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DOE OSTI · 3022648

An Uncertainty-Informed and High-Fidelity Performance Forecasting Framework for Heliostat Fields

Zolan, Alexander [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000326017604)·Kler, Eugene [National Laboratory of the Rockies, Golden, CO (United States)]·Hamilton, William [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000244654239)·Sment, Jeremy [Sandia National Laboratories]
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Abstract

Concentrating Solar Thermal (CST) tower systems employ heliostat fields to direct solar energy to a central receiver, which then transfers the heat either directly to a thermal process (e.g., steam production) or to a thermal energy storage system for future use. Heliostat fields compose a significant proportion of the project costs of a CST tower system and the performance of the heliostats determines a plant's productivity at a given location. While CST characterization tools such as SolarPILOT and System Advisor Model (SAM) include a large collection of inputs that influence the performance of a CST tower system, many are uncertain prior to the development of the project and may have a significant impact on the overall energy delivery and profitability of a project; moreover, the fidelity of these models under default conditions may be insufficient to determine the value of component improvements such as those under development in the Heliostat Consortium. This work introduces a Monte Carlo simulation framework that incorporates uncertainty in key performance parameters to generate confidence intervals and percentile estimates for a CST solar field's energy delivery.

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14 SOLAR ENERGY25 ENERGY STORAGE97 MATHEMATICS AND COMPUTINGconcentrating solar powerheliostat fieldsMonte Carlo simulationperformance forecastingpower towers

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Zolan, Alexander [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000326017604), Kler, Eugene [National Laboratory of the Rockies, Golden, CO (United States)], Hamilton, William [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000244654239), Sment, Jeremy [Sandia National Laboratories]. 2026-03-05. An Uncertainty-Informed and High-Fidelity Performance Forecasting Framework for Heliostat Fields. https://doi.org/10.2172/3022648

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