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Research Papers

Incorporating Uncertainty into Probabilistic Performance Models of Concentrating Solar Power Plants

[+] Author and Article Information
Clifford K. Ho

Department of Solar Technologies, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185-1127

Gregory J. Kolb

Department of Solar Technologies, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185-1127ckho@sandia.gov

J. Sol. Energy Eng 132(3), 031012 (Jun 21, 2010) (8 pages) doi:10.1115/1.4001468 History: Received August 21, 2009; Revised December 13, 2009; Published June 21, 2010; Online June 21, 2010

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.

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Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

The total-system modeling pyramid

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Figure 2

Cumulative distribution function of LEC for a simple cost example

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Figure 3

Sensitivity analysis showing relative importance of uncertain input parameters on simulated LEC for a simple cost example

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Figure 4

Schematic of hypothetical molten-salt central receiver system with thermal storage (from Ref. 6)

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Figure 5

Cumulative probability for annual SOLERGY net energy output

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Figure 6

Sensitivity analysis using SOLERGY net energy output as the metric and the uncertain parameters in Table 3 as inputs

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Figure 7

Cumulative probability for levelized energy cost using SOLERGY , reliability, and cost models

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Figure 8

Sensitivity analysis using LEC as the metric and all 33 parameters as inputs

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