Application of Stochastic and Deterministic Techniques for Uncertainty Quantification and Sensitivity Analysis of Energy Systems

Abstract

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.