Direct Beam and Diffuse Spectral Irradiance Measurements in a Nordic Country Analyzed With the Average Photon Energy Parameter

Riedel, Nicholas; Thorseth, Anders; Lancia, Adrian Alejo Santamaria; Thorsteinsson, Sune; Poulsen, Peter Behrensdorff; Iandolo, Beniamino; Davidsen, Rasmus Schmidt; Benatto, Gisele Alves dos Reis

doi:10.1109/pvsc.2018.8548240

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…The global horizontal spectrum is thus calculated by the summation of the diffuse and cosine‐corrected beam spectral components as described in Riedel. 33 …”

Section: Methodsmentioning

confidence: 99%

“…Spectra were measured every 30 min with a wavelength interval of around 0.43 nm, and the wavelength range is around 282 to 1119 nm.The instruments used include an EKO MS-711 (direct normal irradiance) mounted on a dual axis tracker and an MS-711 that is continuously shaded by a 5 field of view shadow ball. The global horizontal spectrum is thus calculated by the summation of the diffuse and cosine-corrected beam spectral components as described in Riedel 33.…”

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confidence: 99%

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Representative identification of spectra and environments (RISE) using k‐means

Looney

Liu

Classen

et al. 2020

Progress in Photovoltaics

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Spectral differences affect solar cell performance, an effect that is especially visible when comparing different solar cell technologies. To reproduce the impact of varying spectra on solar cell performance in the lab, a unique classification of spectra is needed, which is currently missing in literature. The most commonly used classification, average photon energy (APE), is not unique, and a single APE value may represent various spectra depending on location. In this work, we propose a classification method based on an iterative use of the k-means clustering algorithm. We call this method RISE (Representative Identification of Spectra and the Environment). We define a set of 18 spectra using RISE and reproduce the spectral impact on energy yield for various solar cell technologies and locations. We explore effects on yield for commercially available solar cell technologies (Si and CdTe) in four locations: Singapore (fully humid equatorial climate), Colorado (cold arid), Brazil (warm, humid, and subtropical), and Denmark (fully humid warm temperature). We then reduce our findings to practice by implementing the spectrum set into an LED current-voltage (IV) tester. We verify our performance predictions using our set of representative spectra to reproduce energy yield differences between Si solar cells and CdTe solar cells with an average error of less than 1.5 ± 0.5% as compared to over 5% when using standard testing conditions.

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“…The global horizontal spectrum is thus calculated by the summation of the diffuse and cosine‐corrected beam spectral components as described in Riedel. 33 …”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Representative identification of spectra and environments (RISE) using k‐means

Looney

Liu

Classen

et al. 2020

Progress in Photovoltaics

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“…These spectrometers measure light between 300 and 1100 nm with a full-width-half-maximum (FWHM) spectral resolution of 7 nm in 1-nm steps. The spectral measurement data are recorded in 5-min intervals [34]. Likewise, spectral albedo data are recorded for the test site using a handheld spectroradiometer specified for a wavelength up to 900 nm.…”

Section: Solar Resource and Field Measurement Datamentioning

confidence: 99%

Simulation of Colored BIPV Modules Using Angular-Dependent Spectral Responsivity

Clasing

Riedel

Reiners

et al. 2023

IEEE J. Photovoltaics

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“…The third MS711 records the direct normal spectral irradiance DNIλ. This instrument is installed on a dual-axis tracker and has a 5° field-of-view (FOV) collimation tubeas used in [11]. The spectral albedo αλ is calculated as the ratio of the downward facing RHIλ and upward facing GHIλ measurements:…”

Section: A Field Measurementsmentioning

confidence: 99%

Spectral Albedo in Bifacial Photovoltaic Modeling: What can be learned from Onsite Measurements?

Riedel-Lyngskar

Ribaconka

Pó³

et al. 2021

2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)

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This contribution reports on a yearlong spectral albedo measurement campaign performed in Roskilde, Denmark. Four albedo scenarios are monitored using three sensor types. The ground surfaces include green grass, dry grass, gravel, and snow all of which have been monitored with albedometers based on spectroradiometers, silicon-pyranometers, and thermopile pyranometers. Implications of using the various albedo data sources/assumptions in bifacial PV modeling are assessed with the spectrally weighted bifacial energy gain (BEG). We find that BEG differs by as much as 3% with the different albedo sensors and BEG can deviate by as much as 7% from the ground truth when an incorrect static spectral albedo assumption is used. Finally, the spectral mismatch factor (SMM) is calculated to summarize rear plane of array (POA) spectral shifts. Our measurements show midday backside POA spectral shifts as high as 25% for Silicon bifacial PV devices mounted on single axis trackers above grass.

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Direct Beam and Diffuse Spectral Irradiance Measurements in a Nordic Country Analyzed With the Average Photon Energy Parameter

Cited by 4 publications

References 8 publications

Representative identification of spectra and environments (RISE) using k‐means

Representative identification of spectra and environments (RISE) using k‐means

Simulation of Colored BIPV Modules Using Angular-Dependent Spectral Responsivity

Spectral Albedo in Bifacial Photovoltaic Modeling: What can be learned from Onsite Measurements?

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