Bandgap engineering of multijunction photovoltaic cells for spectral albedo of natural surfaces

From Appropedia

Sunhusky.png Michigan Tech's Open Sustainability Technology Lab.

Wanted: Students to make a distributed future with solar-powered open-source 3-D printing and recycling.
Contact Dr. Joshua Pearce - Apply here

MOST: Projects & Publications, Methods, Lit. reviews, People, Sponsors, News
Updates: Twitter, YouTube


This page describes selected literature available on Bandgap engineering of multijunction photovoltaic cells for spectral albedo of natural surfaces.[edit | edit source]

Variation in Spectral Irradiance and the Consequences for Multi-junction Concentrator Photovoltaic Systems [1][edit | edit source]

Abstract: The most fundamental figure of merit for a solar collector is its power rating under a standard solar spectrum. For concentrator systems that employ highly efficient multi-junction solar cells, the rating of the panel becomes sensitive to the spectrum of the direct beam sunlight. By considering typical spectral conditions at a measurement site, we show that the power rating can vary by up to 16%. Further, we consider to what extent a single reference spectrum can be used to characterize the irradiance at a particular location. Electrical energy yields produced using both synthesized and standard reference spectra are compared, with standardized references shown to be unsuited for accurate energy yield predictions under realistic spectral conditions.

What Is an Air Mass 1.5 Spectrum? [2][edit | edit source]

Abstract: The origin of the AM 1.5 spectra, how they are related to actual outdoor spectral distributions, and the implications for outdoor PV (photovoltaic) performance predictions are explained. It is pointed out that the AM 1.5 spectra provide a reference point corresponding to a particular set of atmospheric conditions and a specific air mass. One can expect variations in outdoor PV device performance for different atmospheric conditions and air masses. The uncertainty in using AM 1.5 spectra to predict field performance depends on the particular PV device design and climate. The wavelength distribution of photon flux varies with respect to conditions such as water vapor and air mass, and this in turn influences current densities in PV devices, depending on such device characteristics as bandgap(s). Therefore, PV device design (e.g. optimization) should be based on a range of spectra representing various atmospheric conditions and air masses.

The Influence of Spectral Solar Irradiance Variations on the Performance of Selected Single-junction and Multijunction Solar Cells [3][edit | edit source]

Abstract: The sensitivities of selected single-junction and multijunction cells to variations in solar irradiance are presented. The one-sun spectral irradiance is varied as a function of air mass, optical aerosol depth (turbidity) and amount of precipitable water vapor for direct-normal and global-normal geometries. Several devices, including one-, two- and three-junction devices with low and high bandgaps and either series- or independent-connection schemes, were investigated. The effects of air mass and turbidity on the consistency of high-bandgap device performance are shown to be greater than the effect of precipitable water vapor. Low-bandgap devices are less affected by variations in air mass and turbidity, but are more sensitive to high water-vapor conditions. The efficiency gained by redesigning a multijunction device for the latitude at which it is expected to be used is shown to be less than about 3% (relative).

Spectral effects on PV-device rating [4][edit | edit source]

Abstract: A recently developed spectral model “SEDES2” is applied to study the effect of variations in solar spectral irradiance on the efficiency of seven particular solar cells. As a new feature, SEDES2 calculates hourly solar spectral irradiance for clear and cloudy skies from readily available site-specific meteorological data. Based on these hourly spectra, monthly and yearly efficiencies for the solar cells are derived. As a key result the efficiencies of amorphous silicon cells differ by 10% between winter and summer months because of spectral effects only. A second intention of this study is to analyse the sensitivity of power and energy rating methods to spectral irradiance but also to total irradiance and cell temperature. As an outcome, a multi-value energy rating scheme applying the concept of “critical operation periods” is proposed.

Outdoor measurement of multi-junction solar cells and modules based on the reference sunlight method [5][edit | edit source]

Abstract: To evaluate the indoor measurement accuracy of multi-junction solar cells and modules experimentally, we have successfully used natural sunlight whose spectral irradiance is close to the reference sunlight. The reference sunlight for evaluating the performance of solar cells can be realized by choosing natural sunlight incident on a sun-facing 37° tilted surface at air mass of 1.5, when the atmosphere has turbidity of 0.27, precipitable water of 1.42 cm and so on. The measurement of atmospheric parameters was performed based mainly on the measurement of direct solar spectral irradiance and additionally on the measurements of irradiance and illuminance. In addition, measured values were compared with aerological observation data. The comparison of I–V characteristics measured outdoors and indoors shows that our pulsed-light solar simulator is capable of measuring multi-junction amorphous silicon alloy solar cells and modules accurately enough when its irradiance is set by an amorphous silicon reference cell. This fact is also confirmed by the examination of spectral irradiance on the test plane of the solar simulator. Moreover, we showed that irradiance (or illuminance) can be used instead of atmospheric turbidity and also water vapor content on a surface of the Earth can be used as a rough estimate of precipitable water for finding the reference sunlight conditions.

