A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants
E. Masero, J. R. D.Frejo, J. M. Maestre, E. F. Camacho
Department of Systems and Automation Engineering, University of Seville, Spain
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.
Model predictive control; Control by clustering; Coalitional control; Non-linear system; Distributed solar collector field; Thermal power
- 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.
Source: Science Direct
Click the video below to play results scenario A
Click the video below to play results scenario B