Experimental characterization of fuel cell gas turbine power system and determination of optimal trajectories of operation using a model predictive controller

Hybrid Performance (HyPer) hardware simulation facility installed in the National Energy Technology Laboratory (NETL), U.S. Department of Energy is a hardware in the loop technology that couples a real recuperated gas turbine cycle with a Solid Oxide Fuel Cell (SOFC) Model. The system is composed of a gas turbine, two high-efficiency recuperators, three different bypasses, and several associated pressure vessels and pipes that represent the volumes and flow impedance of the fuel cell. The real-time fuel cell model is used to control a gas burner which replicates the thermal output of a SOFC. Control of thermal energy in and out of the fuel cell, especially during load transients, is fundamental to maintain safe fuel cell/gas turbine operation. This is achieved in the HyPer system by diverting air around the fuel cell system. Three bypass sub-systems are employed for this purpose. A full factorial experimental design and a replicated fractional factorial design are carried out in the HyPer system. The HyPer system has been experimentally tested mostly using a one factor at a time analysis.;The objectives of this work are first, to expand the envelope of operation by performing a full factorial experimental design and a replicated fractional factorial experimental design to enlarge characterization of the HyPer system. A 34 factorial design is selected to study the effect of four factors (input variables) and their interactions: cold air, hot air, bleed air bypass valves, and the electric load on different parameters such as cathode and turbine inlet temperatures, pressure and mass flow. The results obtained show the effects over the response variables of interaction and nonlinearities between the factors in the range of the operation selected in this experiment. This work describes the methodology, strategy, and some results of these experiments that enhance the understanding of the complex thermo-fluid characteristics of hybrid operation. Second, a Model Predictive Control strategy is used to design a controller that allows the system to regulate these factors and to control the different parameters of interaction between both sub-systems, based on models obtained by system identification techniques. Different off-design scenarios of operation have been tested to confirm the estimated implementation behavior of the plant-controller dynamics