Photovoltaic module and array performance characterization methods for all system operating conditions[6][edit | edit source]

Absract:This paper provides new test methods and analytical procedures for characterizing the electrical performance of photovoltaic modules and arrays. The methods use outdoor measurements to provide performance parameters both at standard reporting conditions and for all operating conditions encountered by typical photovoltaic systems. Improvements over previously used test methods are identified, and examples of the successful application of the methodology are provided for crystalline- and amorphous-silicon modules and arrays. This work provides an improved understanding of module and array performance characteristics, and perhaps most importantly, a straight-forward yet rigorous model for predicting array performance at all operating conditions. For the first time, the influences of solar irradiance, operating temperature, solar spectrum, solar angle-of-incidence, and temperature coefficients are all addressed in a practical way that will benefit both designers and users of photovoltaics.

Lattice-mismatched approaches for high-performance, III-V photovoltaic energy converters [7][edit | edit source]

Abstract:We discuss lattice-mismatched (LMM) approaches utilizing compositionally step-graded layers and buffer layers that yield III-V photovoltaic devices with performance parameters equaling those of similar lattice-matched (LM) devices. Our progress in developing high-performance, LMM, InP-based GaInAs/InAsP materials and devices for thermophotovoltaic (TPV) energy conversion is highlighted. A novel, monolithic, multi-bandgap, tandem device for solar PV (SPV) conversion involving LMM materials is also presented along with promising preliminary performance results.

Spectrally-Selective Photonic Structures for PV Applications[8][edit | edit source]

Abstract:Abstract: We review several examples of how spectrally-selective photonic structures may be used to improve solar cell systems. Firstly, we introduce different spectrally-selective structures that are based on interference effects. Examples shown include Rugate filter, edge filter and 3D photonic crystals such as artificial opals. In the second part, we discuss several examples of photovoltaic (PV) concepts that utilize spectral selectivity such as fluorescence collectors, upconversion systems, spectrum splitting concepts and the intermediate reflector concept. The potential of spectrally selective filters in the context of solar cells is discussed.

Efficiency of multijunction photovoltaic systems[9][edit | edit source]

Abstract:In this paper, an expression for the PV system efficiency is derived that can be used in conjunction with measured device performance and detailed numerical modeling to analyze PV system performance. Such an analysis will help identify design trade-offs and also help to identify which system and cell design changes will be of greatest benefit to the enhancement of PV system performance.

Four-junction spectral beam-splitting photovoltaic receiver with high optical efficiency.[10][edit | edit source]

Abstract:This paper presents a spectral beam splitting arrangement providing the basis for a four-junction photovoltaic receiver with virtually ideal band gap combination. A light trapping assembly in form of a 45 parallelepiped applying two spectrally selective beam splitters and three solar cells realizes an approach to reach very high solar-electrical conversion efficiency. The spectral beam splitters developed and used in this arrangement show nearly ideal broadband characteristics to split the solar spectrum into three parts. The solar cell selection includes III-V semiconductors and silicon. They were all fabricated at Fraunhofer ISE and assembled in the receiver setup. The experimental setup demonstrated an outdoor efficiency of more than 34 %.

Interdisciplinary applications of a versatile spectral solar irradiance model: A review.[11][edit | edit source]

Abstract:A detailed review of different applications that have already been investigated with SMARTS, a versatile spectral solar irradiance model, is proposed here. This review provides examples of applications in many different disciplines, for which recent developments are discussed. Three main types of applications are considered, depending on their spectral range. Purely spectral applications encompass the determination of atmospheric constituents, the performance testing of spectroradiometers, and the improvement and validation of reference spectra for the rating of photovoltaic or glazing systems, or for new standards development in the field of weathering and material degradation. Narrow-band applications include the determination of different UV fluxes and of the UV index, and the prediction of illuminance on any horizontal or tilted surface, of the luminous efficacy of direct, diffuse or global radiation, of the photosynthetically active radiation, and of the irradiance transmitted by different bandpass filters. Finally, some specific broadband applications are reviewed: mesoscale predictions of radiation fluxes, evaluation of circumsolar effects in pyrheliometers, performance assessment of broadband radiation models, and turbidity determination from broadband irradiance data.

