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

A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants

This article proposes a real-time implementation of distributed model predictive controllers to maximize the thermal energy generated by parabolic trough collector fields. For this control strategy, we consider that each loop of the solar collector field is individually managed by a controller, which can form coalition with other controllers to attain its local goals while contributing to the overall objective. The formation of coalitions is based on a market-based mechanism in which the heat transfer fluid is traded. To relieve the computational burden online, we propose a learning-based approach that approximates optimization problems so that the controller can be applied in real time. Finally, simulations in a 100 -loop solar collector field are used to assess the coalitional strategy based on neural networks in comparison with the coalitional model predictive control. The results show that the coalitional strategy based on neural networks provides a reduction in computing time of up to 99.74% and a minimal reduction in performance compared to the coalitional model predictive controller used as the baseline

Implementation-oriented freeway traffic control strategies

When adopting optimisation-based control approaches in real time for freeway traffic systems, practical applicability and efficiency are extremely important aspects. The complexity of the traffic control problems to be solved increases with the dimension of the freeway system, making the centralised online application of the control strategies often problematic. In the literature, several solutions have been proposed in order to address these implementation issues. Some of them are aimed at simplifying the problem structure, while others are designed to reduce the overall computational and measurement transmission burden. This chapter focuses on different control solutions which are implementation-oriented. Specifically, distributed, decentralised and event-triggered control solutions for freeway traffic are discussed, also outlining the technological aspects which characterise their implementation