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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/177188
Ver los metadatos del registro completo
id |
SEDICI_484e4d681346c403dd31369bf379f90b |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/177188 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
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 Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
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 |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1846783773123805184 |
score |
12.982451 |