Solar radiation modeling and measurements for renewable energy applications.[12][edit | edit source]

Abstract:Measurement and modeling of broadband and spectral terrestrial solar radiation is important for the evaluation and deployment of solar renewable energy systems. We discuss recent developments in the calibration of broadband solar radiometric instrumentation and improving broadband solar radiation measurement accuracy. An improved diffuse sky reference and radiometer calibration and characterization software for outdoor pyranometer calibrations are outlined. Several broadband solar radiation model approaches, including some developed at the National Renewable Energy Laboratory, for estimating direct beam, total hemispherical and diffuse sky radiation are briefly reviewed. The latter include the Bird clear sky model for global, direct beam, and diffuse terrestrial solar radiation; the Direct Insolation Simulation Code (DISC) for estimating direct beam radiation from global measurements; and the METSTAT (Meteorological and Statistical) and Climatological Solar Radiation (CSR) models that estimate solar radiation from meteorological data. We conclude that currently the best model uncertainties are representative of the uncertainty in measured data.

Array Performance Characterization and Modeling for Real-Time Performance Analysis of Photovoltaic Systems.[13][edit | edit source]

Abstract:Improvements in the methods used for photovoltaic (PV) system design, performance rating, and long-term monitoring are needed by the rapidly growing industry, as well as by the U.S. Department of Energy in evaluating progress by solar technology development initiatives. This paper describes an improved model for rating and monitoring PV array performance, discusses initial results from an outdoor laboratory designed to assist industry in optimizing system components and integration, and provides a brief discussion of the system performance metrics currently being used by the PV community.

Solar spectral irradiance under overcast skies [solar cell performance effects.[14][edit | edit source]

Abstarct:The authors examine a particular aspect of cloudy-sky conditions that affects PV (photovoltaic) device performance, i.e. the solar spectral distribution under an overcast sky with respect to the spectral response of PV devices. The approach is to analyze several thousand measured spectral irradiance data that illustrate spectral shifts under cloud cover. The questions raised are how the transmission of clouds deviates from an assumed neutral density filter and whether the standard reference spectrum applied by the PV community to design and performance prediction is applicable for cloudy climates. Comparing the measurements with clear-sky simulations, the authors established a correlation between cloud thickness and alterations in the relative spectral transmission. Because of the approach used, these observed effects are true only for the statistical mean of a sufficiently large number of measurements

High efficiency photovoltaic conversion with spectrum splitting on GaAs and Si cells located in light confining cavities.[15][edit | edit source]

Abstract:A high-efficiency photovoltaic conversion system based on spectrum splitting of concentrated light to fall on GaAs and Si cells placed inside light confining cavities is described and experimental results are reported: 29.4% at 180 suns (near AM1.5 direct spectrum), 22% contributed by the GaAs cell. The short-circuit current gain derived from the use of the cavities is 7-8% (relative increase). However, the system is believed to have immediate potential to achieve as much as 32% efficiency. Benefits compared to systems based on stacked cells are pointed out: (a) conventional cells based on developed fabrication techniques can be used, with minor design changes, (b) cell design can be independently optimized with the aim of achieving maximum efficiency, (c) cells can operate at different concentrations, (d) electrical power can be extracted independently from each cell, and (e) it is easy to make the electrical connections to each cell.

Estimating potential photovoltaic yield with r.sun and the open source Geographical Resources Analysis Support System.[16][edit | edit source]

Abstract:he package r.sun within the open source Geographical Resources Analysis Support System (GRASS) can be used to compute insolation including temporal and spatial variation of albedo and solar photovoltaic yield. A complete algorithm is presented covering the steps of data acquisition and preprocessing to post-simulation whereby candidate lands for incoming solar farms projects are identified. The optimal resolution to acquire reliable solar energy outputs to be integrated into PV system design software was determined to be 1 square km. A case study using the algorithm developed here was performed on a North American region encompassing fourteen counties in South-eastern Ontario. It was confirmed for the case study that Ontario has a large potential for solar electricity. This region is found to possess over 935,000 acres appropriate for solar farm development, which could provide 90 GW of PV. This is nearly 60% of Ontario’s projected peak electricity demand in 2025. The algorithm developed and tested in this paper can be generalized to any region in the world in order to foster the most environmentally-responsible development of large-scale solar farms.

Prediction of energy effects on photovoltaic systems due to snowfall events.[17][edit | edit source]

Abstract:The accurate prediction of yields from photovoltaic systems (PV) is critical for their proper operation and financing, and in northern latitudes the effects of snowfall on yield can become significant. This work provides methods for identifying snowfall effects from commonly collected performance data, and recommends a model to allow for prediction of these effects based solely on meteorological time series. The model was validated with data from two large-scale (>;8MW) operational PV plants. For the low tilt angles most affected by snowfall, this analysis was able to accurately predict both daily and mean values of snow effects. This methodology will enable system operators to utilize performance data to accurately identify and predict snowfall losses, and will assist system designers to optimize for the effects of snowfall on new system designs.

