Clustering-based model predictive control of solar parabolic trough plants

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Bibliographic Details
Authors: Chanfreut Palacio, Paula, Maestre Torreblanca, José María, Gallego Len, Antonio Javier, Annaswamy, Anuradha M., Camacho, Eduardo F.
Format: article
Status:Published version
Publication Date:2023
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/149177
Online Access:https://hdl.handle.net/11441/149177
https://doi.org/10.1016/j.renene.2023.118978
Access Level:Open access
Keyword:Model predictive control
Solar thermal power plants
Parabolic trough collectors
Control by clustering
Coalitional control
Hierarchical control
id ES_c6d6918c4c0fa8a6ab2df85bc258b439
oai_identifier_str oai:idus.us.es:11441/149177
network_acronym_str ES
network_name_str España
repository_id_str
spelling Clustering-based model predictive control of solar parabolic trough plantsChanfreut Palacio, PaulaMaestre Torreblanca, José MaríaGallego Len, Antonio JavierAnnaswamy, Anuradha M.Camacho, Eduardo F.Model predictive controlSolar thermal power plantsParabolic trough collectorsControl by clusteringCoalitional controlHierarchical controlThis is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).This paper presents a clustering-based model predictive controller for optimizing the heat transfer fluid (HTF) flow rates circulating through every loop in solar parabolic trough plants. In particular, we present a hierarchical approach consisting of two layers: a bottom layer, composed of a set of model predictive control (MPC) agents; and a top layer, which dynamically partitions the set of loops into clusters. Likewise, the top layer allocates a certain share of the total available HTF to each cluster, which is then distributed among the loops by the bottom layer in response to the varying conditions of the solar field, e.g., to deal with passing clouds. The dynamic clustering of the system reduces the number of variables to be coordinated in comparison with centralized MPC, thereby speeding up the computations. Moreover, the loops efficiencies and the heat losses coefficients, which influence the loops control model, are also estimated at the bottom layer. Numerical results on a 10-loop and an 80-loop plant are provided.ElsevierIngeniería de Sistemas y AutomáticaTEP116: Automática y Robótica IndustrialUnión EuropeaMinisterio de Ciencia e Innovación (MICIN). España2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/149177https://doi.org/10.1016/j.renene.2023.118978reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésRenewable Energy, 216, 118978.SI-1838/24/2018PID2020-119476RB-I00https://www.sciencedirect.com/science/article/pii/S0960148123008844info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1491772026-06-17T12:51:07Z
dc.title.none.fl_str_mv Clustering-based model predictive control of solar parabolic trough plants
title Clustering-based model predictive control of solar parabolic trough plants
spellingShingle Clustering-based model predictive control of solar parabolic trough plants
Chanfreut Palacio, Paula
Model predictive control
Solar thermal power plants
Parabolic trough collectors
Control by clustering
Coalitional control
Hierarchical control
title_short Clustering-based model predictive control of solar parabolic trough plants
title_full Clustering-based model predictive control of solar parabolic trough plants
title_fullStr Clustering-based model predictive control of solar parabolic trough plants
title_full_unstemmed Clustering-based model predictive control of solar parabolic trough plants
title_sort Clustering-based model predictive control of solar parabolic trough plants
dc.creator.none.fl_str_mv Chanfreut Palacio, Paula
Maestre Torreblanca, José María
Gallego Len, Antonio Javier
Annaswamy, Anuradha M.
Camacho, Eduardo F.
author Chanfreut Palacio, Paula
author_facet Chanfreut Palacio, Paula
Maestre Torreblanca, José María
Gallego Len, Antonio Javier
Annaswamy, Anuradha M.
Camacho, Eduardo F.
author_role author
author2 Maestre Torreblanca, José María
Gallego Len, Antonio Javier
Annaswamy, Anuradha M.
Camacho, Eduardo F.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
TEP116: Automática y Robótica Industrial
Unión Europea
Ministerio de Ciencia e Innovación (MICIN). España
dc.subject.none.fl_str_mv Model predictive control
Solar thermal power plants
Parabolic trough collectors
Control by clustering
Coalitional control
Hierarchical control
topic Model predictive control
Solar thermal power plants
Parabolic trough collectors
Control by clustering
Coalitional control
Hierarchical control
description This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/149177
https://doi.org/10.1016/j.renene.2023.118978
url https://hdl.handle.net/11441/149177
https://doi.org/10.1016/j.renene.2023.118978
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Renewable Energy, 216, 118978.
SI-1838/24/2018
PID2020-119476RB-I00
https://www.sciencedirect.com/science/article/pii/S0960148123008844
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
_version_ 1869419106596290560
score 15,300719

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