Incorporating Uncertainty into Probabilistic Performance Models of Concentrating Solar Power Plants

Abstract

A method for applying probabilistic models to concentrating solar-thermal power plants is described in this paper. The benefits of using probabilistic models include quantification of uncertainties inherent in the system and characterization of their impact on system performance and economics. Sensitivity studies using stepwise regression analysis can identify and rank the most important parameters and processes as a means to prioritize future research and activities. The probabilistic method begins with the identification of uncertain variables and the assignment of appropriate distributions for those variables. Those parameters are then sampled using a stratified method (Latin hypercube sampling) to ensure complete and representative sampling from each distribution. Models of performance, reliability, and cost are then simulated multiple times using the sampled set of parameters. The results yield a cumulative distribution function that can be used to quantify the probability of exceeding (or being less than) a particular value. Two examples, a simple cost model and a more detailed performance model of a hypothetical 100-MWe power tower, are provided to illustrate the methods.