Quality control of solar radiation data: Present status and proposed new approaches.[18][edit | edit source]

Abstarct:During the past few decades, there has been a continual rise in interest in passive and active solar energy uses, not only in the governmental and commercial sectors, but also within the private sector. There is thus a need for taking measurements of solar irradiation and creating local and regional databases of irradiation and synoptic (meteorological) information. However, there is no guarantee of the quality of the data collected, as often due care is not exercised with respect to quality control of the measured dataset.

This article reviews the presently available procedures for quality assessment of the solar irradiation data. Furthermore, we propose a set of stringent physical and statistical measures to create a semi-automated procedure that is based on the creation of an envelope in the clearness index–diffuse to global irradiance ratio domain. The procedure is very general in nature and may be used with equal effectiveness for any terrestrial dataset.

Effects of snow physical parameters on spectral albedo and bidirectional reflectance of snow surface.[19][edit | edit source]

Abstract:Observations of spectral albedo and bidirectional reflectance in the wavelength region of lambda = 0.35-2.5 mu m were made together with snow pit work on a flat snowfield in eastern Hokkaido, Japan. The effects of snow impurities, density, layer structure, and grain size attained by in situ and laboratory measurements were taken into account in snow models for which spectral albedos were calculated using a multiple-scattering model for the atmosphere-snow system. Comparisons of these theoretical albedos with measured ones suggest that the snow impurities were concentrated at the snow surface by dry fallout of atmospheric aerosols. The optically equivalent snow grain size was found to be of the order of a branch width of dendrites or of a dimension of narrower portion of broken crystals. This size was smaller than both the mean grain size and the effective grain size obtained from micrographs by image processing. The observational results for the. bidirectional reflection distribution function (BRDF) normalized by the radiance at the nadir showed that the anisotropic reflection was very significant in the near-infrared region, especially for lambda > 1.4 mu m, while the visible normalized BRDF (NBRDF) patterns were relatively flat. Comparison of this result with two kinds of theoretical NBRDFs, where one having been calculated using single-scattering parameters by Mie theory and the other using the same parameters except for Henyey-Greenstein (HG) phase function obtained from the same asymmetry factor as in the Mie theory, showed that the observed NBRDF agreed with the theoretical one using the HG phase function rather than with that using the Mie phase function, while the albedos calculated with both phase functions agreed well with each other.

 - Study quantifies the effects of snow physical parameters in Japan for the 0.35 - 2.5 microns spectral range.
 - Spectral albedo observed using a grating spectrometer, FieldSpec FR (ASD Inc, USA)with spetral resolution 3 - 10 nm.


Abstract: The spectral albedo of the earth's surface, i.e. the ratio between spectral irradiance reflected by the ground to all directions and global irradiance, was measured by a spectroradiometer in the UV and visible region from 290 nm to 800 nm with a spectral resolution of 1.5 nm at steps of 2 nm in the UV (290–400 nm) and 10 nm in the visible (400–800 nm) region. The measurements were performed over bare fertile soil, sand at the beach, concrete (autobahn) and snow as well as over different types of vegetation (grass, oats, rye, sugar-beet, stubble). As the albedo increases with increasing wavelengths for most types of surfaces considered, it is smaller in the UV than in the visible region. In the UVB region (λ < 315 nm) the measured albedo is as small as 0.016-0.017 over vegetation, 0.04-0.05 over bare fertile soil, 0.07-0.10 over concrete (“autobahn”) and 0.62-0.76% over polluted snow with a small wavelength dependence. A somewhat higher albedo occurs in the UVA region (315 < λ < 400 nm) with values ranging from 0.02 over vegetation to 0.05 to 0.08 over bare soil. The albedo over dry bright sand, which is typically found at the beach, is significantly higher (0.14 at 300 nm to 0.24 at 400 nm) than over other snow-free surfaces, thus leading to an enhanced dose of biologically effective radiation at the beach.

Experimental study of variations of the solar spectrum of relevance to thin film solar cells[21].[edit | edit source]

Abstract:The influence of variations in the incident solar spectrum on solar cells is often neglected. This paper investigates the magnitude of this variation and its potential influence on the performance of thin film solar cells in a maritime climate. The investigation centres on the analysis of a large number of measurements carried out in Loughborough, UK, at 10 min intervals over a period of 30 months. The magnitude of the spectral variation is presented both on a daily and a seasonal basis. Of the different thin film materials studied, amorphous silicon is shown to be the most susceptible to changes in the spectral distribution, with the “useful fraction” of the light varying in the range +6% to −9% of the annual average, with the maximum occurring in summer time.

