Clustering-based model predictive control of solar parabolic trough plants

Abstract

This paper presents a clustering-based model predictive controller for optimizing the heat transfer fluid (HTF) flow rates circulating through every loop in solar parabolic trough plants. In particular, we present a hierarchical approach consisting of two layers: a bottom layer, composed of a set of model predictive control (MPC) agents; and a top layer, which dynamically partitions the set of loops into clusters. Likewise, the top layer allocates a certain share of the total available HTF to each cluster, which is then distributed among the loops by the bottom layer in response to the varying conditions of the solar field, e.g., to deal with passing clouds. The dynamic clustering of the system reduces the number of variables to be coordinated in comparison with centralized MPC, thereby speeding up the computations. Moreover, the loops efficiencies and the heat losses coefficients, which influence the loops control model, are also estimated at the bottom layer. Numerical results on a 10-loop and an 80-loop plant are provided.