On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid

In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real rene...

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Autores: Velarde Rueda, Pablo, Valverde, L., Maestre Torreblanca, José María, Ocampo-Martínez, Carlos, Bordons Alba, Carlos
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2017
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/89672
Acceso en línea:https://hdl.handle.net/11441/89672
https://doi.org/10.1016/j.jpowsour.2017.01.015
Access Level:acceso abierto
Palabra clave:Hydrogen storage
Microgrid
Model predictive control
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spelling On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based MicrogridVelarde Rueda, PabloValverde, L.Maestre Torreblanca, José MaríaOcampo-Martínez, CarlosBordons Alba, CarlosHydrogen storageMicrogridModel predictive controlIn this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RMinisterio de Economía y Competitividad DPI2013-482443-C2-1-RElsevierIngeniería de Sistemas y Automática2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/89672https://doi.org/10.1016/j.jpowsour.2017.01.015reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésJournal of Power Sources, 343, 161-173.DPI2013-46912-C2-1-RDPI2013-482443-C2-1-Rhttps://reader.elsevier.com/reader/sd/pii/S0378775317300150?token=259639655EE2C5FADCC4AB2CB70685DC1492AB33F5DCA6DABCE57E7348CCB654E629854491101C319E381105E40758F0info:eu-repo/semantics/openAccessoai:idus.us.es:11441/896722026-06-17T12:51:07Z
dc.title.none.fl_str_mv On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid
title On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid
spellingShingle On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid
Velarde Rueda, Pablo
Hydrogen storage
Microgrid
Model predictive control
title_short On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid
title_full On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid
title_fullStr On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid
title_full_unstemmed On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid
title_sort On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid
dc.creator.none.fl_str_mv Velarde Rueda, Pablo
Valverde, L.
Maestre Torreblanca, José María
Ocampo-Martínez, Carlos
Bordons Alba, Carlos
author Velarde Rueda, Pablo
author_facet Velarde Rueda, Pablo
Valverde, L.
Maestre Torreblanca, José María
Ocampo-Martínez, Carlos
Bordons Alba, Carlos
author_role author
author2 Valverde, L.
Maestre Torreblanca, José María
Ocampo-Martínez, Carlos
Bordons Alba, Carlos
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
dc.subject.none.fl_str_mv Hydrogen storage
Microgrid
Model predictive control
topic Hydrogen storage
Microgrid
Model predictive control
description In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/89672
https://doi.org/10.1016/j.jpowsour.2017.01.015
url https://hdl.handle.net/11441/89672
https://doi.org/10.1016/j.jpowsour.2017.01.015
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Power Sources, 343, 161-173.
DPI2013-46912-C2-1-R
DPI2013-482443-C2-1-R
https://reader.elsevier.com/reader/sd/pii/S0378775317300150?token=259639655EE2C5FADCC4AB2CB70685DC1492AB33F5DCA6DABCE57E7348CCB654E629854491101C319E381105E40758F0
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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