The analysis and design of complex energy systems is generally performed starting from a single operating condition and assuming a series of design parameters as fixed values. However, many of the variables on which the design is based are subject to uncertainty because they are not determinable with an adequate precision. For advanced energy system and other processes, the uncertainties associated with model input parameters can affect both the performance and the cost. Methods for system design under uncertainty thus become essential. As uncertainty is a broad concept, it is possible, and often useful, to approach it in several ways. One rather general approach, which is applied to a wide variety of problems, is to assign a probability distribution to the various uncertain input parameters of the model. Many studies have faced optimization problems, but almost no one has considered the uncertainty as a factor to be taken into account. An off-design static model of a micro-turbine has been studied: it is built on the configuration of a Turbec T100 actually installed at the University research laboratory. Stochastic analysis has been treated implementing the approximated method Response Sensitivity Analysis (RSA) based on Taylor series expansion. RSA methods have been used on this system model to estimate its main performance and economic parameters under the influence of uncertainties related to various operating parameters of the turbo-machinery.
