Optimization Under Uncertainties of a Biogas-Fueled SOFC-GT Hybrid System

Abstract SOFC-GT hybrid systems can be a good solution for small-medium size applications of distributed generation thanks to their high efficiency and their high fuel flexibility. Fueling these systems with raw biogas coming from biomass gasification makes them also completely renewable, but it introduces a high variability of performance due to the syngas composition fluctuations. For this reason, this work pursues the aim of realizing a robust system optimization, necessary to obtain the best design and ensure high efficiency combined with low variability. To do this, a co-flow, planar, anode supported SOFC Simulink model was used. Moreover, a related surrogate model was created to decrease the computational time and increase exponentially the number of simulations. To calculate the robust optimum design, the Monte Carlo method was used, simulating the syngas composition distribution with 2000 points and to the operating envelope with 5000 input combinations. Mean value and standard deviation in system efficiency were used to select a Pareto front. The robust points turned out to be those with low electric load, small-medium pressure, and high temperature, while maximum efficiency points were characterized by higher pressure levels. The smaller standard deviation at lower pressure was shown to be linked to the bottoming cycle operation and in particular to the gas turbine off-design condition. This difference between the two design conditions (robust optimum and efficiency optimum) confirmed the importance of this optimization process and the influence of fuel composition on system performance.