An Effective Nonlinear Multivariable HMPC for USC Power Plant Incorporating NFN-Based Modeling

The ultra-supercritical (USC) unit is an advanced power generation technology with high plant efficiency, high coal utilization, and low emission. However, it is difficult to realize a coordinate control for the USC unit to achieve fast and stable dynamic response during load tracking and grid frequency disturbances, because it is complex, nonlinear, and large scale. This paper presents a nonlinear hierarchical model predictive control (HMPC) to incorporate both the plant-wide economic process optimization and the regulatory process control into a hierarchical control structure, in which the model predictive control (MPC) technology is utilized to solve the multilayer optimization problem. While the nonlinear HMPC optimization problems can be nonconvex, the neuro-fuzzy network (NFN) modeling on USC is incorporated to facilitate the convex quadratic program (QP) routine. Detailed analysis on load tracking and grid frequency disturbances via simulations has been addressed to demonstrate the effectiveness of the proposed nonlinear HMPC.