Robust model predictive control for a nanofluid based solar thermal power plant

Among the several technologies for solar energy recovery, parabolic solar collectors have emerged as one of the most promising due to their performance, which can be enhanced if nanofluids are employed as heat transfer fluids instead of the traditional alternatives. The inherent time-dependent behavior of solar radiation profiles forces the solar thermal plants to be operated aided with controllers able to reject these strong disturbances. While traditional controllers can be employed for this aim, more advanced techniques such as Model Predictive Control are suggested since this optimal-control based method can be tuned to minimize operating costs, among some other features. The main objective of this work is to implement an MPC controller to a nanofluid-based solar thermal power plant in order to evaluate its performance to reject disturbances on the solar radiation profile in an efficient manner. An off-line nonlinear programming optimization was deployed so we could compare the response of the on-line MPC implementation on a strict enough basis. Furthermore, the performance of MPC controllers is affected by how well does the modeling of the system is able to stick to reality, thus, it is important to test if the controller is robust enough to deal with uncertainty that might be introduced as modeling errors. Results indicate that MPC controllers are suitable for their implementation on these kinds of power plants since they steer the system to achieve desired conditions by smoothly manipulating the decision variable, even in the scenarios where a substantial cascade-effect modeling error was imposed in the parameters of the nanofluid. (C) 2020 Elsevier Ltd. All rights reserved.