A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants. (15th January 2021)

• Record Type: Journal Article
• Authors: Masero, Eva; Frejo, José Ramón D.; Maestre, José M.; Camacho, Eduardo F.
• Is Part Of: Solar energy. Volume 214(2021)
• Page Start: 531
• Page End: 541
• Publication Date: 2021-01-15
• Subjects: Model predictive control -- Control by clustering -- Coalitional control -- Non-linear system -- Distributed solar collector field -- Thermal power; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2020.11.056
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: 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. Abstract: 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.