A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants

•Control by clustering for maximizing the thermal power of solar fields.•Valves at the beginning of each loop increase the achieved thermal power.•The clustering criterion is to associate unbalanced loops dynamically.•Coalitional MPC approaches the optimal performance and can be carried in real-time...

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Bibliographic Details
Published in:Solar energy 2021-01, Vol.214, p.531-541
Main Authors: Masero, Eva, Frejo, José Ramón D., Maestre, José M., Camacho, Eduardo F.
Format: Article
Language:English
Subjects:
Clustering
Coalitional control
Control by clustering
Distributed solar collector field
Model predictive control
Non-linear system
Power plants
Predictive control
Solar energy
Subsystems
Thermal power
Thermoelectricity
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Summary:•Control by clustering for maximizing the thermal power of solar fields.•Valves at the beginning of each loop increase the achieved thermal power.•The clustering criterion is to associate unbalanced loops dynamically.•Coalitional MPC approaches the optimal performance and can be carried in real-time.•Scalability and ease of deployment in large-scale CSP fields. 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.
ISSN:0038-092X
1471-1257
DOI:10.1016/j.solener.2020.11.056