A method for modeling the current–voltage curve of a PV module for outdoor conditions,” Progress in Photovoltaics: Research and Applications.[22][edit | edit source]

Abstract:A method has been developed for modeling the current–voltage curve of a photovoltaic (PV) module for outdoor conditions. An indoor characterization procedure determines a PV module's temperature and irradiance correction factors, which are used in conjunction with equations to translate a reference curve to outdoor conditions of PV module temperature and irradiance. A PV technology's spectral response characteristics are accommodated in the equation for irradiance. The modeled and measured energy is compared for a one-year period for seven PV modules of different technologies. The results validate the method's use for modeling the hourly performance of PV modules, and for modeling daily energy production for PV module energy rating purposes.

The rating of photovoltaic performance.[23][edit | edit source]

Abstract:— The electrical performance of photovoltaic (PV)cells, modules, and systems are rated in terms of their maximum electrical power with respect to a total irradiance, temperature, and spectral irradiance. The impact of the reference conditions, measurement procedures, and equipment on the performance rating is discussed.

Performance of thin film PV modules.[24][edit | edit source]

Abstract:Estimation of the electrical yield of a PV module is expected to be a more useful predictor of performance for installers than Wp alone. A method for the energy rating of PV modules based on performance surfaces under development at the ESTI laboratory uses the module temperature and incident irradiance as independent variables and has been successful in prediction of real energy production for crystalline Si modules. However, it was found to be more difficult to accurately predict the performance of thin film modules and it was therefore necessary to explore the reasons. One potentially significant parameter not included in the standard performance surface is the effect of spectral variations, and this has been studied during indoor and outdoor testing on CIS and a-Si modules. The outdoor measurements were performed on a tracker so as to preclude angle of incidence effects. Module I–V curves and the solar spectrum were measured at frequent intervals over a range of air mass values during the course of a number of days. A crystalline Si reference device and a pyranometer were used as irradiance sensors in order to explore the effect of the choice of reference device used. The spectral mismatch factor is calculated from measurements of the solar spectrum and device spectral responses and is applied to correct the individual module measurement points.

The dependence on air mass, i.e., the details of the solar spectrum of these devices, has also been shown, so employing only total irradiance and device temperature may not be sufficient when an energy rating is being made. This effect is most pronounced for the a-Si module tested, for which a significant part of this dependence was corrected by the application of the relevant mismatch factors.

Temperature dependence of photovoltaic cells, modules and systems.'[25][edit | edit source]

Abstract:Photovoltaic (PV) cells and modules are often rated in terms of a set of standard reporting conditions defined by a temperature, spectral irradiance and total irradiance. Because PV devices operate over a wide range of temperatures and irradiances, the temperature and irradiance-related behavior must be known. This paper surveys the temperature dependence of crystalline and thin-film, state-of-the-art, research-size cells, modules and systems measured by a variety of methods. The various error sources and measurement methods that contribute to cause differences in the temperature coefficient for a given cell or module measured with various methods are discussed

Theoretical analysis of the optimum energy band gap of semiconductors for fabrication of solar cells for applications in higher latitudes locations.[26][edit | edit source]

Abstract:In this work some results of theoretical analysis on the selection of optimum band gap semiconductor absorbers for application in either single or multijunction (up to five junctions) solar cells are presented. For calculations days have been taken characterized by various insolation and ambient temperature conditions defined in the draft of the IEC 61836 standard (Performance testing and energy rating of terrestrial photovoltaic modules) as a proposal of representative set of typical outdoor conditions that may influence performance of photovoltaic devices. Besides various irradiance and ambient temperature ranges, these days additionally differ significantly regarding spectral distribution of solar radiation incident onto horizontal surface. Taking these spectra into account optimum energy band gaps and maximum achievable efficiencies of single and multijunction solar cells made have been estimated. More detailed results of analysis performed for double junction cell are presented to show the effect of deviations in band gap values on the cell efficiency.

- Study discusses how to achieve maximum conversion efficiency for a specified solar spectrum absorber material with optimum band gap for solar cell fabrication.
 - Proper matching of the solar cell absorber band gap to light spectrum as being fundamental for efficient energy conversion.
 - Article also discusses the application of graded gap semiconductors for band gap engineering.
 - 3rd generation solar cells aiming to obtain structures with higher than one QE due to the introduction of the band gap multiple intermediate bands (IB).
 - It is expected that in such structures photons may generate more than one electron–hole pair.
 - Conclusions.
 - The IEC 60904-3 standard with recommended AM1.5 solar spectrum distribution seems to be nonsuitable for performance prediction of multijunction solar cells 
  for terrestrial applications. 
 - It is as well nonsuitable in the case of single junction devices with energy band gap significantly different from the optimum 1.39 eV value, e.g.,a-Si cells.. 
 - Presented results merely serve only as a useful guide to the range of band gap values of interest since calculations are based on simplified cell model.

