However, in engineering scenarios, high-interaction term coefficients (those representing interactions between multiple uncertain parameters) are often negligible, as physical phenomena are primarily driven by main effects and low-order interactions [31]. Consequently, determining these high-interaction coefficients through deterministic model evaluations is inefficient. In the truncation scheme, a substantial number of coefficients correspond to high-interaction terms in the PCE. For instance, in a case with a stochastic dimension of 50 and a maximum polynomial degree of 3, the number of terms in \(\mathcal{A}^{50,3}\) is 23,426. Within this set, only 150 polynomials depend on a single random parameter (univariate polynomials), which is less than 1% of the total. Accordingly, many coefficients can be considered negligible and should not significantly impact the computational cost of constructing the PCE, aligning with the sparsity-of-effect principle [32]. In line with this principle, sparse PCE methods have been developed to identify significant coefficients a priori [33]. Initially, sparse PCE algorithms emerged within the context of compressive sensing [34]. Compressive sensing methods enabled the retrieval of a sparse solution with fewer design samples than basis functions [33]. Regression-based sparse PCE was first introduced using a forward-backward selection procedure [32]. An alternative truncation scheme was proposed by [35], where only multivariate polynomials dependent on a limited number of input parameters are included. The number of input parameters on which multivariate polynomials may depend is a user-defined constant established a priori. A more relevant approach is least-angle regression (LAR) [35], which utilizes a hyperbolic truncation scheme to exclude terms with high-interaction order. Recently, [PERSON] et al. [36] introduced a stepwise regression method that adaptively determinessignificant terms from the basis functions pool, promising improved sparsity and accuracy in sparse PCE compared to LAR. In this paper, the performance of the USC coal-fired power plant at 100% load (current situation) is compared with the low temperature biomass CHP at 80% load (best case in [23]) using the stepwise sparse PCE algorithm proposed by [PERSON] et al. [36]. The goal of this paper is to address this question: \"How can the performance of complex thermodynamic systems be evaluated under uncertainty?\". The following are the next steps:  
1. The definition of a methodology to apply advanced uncertainty quantification techniques to process modeling involving large numbers of parameters and several KPIs. 2. Apply this methodology to the case of a retrofitted, large biomass CHP unit. 3. Investigation of the limits of uncertainty quantification (UQ) polynomial chaos expansion (PCE) and, finally, sparse PCE. 4. Investigation of the most important parameters to model both cases. Currently, the coal-fired power plant runs at base load as much as possible; therefore, the studied load for the coal-based operation is 100%. The research in [23] revealed that running the biomass CHP system at 80% load, with a CHP efficiency of 67%, is the most effective mode of operation. This will also be the preferred operating mode after conversion, and in this study the load of the biomass-based operation will be 80%.