Market-based clustering of model predictive controllers for maximizing collected energy by parabolic-trough solar collector fields

This article focuses on maximizing the thermal energy collected by parabolic-trough solar collector fields to increase the production of the plant. To this end, we propose a market-based clustering model predictive control strategy in which controllers of collector loops may offer and demand heat transfer fluid in a market. When a transaction is made between loop controllers, a coalition is formed, and the corresponding agents act as a single entity. The proposed hierarchical algorithm fosters the formation of coalitions dynamically to improve the overall control objective, increasing the thermal energy delivered by the field. Finally, the proposed controller is assessed via simulation with other control methods in two solar parabolic-trough fields. The results show that the energy efficiency with the clustering strategy outperforms by 12% that of traditional controllers, and the method is implementable in real-time to control large-scale solar collector fields, where significant gains in thermal collected energy can be obtained, due to its scalability.

Automated summary

This article proposes a market-based clustering model predictive control strategy for maximizing the thermal energy collected by parabolic-trough solar collector fields. The hierarchical algorithm fosters the formation of coalitions dynamically to improve the overall control objective, resulting in a 12% increase in energy efficiency compared to traditional controllers. The method is implementable in real-time for controlling large-scale solar collector fields and can achieve significant gains in thermal collected energy due to its scalability.