A practical method for the energy rating of c-Si photovoltaic modules based on standard tests.[27][edit | edit source]

Abstract:The performance of a photovoltaic module at Standard Test Conditions (STC) is valuable for comparing the peak performance of different module types. It does not, however, give enough information to accurately predict how much energy a module will deliver when subjected to real operating conditions. There are several proposals for an energy rating for PV modules which attempt to account for the varying operating conditions that one encounters in the field. In this paper, we present an approach with the emphasis on simplicity and practicality that incorporates existing standard measurements to determine the energy output as a function of global in-plane irradiance and ambient temperature. The method is applied to crystalline Si modules and tested with outdoor measurements, and a good accuracy of prediction of energy production is observed. Finally, a proposal is made for a simple Energy Rating labeling of PV modules

Modelling long-term module performance based on realistic reporting conditions with consideration to spectral effects.[28][edit | edit source]

Abstract:A model for the annual performance of different module technologies is presented that includes spectral effects. The model is based on the realistic reporting conditions but also allows for secondary spectral effects, as experienced by multi-junction devices. The model is validated against measurements taken at CREST and shows a good agreement for all devices. Combining this relatively simple model with ASPIRE, a spectral irradiance model based on standard meteorological measurements, allows the translation to other locations. The method is applied to measurements of different devices deployed in Loughborough University and the significance of certain effects is discussed.

Experimental solar spectral irradiance until 2500 nm: results and influence on the PV conversion of different materials.[29][edit | edit source]

Abstract:In this work, results are presented concerning solar spectral irradiance measurements performed in Madrid in the wavelength range 250–2500 nm, that is, extending the spectral range far away from the wavelengths where PV semiconductors are active. These data were obtained considering a horizontal receiver surface during selected clear days covering the four seasons of the year. PV materials having different spectral responses (m-Si, a-Si, CIGS, CdTe) have been considered to calculate spectral factors (SF) taking as reference the standard solar spectrum AM1.5 defined in standard IEC 60904-3. From these SFs, the influence of natural solar spectral variations in PV conversion has been established. It is shown, for example, that PV technologies based on a-Si are highly favored, from the spectral point of view, in spring–summer compared to other technologies having broader spectral responses, which are more favored in autumn–winter. From the experimental measured solar spectra, we have calculated Weighed Solar Spectra (WSS) corresponding to the four seasons of the year and also to the whole year. The WSS represents, for a certain period of time, the solar spectrum weighed over the irradiance level. SFs have been calculated for different WSSs showing spectral gains for the four PV materials during almost the full year. Otherwise, it is also shown in this work how the near-IR part of the solar spectrum affects the evaluation of the solar resource as a whole when reference solar cells made of different PV materials are used. For typical m-Si, a-Si, CIGS, and CdTe solar cells, the ratio of Isc over global irradiance is not constant along a given day showing variations that depend on the season and on the PV material considered.

 - Study presents the spectral responses of the different PV materials; m-Si, a-Si, CIGS and CdTe using experimental solar spectra from 250 - 2500 nm
 - most studies limit themselves to the 300 - 1100 nm spectral range.
 - Data is for 1 year period covering the four seasons in Madrid Spain.
 - The study compares the short circuit current, Isc data from literature with corresponding integrated experimental solar irradiance (250 - 2500 nm) 
  data  for the four commercial PV cells .
 - Solar spectral radiance measurements were performed using a spectroradiometer MONOLIGHTTM model with 1 nm resolution and 100 - 300 s scan time.
 - Two detectors used, the Si (250 - 1095 nm wavelength range) and InGaAs (for 1095 - 2500 nm range) thermoelectrically cooled photodiodes.
 - All measurements were rooftop, 620 m above sea level, on clear days.
 - Study concludes that PV conversion efficiency of semiconductors is seasonal dependent due to natural solar spectrum variations:
 - a-Si and CdTe (narrow spectral response) - suitable for summer time , and m-Si and CIGS (wide spectral response) - winter time.  
 - Natural solar spectral variations cause a non-linearity of Iscvs integrated global irradiance that can lead to some errors if spectral corrections are not made.

On the importance of considering the incident spectrum when measuring the outdoor performance of amorphous silicon photovoltaic devices.[30][edit | edit source]

Abstract:Conventional measurement practice for the outdoor performance evaluation of solar cells does not make use of the complete spectrum, relying instead on the total irradiance as measured, say, with a pyranometer. In this paper it is shown that this can result in significant errors for solar cells having wide band gaps, in particular, for amorphous silicon solar cells. Two effects are investigated. The first relates to quantifying the typical errors associated with instantaneous measurements; what one might term the calibration of devices. The second relates to quantifying the impact of neglecting variations in the spectrum on the estimation of the annual energy production. It is observed that the fraction of the spectrum falling in the spectrally useful range for amorphous silicon can vary by as much as +10% to −15% with respect to standard test conditions at the test site used in this study, which translates directly into performance variations of similar magnitude. The relationship between changes due to spectral variations as opposed to variations in device temperature is also investigated. The results show that there is a strong case for investigating spectral effects more thoroughly, and explicitly including the measurement of the spectral distribution in all outdoor performance testing.

