Model predictive control for combined cycles integrated with [formula omitted] capture plants

Abstract

Flexible thermal power plants integrated with CO2 capture systems can balance the intermittent power generation of renewable energy sources with low-carbon electricity. Among these power systems, natural gas combined cycles will play a fundamental role because of their faster operation and higher efficiency. Optimisation-based control strategies can enhance the flexible power dispatch of these systems and improve their performance during transient operation. This work proposes a model predictive control (MPC) strategy to stabilise these power plants with post-combustion CO2 capture based on temperature swing chemical absorption and provide offset-free reference tracking. A delta-input formulation with disturbance modelling is proposed, as it provides more efficient computation with offset-free control. Data-based models were developed to replicate the performance of the actual power and capture plants. Prediction of nonlinear behaviour was accomplished by creating a network of local linear models, which allowed the formulation of the dynamic optimisation program in the MPC strategy as a convex quadratic programming problem. A case study demonstrated the effectiveness of the proposed MPC to balance drastic changes on power demand and keep specified capture ratios. Furthermore, the reduced deviations achieved in the reboiler temperature suggest that the nominal value of this parameter could be increased to improve the desorption process without risks of reaching temperatures where the solvent would degradate.