  Sensitivity analysis (SA) and uncertainty quantification (UQ) are used to
assess and improve engineering models. In this study, various methods of SA and
UQ are described and applied in theoretical and practical examples for use in
energy system analysis. This paper includes local SA (one-at-a-time linear
perturbation), global SA (Morris screening), variance decomposition (Sobol
indices), and regression-based SA. For UQ, stochastic methods (Monte Carlo
sampling) and deterministic methods (using SA profiles) are used. Simple test
problems are included to demonstrate the described methods where input
parameter interactions, linear correlation, model nonlinearity, local
sensitivity, output uncertainty, and variance contribution are explored.
Practical applications of analyzing the efficiency and power output uncertainty
of a molten carbonate fuel cell (MCFC) are conducted. Using different methods,
the uncertainty in the MCFC responses is about 10%. Both SA and UQ methods
agree on the importance ranking of the fuel cell operating temperature and
cathode activation energy as the most influential parameters. Both parameters
contribute to more than 90% of the maximum power and efficiency variance. The
methods applied in this paper can be used to achieve a comprehensive
mathematical understanding of a particular energy model, which can lead to
better performance.
