Spectral binning for energy production calculations and multijunction solar cell design

Abstract

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...

Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations

“…A method to estimate the yearly energy yield was proposed by García et al, where spectra are grouped or binned according to their spectral characteristics and then all spectra in the same group or bin are averaged to obtain a few representative spectra 2 . The binning method is applied here and compared with a clustering technique where spectra are classified on the basis of their Euclidean distances in a highly multidimensional vector space defined by the number of components of the spectra.…”

“…As a consequence, there is almost no margin left to compete in terms of solar cell costs, and demand for emerging PV technologies can only be expected if these can exceed the efficiency of silicon single junctions. The only proven method to significantly increase the efficiency beyond the limits of conventional silicon technology is the use of multijunction devices, used either with or without optical concentration, but there still exists uncertainty about how the changes of the solar spectrum as a function of time affect the energy production of multijunction solar cells 2 – 4 .…”

Dependence of Multijunction Optimal Gaps on Spectral Variability and Other Environmental and Device Parameters

“…The only proven method to significantly increase the efficiency beyond the limits of conventional silicon technology is the use of multijunction devices, used either with or without optical concentration. But there still exists uncertainty about how the changes of the solar spectrum as a function of time affect the energy production of multijunction solar cells [2]. As reviewed by Kurtz et al, there have been many previous efforts to optimize solar cell band gaps, but in all cases the target of the optimization was aimed at maximizing the efficiency under standard conditions, and not the yearly energy production [3], [4].…”

Fine-Tuning of Multijunction Solar Cells: An In-Depth Evaluation

“…These findings may constitute a paradigm shift in the way advanced MJ solar cells should be designed: rather than aiming at the highest solar to electricity conversion efficiency considering a unique reference solar spectrum (typically ASTM G173 AM 1.5 solar spectrum [9]), advanced MJ solar cells should be tailored to ensure a higher energy output over extended periods of time. This question was recently investigated by several authors [10]- [12]). Yandt et al considered representative spectrum, using AM as a proxy for describing spectral changes, to tailor MJ cells comprising up to 10 subcells [10].…”

“…They showed a significant improvement in the energy output achievable, especially for PV cells involving a high number of subcells and for high-latitude locations. Garcia et al suggested a binning algorithm aiming to generate "proxy" spectra accounting for the spectral variability at a specific location [12]. This work aims at shedding light on the ability of fine-tuning to better harness solar energy, considering the most significant atmospheric parameters.…”