Artículo
A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants
Autor/es | Masero Rubio, Eva
Domínguez Frejo, José Ramón Maestre Torreblanca, José María Camacho, Eduardo F. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2021 |
Fecha de depósito | 2021-05-06 |
Publicado en |
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Resumen | This article shows how coalitional model predictive control (MPC) can be used to maximize thermal power of large-scale solar parabolic-trough plants. This strategy dynamically generates clusters of loops of collectors ... This article shows how coalitional model predictive control (MPC) can be used to maximize thermal power of large-scale solar parabolic-trough plants. This strategy dynamically generates clusters of loops of collectors according to a given criterion, thus dividing the plant into loosely coupled subsystems that are locally controlled by their corresponding loop valves to gain performance and speed up the computation of control inputs. The proposed strategy is assessed with decentralized and centralized MPC in two simulated solar parabolic-trough fields. Finally, results regarding scalability are also given using these case studies. |
Identificador del proyecto | 789051
DPI2017-86918-R IJC2018-035395-I FPU18/04476 |
Cita | Masero Rubio, E., Domínguez Frejo, J.R., Maestre Torreblanca, J.M. y Fernández Camacho, E. (2021). A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants. Solar Energy, 214, 531-541. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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21_A light (1).pdf | 4.254Mb | [PDF] | Ver/ | |