Model predictive control of largue scale solar trough plants

One of the current technological challenges is to make solar energy economical and competitive. Advanced control techniques may contribute in this direction by maximizing the electricity generated by using optimal control strategies. A number of research works have been developed concerning control...

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Detalles Bibliográficos
Autores: Camacho, Eduardo F., Sánchez del Pozo Fernández, Adolfo Juan, Gallego Len, Antonio Javier
Tipo de recurso: capítulo de libro
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2019
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/109020
Acceso en línea:https://hdl.handle.net/11441/109020
Access Level:acceso abierto
Palabra clave:Solar parabolic
Model predictive control
Collector defocus
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spelling Model predictive control of largue scale solar trough plantsCamacho, Eduardo F.Sánchez del Pozo Fernández, Adolfo JuanGallego Len, Antonio JavierSolar parabolicModel predictive controlCollector defocusOne of the current technological challenges is to make solar energy economical and competitive. Advanced control techniques may contribute in this direction by maximizing the electricity generated by using optimal control strategies. A number of research works have been developed concerning control and optimization of solar plants. Most of these works have been developed for the experimental solar trough plant of ACUREX at the Plataforma solar de Almería (PSA) (10 parallel loops of collectors). Generally, small plants such as the ACUREX field can be modelled as an equivalent loop for developing control strategies. Commercial solar trough plants are very extensive, covering vast areas. As an example, Solana Generating Station which has 808 parallel loops of four collectors connected in series (3,232 collectors) covering 780 hectares. The optimization of large scale solar trough plants poses important challenges which require new advanced control techniques to address them: 1. The optical efficiency of different groups of loops may be substantially different in large scale solar plants. The most efficient loops will probably have to be defocused to avoid excessive temperatures. Paradoxically, the most efficient loops will have the higher energy losses because of defocusing. To avoid this energy loss, the valves of the most efficient loops would have to be opened to increase the HTF flow. However, any movement of the valve in one of the loops will influence the flow of the rest of the loops. Loop valves are only used in current plants for steady state flow balancing. 2. Scattered clouds may only affect the locations where the sensors are placed, while the rest of the plant may be under the effect of intense DNI, or vice versa. Sudden changes in DNI produced by scattered clouds induce oscillations so severe that the solar field may have to be defocused or shutdown. This fact produces, in general, not only energy losses but plant deterioration. A spatially distributed DNI nowcasting can be used to improve plant operation and optimize the production. This chapter presents some new concepts and ideas that the authors believe will be the future steps in the development and progress of solar thermal energy. Preliminary results for advanced control of solar plants are presented, using more effective defocusing mechanisms and dynamic thermal balance of loops that have already shown to produce significant gains,.Unión Europea 789051Nova science publishersIngeniería de Sistemas y Automática2019info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/109020reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSolar Energy Systems: Progress and Future Directions789051info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1090202026-06-17T12:51:07Z
dc.title.none.fl_str_mv Model predictive control of largue scale solar trough plants
title Model predictive control of largue scale solar trough plants
spellingShingle Model predictive control of largue scale solar trough plants
Camacho, Eduardo F.
Solar parabolic
Model predictive control
Collector defocus
title_short Model predictive control of largue scale solar trough plants
title_full Model predictive control of largue scale solar trough plants
title_fullStr Model predictive control of largue scale solar trough plants
title_full_unstemmed Model predictive control of largue scale solar trough plants
title_sort Model predictive control of largue scale solar trough plants
dc.creator.none.fl_str_mv Camacho, Eduardo F.
Sánchez del Pozo Fernández, Adolfo Juan
Gallego Len, Antonio Javier
author Camacho, Eduardo F.
author_facet Camacho, Eduardo F.
Sánchez del Pozo Fernández, Adolfo Juan
Gallego Len, Antonio Javier
author_role author
author2 Sánchez del Pozo Fernández, Adolfo Juan
Gallego Len, Antonio Javier
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
dc.subject.none.fl_str_mv Solar parabolic
Model predictive control
Collector defocus
topic Solar parabolic
Model predictive control
Collector defocus
description One of the current technological challenges is to make solar energy economical and competitive. Advanced control techniques may contribute in this direction by maximizing the electricity generated by using optimal control strategies. A number of research works have been developed concerning control and optimization of solar plants. Most of these works have been developed for the experimental solar trough plant of ACUREX at the Plataforma solar de Almería (PSA) (10 parallel loops of collectors). Generally, small plants such as the ACUREX field can be modelled as an equivalent loop for developing control strategies. Commercial solar trough plants are very extensive, covering vast areas. As an example, Solana Generating Station which has 808 parallel loops of four collectors connected in series (3,232 collectors) covering 780 hectares. The optimization of large scale solar trough plants poses important challenges which require new advanced control techniques to address them: 1. The optical efficiency of different groups of loops may be substantially different in large scale solar plants. The most efficient loops will probably have to be defocused to avoid excessive temperatures. Paradoxically, the most efficient loops will have the higher energy losses because of defocusing. To avoid this energy loss, the valves of the most efficient loops would have to be opened to increase the HTF flow. However, any movement of the valve in one of the loops will influence the flow of the rest of the loops. Loop valves are only used in current plants for steady state flow balancing. 2. Scattered clouds may only affect the locations where the sensors are placed, while the rest of the plant may be under the effect of intense DNI, or vice versa. Sudden changes in DNI produced by scattered clouds induce oscillations so severe that the solar field may have to be defocused or shutdown. This fact produces, in general, not only energy losses but plant deterioration. A spatially distributed DNI nowcasting can be used to improve plant operation and optimize the production. This chapter presents some new concepts and ideas that the authors believe will be the future steps in the development and progress of solar thermal energy. Preliminary results for advanced control of solar plants are presented, using more effective defocusing mechanisms and dynamic thermal balance of loops that have already shown to produce significant gains,.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/submittedVersion
format bookPart
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/109020
url https://hdl.handle.net/11441/109020
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Solar Energy Systems: Progress and Future Directions
789051
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 Nova science publishers
publisher.none.fl_str_mv Nova science publishers
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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score 15,301603

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