 - Article investigates the error associated with neglecting spectral variations in the outdoor calibration of PV devices (i.e. the instantaneous response), and
 the errors associated with the estimation of annual energy yield.
 - Data collected over 5 yrs from 300 - 1700 nm in 10 nm steps at Loughborough, UK.
 - Srong correlation with AM and the degree of cloud cover demonstrated, and can affect instantaneous performance calibration by up to 20% depending on the 
    time of    day and the time of year and climatic conditions.
 - There is a seasonal variation in the incident energy distribution that is strong enough to explain the seasonal performance of a-Si devices in a maritime,
    high latitude climate.
 - Spectral effects sometimes confused with other factors such as changes in device temperature.'
 - Study concludes that a more thorough understanding of the performance of a-Si devices is only possible if spectral data are available.
 - article less relevant to our research focus since it does not look at bandgap realted issues.

A Model for the Spectral Albedo of Snow. I: Pure Snow.[31][edit | edit source]

Abstract:We present a method for calculating the spectral albedo of snow which can be used at any wavelength in the solar spectrum and which accounts for diffusely or directly incident radiation at any zenith angle. For deep snow, the model contains only one adjustable parameter, an effective grain size, which is close to observed grain sizes. A second parameter, the liquid-equivalent depth, is required only for relatively thin snow.

In order for the model to make realistic predictions, it must account for the extreme anisotropy of scattering by snow particles. This is done by using the “delta-Eddington” approximation for multiple scattering, together with Mie theory for single scattering.

The spectral albedo from 0.3 to 5 μm wavelength is examined as a function of the effective grain size, the solar zenith angle, the snowpack thickness, and the ratio of diffuse to direct solar incidence. The decrease in albedo due to snow aging can be mimicked by reasonable increases in grain size (50–100 μm for new snow, growing to 1 mm for melting old snow).

The model agrees well with observations for wavelengths above 0.8 μm. In the visible and near-UV, on the other hand, the model may predict albedos up to 15% higher than those which are actually observed. Increased grain size alone cannot lower the model albedo sufficiently to match these observations. It is also argued that the two major effects which are neglected in the model, namely nonsphericity of snow grains and near-field scattering, cannot be responsible for the discrepancy. Insufficient snow depth and error in measured absorption coefficient are also ruled out as the explanation. The remaining hypothesis is that visible snow albedo is reduced by trace amounts of absorptive impurities (Warren and Wiscombe, 1980, Part II).

A Model for the Spectral Albedo of Snow II.[32][edit | edit source]

Visible and near-ultraviolet absorption spectrum of ice from transmission of solar radiation into snow[33][edit | edit source]

Method to Determine Snow Albedo Values in the Ultraviolet for Radiative Transfer Modeling[34][edit | edit source]

Abstract:For many cases modeled and measured UV global irradiances agree to within ∓5% for cloudless conditions, provided that all relevant parameters for describing the atmosphere and the surface are well known. However, for conditions with snow-covered surfaces this agreement is usually not achievable, because on the one hand the regional albedo, which has to be used in a model, is only rarely available and on the other hand UV irradiance alters with different snow cover of the surface by as much as 50%. Therefore a method is given to determine the regional albedo values for conditions with snow cover by use of a parameterization on the basis of snow depth and snow age, routinely monitored by the weather services. An algorithm is evolved by multiple linear regression between the snow data and snow-albedo values in the UV, which are determined from a best fit of modeled and measured UV irradiances for an alpine site in Europe. The resulting regional albedo values in the case of snow are in the 0.18–0.5 range. Since the constants of the regression depend on the area conditions, they have to be adapted if the method is applied for other sites. Using the algorithm for actual cases with different snow conditions improves the accuracy of modeled UV irradiances considerably. Compared with the use of an average, constant snow albedo, the use of actual albedo values, provided by the algorithm, halves the average deviations between measured and modeled UV global irradiances.

