Tuning the current ratio of a CPV system to maximize the energy harvesting in a particular location

Victoria, Marta; Askins, Stephen; Núñez, Rubén; Domínguez, César; Herrero, Rebeca; Antón, Ignacio; Sala, Gabriel; Ruiz, José María Ruiz

doi:10.1063/1.4822221

Cited by 21 publications

(17 citation statements)

References 26 publications

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“…Even though both measurements were carried out at exactly the same irradiance B and spectral condition (fixed by SMR), most probably short circuit currents will not be equal because of its strong dependence on lens temperature [10][11][12] so the current correction included in eq. (7) would be needed.…”

Section: Indoors-outdoors V Oc Comparisonmentioning

confidence: 99%

Characterization of CPV arrays based on differences on their thermal resistances

Núñez¹,

Antón²,

Askins³

et al. 2014

AIP Conference Proceedings

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Thermal characterization of Concentrating Photovoltaics (CPV) modules and arrays is needed to determine their performance and modelling of energy forecast. Module-ambient thermal resistance is easily obtained from its definition but the cell-module thermal resistant needs to be estimated from indirect procedures, two of them are presented in this paper. In addition, an equivalent parameter is defined, the Concentrator Nominal Operating Module/Cell Temperature (CNOMT/CNOCT), the temperature at Concentrator Standard Operating Conditions (CSOC). Definitions and expression to relate (CNOMT/CNOCT) to thermal resistances are presented, plus several examples of estimations from real operating arrays.

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Section: Indoors-outdoors V Oc Comparisonmentioning

confidence: 99%

Characterization of CPV arrays based on differences on their thermal resistances

Núñez¹,

Antón²,

Askins³

et al. 2014

AIP Conference Proceedings

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“…For example, the cell temperature varies daily and seasonally, as the ambient temperature, wind speed, humidity, and irradiance change. In a CPV system, optical nonidealities and tracking errors can alter the spatial and spectral distribution of irradiance, and this can change the cell efficiency . Any system‐dependent factor that introduces performance variability could motivate an additional binning dimension, or other adjustments to the binning algorithm.…”

Section: Cell Temperature and Other System‐dependent Factorsmentioning

confidence: 99%

“…In a CPV system, optical nonidealities and tracking errors can alter the spatial and spectral distribution of irradiance, and this can change the cell efficiency. [18][19][20][21] Any system-dependent factor that introduces performance variability could motivate an additional binning dimension, or other adjustments to the binning algorithm. Here, we consider the additional variability created by cell temperature variations.…”

Section: Cell Temperature and Other System-dependent Factorsmentioning

confidence: 99%

Spectral binning for energy production calculations and multijunction solar cell design

García

McMahon

Habte

et al. 2017

Progress in Photovoltaics

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Currently, most solar cells are designed for and evaluated under standard spectra intended to represent typical spectral conditions. However, no single spectrum can capture the spectral variability needed for annual energy production (AEP) calculations, and this shortcoming becomes more significant for series-connected multijunction cells as the number of junctions increases. For this reason, AEP calculations are often performed on very detailed yearlong sets of data, but these pose 2 inherent challenges: (1) These data sets comprise thousands of data points, which appear as a scattered cloud of data when plotted against typical parameters and are hence cumbersome to classify and compare, and (2) large sets of spectra bring with them a corresponding increase in computation or measurement time. Here, we show how a large spectral set can be reduced to just a few "proxy" spectra, which still retain the spectral variability information needed for AEP design and evaluation. The basic "spectral binning" methods should be extensible to a variety of multijunction device architectures. In this study, as a demonstration, the AEP of a 4-junction device is computed for both a full set of spectra and a reduced proxy set, and the results show excellent agreement for as few as 3 proxy spectra. This enables much faster (and thereby more detailed) calculations and indoor measurements and provides a manageable way to parameterize a spectral set, essentially creating a "spectral fingerprint," which should facilitate the understanding and comparison of different sites. For design purposes, standard direct spectra can be developed for specific regions. For example, the ASTM G173 direct spectrum represents the spectral content of the direct spectra in the arid southwestern region of the United States.1,2 This is quite useful, because it ensures that a concentrator photovoltaics (CPV) system designed for a particular standard spectrum will be suitable for use in the corresponding geographic area.However, a single design spectrum does not convey any information about the variability of spectra at a particular site. This variability is crucial, because it affects the annual energy production (AEP), 3-13 and thereby the levelized cost of electricity of a system, and may also affect the design of multijunction cells for such systems. Here, we show how a set of~50 000 spectra representing spectral conditions over the course of a year can be reduced to a much smaller set of "proxy" spectra. Our results show that, with an effective binning process, a very small set of just 3 proxy spectra retains enough information about spectral variability to accurately compute and design for AEP.Such a small set of proxy spectra can greatly reduce the time needed for AEP calculations, enabling much more detailed and thorough investigations. Although in some cases it may be possible to use a full yearlong set of spectra, the time needed for computations can become prohibitive due to the large number of spectra (around 50 000 for 5-min time incremen...

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“…The results suggest that the CPV module optimization should increase top current for the four technologies and most of the sites, which can be made at the cell level or by means of the optics, as for example promoting short wavelength focusing or tuning the antireflective coating . The optimum top/mid current ratio under the reference spectrum must accomplish the following condition for a particular site.…”

Section: Global Analysis Of Spectral Influence For Several Kinds Of Tmentioning

confidence: 99%

Spectral study and classification of worldwide locations considering several multijunction solar cell technologies

Núñez

Jin

Victoria

et al. 2016

Progress in Photovoltaics

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Multi‐junction solar cells are widely used in high‐concentration photovoltaic systems (HCPV) attaining the highest efficiencies in photovoltaic energy generation. This technology is more dependent on the spectral variations of the impinging Direct Normal Irradiance (DNI) than conventional photovoltaics based on silicon solar cells and consequently demands a deeper knowledge of the solar resource characteristics. This article explores the capabilities of spectral indexes, namely, spectral matching ratios (SMR), to spectrally characterize the annual irradiation reaching a particular location on the Earth and to provide the necessary information for the spectral optimization of a MJ solar cell in that location as a starting point for CPV module spectral tuning. Additionally, the relationship between such indexes and the atmosphere parameters, such as the aerosol optical depth (AOD), precipitable water (PW), and air mass (AM), is discussed using radiative transfer models such as SMARTS to generate the spectrally resolved DNI. The network of ground‐based sun and sky‐scanning radiometers AErosol RObotic NETwork (AERONET) is exploited to obtain the atmosphere parameters for a selected bunch of 34 sites worldwide. Finally, the SMR indexes are obtained for every location, and a comparative analysis is carried out for four architectures of triple junction solar cells, covering both lattice match and metamorphic technologies. The differences found among cell technologies are much less significant than among locations. Copyright © 2016 John Wiley & Sons, Ltd.

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Tuning the current ratio of a CPV system to maximize the energy harvesting in a particular location

Cited by 21 publications

References 26 publications

Characterization of CPV arrays based on differences on their thermal resistances

Characterization of CPV arrays based on differences on their thermal resistances

Spectral binning for energy production calculations and multijunction solar cell design

Spectral study and classification of worldwide locations considering several multijunction solar cell technologies

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