila, Luis
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Electric vehicles
Smart Charging
Renewable Energy
Reinforcement Learning
topic Ciencias Informáticas
Electric vehicles
Smart Charging
Renewable Energy
Reinforcement Learning
dc.description.none.fl_txt_mv The incorporation of EV parking within vehicle-to-building (V2B) frameworks signifies not only a technological evolution but also a pivotal step towards constructing smarter and environmentally friendly urban environments. This initiative actively contributes to the optimization of system resources while also enabling the incorporation of renewable energy resources. In this study, we propose the development of reinforcement learning (RL) algorithms for the management of smart parking lots, aiming to minimize building energy purchases from the grid while ensuring efficient charging of EVs. The proposed methods obtained a 15% to 17% improvement in the evaluation reward in comparison with rule based method as a benchmark. In the realm of grid energy, they saved 9 to 11% in average purchase cost. In essence, these algorithms, after training, make more efficient decisions than more traditional control methods while ensuring electric vehicle (EV) charging.
Sociedad Argentina de Informática e Investigación Operativa
description The incorporation of EV parking within vehicle-to-building (V2B) frameworks signifies not only a technological evolution but also a pivotal step towards constructing smarter and environmentally friendly urban environments. This initiative actively contributes to the optimization of system resources while also enabling the incorporation of renewable energy resources. In this study, we propose the development of reinforcement learning (RL) algorithms for the management of smart parking lots, aiming to minimize building energy purchases from the grid while ensuring efficient charging of EVs. The proposed methods obtained a 15% to 17% improvement in the evaluation reward in comparison with rule based method as a benchmark. In the realm of grid energy, they saved 9 to 11% in average purchase cost. In essence, these algorithms, after training, make more efficient decisions than more traditional control methods while ensuring electric vehicle (EV) charging.
publishDate 2024
dc.date.none.fl_str_mv 2024-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/177188
url http://sedici.unlp.edu.ar/handle/10915/177188
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://revistas.unlp.edu.ar/JAIIO/article/view/17907
info:eu-repo/semantics/altIdentifier/issn/2451-7496
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
223-236
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