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

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.

Automated summary

This article demonstrates how coalitional model predictive control can be used to maximize the thermal power of large-scale solar parabolic-trough plants. The proposed strategy divides the plant into subsystems controlled by their corresponding loop valves, resulting in improved performance and faster computation of control inputs. The scalability of the strategy is evaluated using decentralized and centralized MPC in simulated solar parabolic-trough fields.