Electric power optimization in solar trough plants with deep learning-based model predictive control

On-site computational capabilities limit the optimization of electricity production in commercial plants. This work addresses the implementation of a virtual operator for electric power maximization in parabolic-trough collector plants by manipulating the flow rate circulating through the pipes with a neural network-based procedure. A model predictive control (MPC) strategy is proposed using nonlinear models to predict the system’s response. The effect of including diverse parts of the plant in the prediction model and using different objective functions (the model of the pump and the pipes and the difference between optimizing thermal and electric power) is analyzed. First, two control layers are implemented: one for obtaining the temperature setpoints and one for computing the flow rate. Since the nonlinear MPC cannot be computed in real-time for medium and large plants, an artificial neural network is trained to learn the optimal solution and lower the computational burden, reducing a 2-layer MPC strategy into only one neural controller that internally decides the operating point. The simulation results obtain a mean time reduction of 99.99995%, while the electricity production for the studied cases is only reduced by around 1%, making the controller implementable in real-time in actual plants.