Coalitional model predictive control of parabolic-trough solar collector fields with population-dynamics assistance

Parabolic-trough solar collector fields are large-scale systems, so the application of centralized optimization -based control methods to these systems is often not suitable for real-time control. As such, this paper formulates a novel coalitional control approach as an appropriate alternative to the centralized scheme. The key idea is to split the overall solar collector field into smaller subsystems, each of them governed by a local controller. Then, controllers are clustered into coalitions to solve a local optimization-based problem related to the corresponding subset of subsystems, so that an approximate solution of the original centralized problem can be obtained in a decentralized fashion. However, the operational constraints of the solar collector field couple the optimization problems of the multiple coalitions, thus limiting the ability to solve them in a fully decentralized manner. To overcome this issue, a novel population-dynamics-assisted resource allocation strategy is proposed as a mechanism to decouple the local optimization problems of the multiple coalitions. The proposed coalitional methodology allows to solve the multiple local subproblems in parallel, hence reducing the overall computational burden, while guaranteeing the satisfaction of the operational constraints and without significantly compromising the overall performance. The effectiveness of proposed approach is shown through numerical simulations of a 10-and 100-loop version of the ACUREX solar collector field of Plataforma Solar de Almeria, Spain.

Automated summary

This paper proposes a novel coalitional control approach for large-scale parabolic-trough solar collector fields. The approach splits the field into smaller subsystems, each governed by a local controller. Controllers are clustered into coalitions to solve local optimization problems, achieving an approximate solution to the centralized problem in a decentralized manner. A population-dynamics-assisted resource allocation strategy is proposed to decouple the optimization problems of the coalitions, reducing computational burden while ensuring operational constraints and overall performance.