Smart grid optimized operation driven by reinforcement learning

This thesis focuses on the development of a reinforcement learning model for the operation and demand response control of a smart grid. First, a generic problem is formulated to define the demand response control. Then a study case is proposed with different locations of distributed energy resources...

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Detalles Bibliográficos
Autor: Fisco Compte, Pau
Tipo de recurso: tesis de maestría
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/374273
Acceso en línea:https://hdl.handle.net/2117/374273
Access Level:acceso abierto
Palabra clave:Smart power grids
Xarxes elèctriques intel·ligents
Àrees temàtiques de la UPC::Matemàtiques i estadística
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spelling Smart grid optimized operation driven by reinforcement learningFisco Compte, PauSmart power gridsXarxes elèctriques intel·ligentsÀrees temàtiques de la UPC::Matemàtiques i estadísticaThis thesis focuses on the development of a reinforcement learning model for the operation and demand response control of a smart grid. First, a generic problem is formulated to define the demand response control. Then a study case is proposed with different locations of distributed energy resources and flexible components for reducing the cost associated with its grid con- sumption and safety management. The potential application of different deep reinforcement learning models with different activation functions and network shapes, among them, will be compared and analysed for the grid operation. The goal is to find a deep reinforcement learning model to optimize the demand side of energy management of a smart grid, that achieves better results than other existing approaches. Finally, a new policy for deep reinforcement learning algorithms will be proposed. This will provide a tool to guide the energy management of elec- trical distribution grids with high penetration of renewable energy sources.Universitat Politècnica de CatalunyaAragüés Peñalba, MònicaHernández Matheus, Alejandro Henrique20222022-09-1220222022-10-11master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttps://hdl.handle.net/2117/374273reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3742732026-05-27T15:37:01Z
dc.title.none.fl_str_mv Smart grid optimized operation driven by reinforcement learning
title Smart grid optimized operation driven by reinforcement learning
spellingShingle Smart grid optimized operation driven by reinforcement learning
Fisco Compte, Pau
Smart power grids
Xarxes elèctriques intel·ligents
Àrees temàtiques de la UPC::Matemàtiques i estadística
title_short Smart grid optimized operation driven by reinforcement learning
title_full Smart grid optimized operation driven by reinforcement learning
title_fullStr Smart grid optimized operation driven by reinforcement learning
title_full_unstemmed Smart grid optimized operation driven by reinforcement learning
title_sort Smart grid optimized operation driven by reinforcement learning
dc.creator.none.fl_str_mv Fisco Compte, Pau
author Fisco Compte, Pau
author_facet Fisco Compte, Pau
author_role author
dc.contributor.none.fl_str_mv Aragüés Peñalba, Mònica
Hernández Matheus, Alejandro Henrique
dc.subject.none.fl_str_mv Smart power grids
Xarxes elèctriques intel·ligents
Àrees temàtiques de la UPC::Matemàtiques i estadística
topic Smart power grids
Xarxes elèctriques intel·ligents
Àrees temàtiques de la UPC::Matemàtiques i estadística
description This thesis focuses on the development of a reinforcement learning model for the operation and demand response control of a smart grid. First, a generic problem is formulated to define the demand response control. Then a study case is proposed with different locations of distributed energy resources and flexible components for reducing the cost associated with its grid con- sumption and safety management. The potential application of different deep reinforcement learning models with different activation functions and network shapes, among them, will be compared and analysed for the grid operation. The goal is to find a deep reinforcement learning model to optimize the demand side of energy management of a smart grid, that achieves better results than other existing approaches. Finally, a new policy for deep reinforcement learning algorithms will be proposed. This will provide a tool to guide the energy management of elec- trical distribution grids with high penetration of renewable energy sources.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-09-12
2022
2022-10-11
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/374273
url https://hdl.handle.net/2117/374273
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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