A L1-LEMPC hierarchical control structure for economic load-tracking of super-critical power plants

The super-critical thermal power plants are undertaking more and more responsibilities for the balance of the power grid intermittency of the renewable energies. However, the frequent wide-range load regulation may deteriorate the operational efficiency of the power plant. To this end, a hierarchical control structure with two layers is proposed in this paper. An economic model predictive controller using a locally linearized model of the plant (LEMPC) is employed in the upper layer to realize an optimal load tracking. A L1 adaptive controller in the lower layer forces the plant to track the optimal trajectory by estimating and compensating the lumped uncertainty between the real plant and the linear model. The tracking performance is theoretically proved to reach the desired transient process. The proposed hierarchical control architecture is validated through simulations on a simplified 1000MW super-critical boiler-turbine unit model with comparison to the other two conventional real-time optimization control approaches. The results show that the proposed L1-LEMPC control system produces better load tracking performance than the other two conventional control strategies with higher operational economy efficiency. Moreover, under the environment of severe external disturbance and parameter perturbation, the proposed control system still maintains satisfactory performance.