Research Papers

Solar Field Optical Characterization at Stillwater Geothermal/Solar Hybrid Plant

[+] Author and Article Information
Guangdong Zhu

National Renewable Energy Laboratory,
15013 Denver West Parkway,
Golden, CO 80401
e-mail: Guangdong.Zhu@nrel.gov

Craig Turchi

National Renewable Energy Laboratory,
15013 Denver West Parkway,
Golden, CO 80401

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received July 26, 2016; final manuscript received December 2, 2016; published online January 27, 2017. Assoc. Editor: Wojciech Lipinski.The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Sol. Energy Eng 139(3), 031002 (Jan 27, 2017) (10 pages) Paper No: SOL-16-1342; doi: 10.1115/1.4035518 History: Received July 26, 2016; Revised December 02, 2016

Concentrating solar power (CSP) can provide additional thermal energy to boost geothermal plant power generation. For a newly constructed solar field at a geothermal power plant site, it is critical to properly characterize its performance so that the prediction of thermal power generation can be derived to develop an optimum operating strategy for a hybrid system. In the past, laboratory characterization of a solar collector has often extended into the solar field performance model and has been used to predict the actual solar field performance, disregarding realistic impacting factors. In this work, an extensive measurement on mirror slope error and receiver position error has been performed in the field by using the optical characterization tool called distant observer (DO). Combining a solar reflectance sampling procedure, a newly developed solar characterization program called firstoptic and public software for annual performance modeling called system advisor model (SAM), a comprehensive solar field optical characterization has been conducted, thus allowing for an informed prediction of solar field annual performance. The paper illustrates this detailed solar field optical characterization procedure and demonstrates how the results help to quantify an appropriate tracking-correction strategy to improve solar field performance. In particular, it is found that an appropriate tracking-offset algorithm can improve the solar field performance by about 15%. The work here provides a valuable reference for the growing CSP industry.

Copyright © 2017 by ASME
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Fig. 1

Schematic of mirror reflectance [13]

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Fig. 2

Distribution of solar specular reflectance (at 25 mrad and at a wavelength of 660 nm) among loops. Variability comes from measurement uncertainties and damage to some panels prior to construction.

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Fig. 3

Specular reflectance (at a wavelength of 660 nm) as a function of acceptance aperture size for four mirror panel samples

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Fig. 4

Fitted mirror specularity profile using a single-Gaussian approximation

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Fig. 5

A snapshot of distant observer (DO) optical characterization: raw photo (left) and scaled photo (right)

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Fig. 6

Rescaling of the raw image based on the target locations [40]

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Fig. 7

Receiver position error along x and z directions. Because these measurements were taken near the horizon, they represent a worst-case scenario that occurs during operation. Receiver position error during normal operating angles may be substantially less.

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Fig. 8

Slope error distribution over one collector module of a SkyTrough collector module (indexed by L5R2M10)

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Fig. 9

Slope error distribution attributes (mean value and RMS) for all sampled collector modules. Note that the mean value is dominated by the receiver's gravity-induced displacement, which is greatest at the low tracking angle while the DO measurements were conducted. Its impact on the collector performance is compensated by including a tracking-offset algorithm, to be discussed later.

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Fig. 10

Intercept factor calculation for all sampled collector modules

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Fig. 11

Intercept factor calculation for all sampled collector modules with and without tracking-offset algorithm

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Fig. 12

Incidence-angle modifier (IAM) curve: circles mark the predicted data points, and the line indicates the fitting function

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Fig. 13

Hourly SAM-predicted solar field thermal energy output for Stillwater based on measured parameters listed in Table 7




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