Hierarchical Model Predictive Control Approach for Start-up Optimization of a Combined Cycle Power Plant

Abstract

Due to their numerous advantages, Combined Cycle Power Plants (CCPPs) have become an important technology for power generation. The participation of the CCPPs on the power production market involves frequent start-up/shutdown operations. In this context, optimizing their start-up procedure is of high interest. The optimization objective corresponds to problems that must be solved in real time while fulfilling operating constraints and in the mean time minimizing costs. In this paper a hierarchical model predictive control (H-MPC) structure is proposed to improve the start-up performances and to reduce the computation time. The structure includes two layers, each having a different time scale. The control problem at each level is formulated and based on a model developed in the modeling language Modelica and it aims at determination of an optimal profile of the gas turbine (GT) load. The CCPP model is adapted for optimization purposes and the load profile is assumed to be describable by parameterized functions, whose parameters are computed by solving a constrained optimal control problem. Numerical results prove the potential advantages of the proposed approach.