Progress and challenges for next-generation high-efficiency multijunction solar cells[35][edit | edit source]

Abstract:Multijunction solar cells are the most efficient solar cells ever developed with demonstrated efficiencies above 40%, far in excess of the performance of any conventional single-junction cell. This paper describes paths toward next-generation multijunction cells with even higher performance. Starting from fundamental multijunction concepts, the paper describes the desired characteristics of semiconductor materials for multijunction cells; the corresponding challenges in obtaining these characteristics in actual materials; and materials and device architectures to overcome these challenges

Four-junction spectral beam-splitting photovoltaic receiver with high optical efficiency[36][edit | edit source]

Abstract:A spectral beam-splitting architecture is shown to provide an excellent basis for a four junction photovoltaic receiver with a virtually ideal band gap combination. Spectrally selective beam-splitters are used to create a very efficient light trap in form of a 458 parallelepiped. The light trap distributes incident radiation onto the different solar cells with an optical efficiency of more then 90%. Highly efficient solar cells including III–V semiconductors and silicon were fabricated and mounted into the light trapping assembly. An integrated characterization of such a receiver including the measurement of quantum efficiency as well as indoor and outdoor I–V measurements is shown. Moreover, the optical loss mechanisms and the optical efficiency of the spectral beam-splitting approach are discussed. The first experimental setup of the receiver demonstrated an outdoor efficiency of more than 34% under unconcentrated sunlight.

Enhancing solar cell efficiency by using spectral converters[37][edit | edit source]

Abstract:Planar converters containing quantum dots as wavelength-shifting moieties on top of a multi-crystalline silicon and an amorphous silicon solar cell were studied. The highly efficient quantum dots are to shift the wavelengths where the spectral response of the solar cell is low to wavelengths where the spectral response is high, in order to improve the conversion efficiency of the solar cell. It was calculated that quantum dots with an emission at 603 nm increase the multi-crystalline solar cell short-circuit current by nearly 10%. Simulation results for planar converters on hydrogenated amorphous silicon solar cells show no beneficial effects, due to the high spectral response at low wavelength.

Effects of Gallium-Phosphide and Indium-Gallium-Antimonide semiconductor materials on photon absorption of multijunction solar cells[38][edit | edit source]

Abstract:The main challenge in the photovoltaic industry is making the solar cells more cost effective. Single junction solar cells can only absorb a certain wavelength of the solar spectrum, hence produce less efficiency. In contrary multijunction solar cells direct sunlight towards matched spectral sensitivity by splitting the spectrum into smaller slices. The high efficiency multijunction photovoltaics made up of III-V semiconductor material alloys with high optical sensitivity and ideal combination of band-gaps increase absorption of photons, creates more electron-hole pairs, and hence increase the efficiency of the solar cell. National Renewable Energy Laboratory (NREL), US Department of Energy (DOE) and many leading research organizations all over the world are investing money in the design of III-IV multijunction solar cell projects. In this paper, we introduce a novel multijunction photovoltaic cell based on GaP/InGaAs/InGaSb, and compare it with existing single-junction and multijunction cells. We observe that the inclusion of GaP and InGaSb layers in our design has made a significant improvement in absorption of solar energy in the entire spectral range, thus resulting in higher efficiency.

Band gap-voltage offset and energy production in next-generation multijunction solar cells[39][edit | edit source]

Abstract:The potential for new 4-, 5-, and 6-junction solar cell architectures to reach 50% efficiency is highly leveraging for the economics of concentrator photovoltaic (CPV) systems.The theoretical performance of such next-generation cells, and experimental results for 3- and 4-junction CPV cells, are examined here to evaluate their impact for real-world solar electricity generation. Semiconductor device physics equations are formulated in terms of the band gap-voltage offset Woc [TRIPLE BOND] (Eg/q) − Voc, to give a clearer physical understanding and more general analysis of the multiple subcell band gaps in multijunction cells. Band gap-voltage offset is shown experimentally to be largely independent of band gap Eg for a wide range of metamorphic and lattice-matched semiconductors from 0.67 to 2.1 eV. Its theoretical Eg dependence is calculated from that of the radiative recombination coefficient, and at a more fundamental level using the Shockley-Queisser detailed balance model, bearing out experimental observations. Energy production of 4-, 5-, and 6-junction CPV cells, calculated for changing air mass and spectrum over the course of the day, is found to be significantly greater than for conventional 3-junction cells. The spectral sensitivity of these next-generation cell designs is fairly low, and is outweighed by their higher efficiency. Lattice-matched GaInP/GaInAs/Ge cells have reached an independently confirmed efficiency of 41.6%, the highest efficiency yet demonstrated for any type of solar cell. Light I-V measurements of this record 41.6% cell, of next-generation upright metamorphic 3-junction cells with 40% target production efficiency, and of experimental 4-junction CPV cells are presented.

The AM1.5 absorption factor of thin-film solar cells[40][edit | edit source]

Abstract:Both for photovoltaic and photovoltaic/thermal applications insight is required in the mechanisms that determine the effective absorption factor Aeff. Aeff is the part of the incident irradiation that is converted into heat, taking into account that part of the energy is withdrawn as electricity. Aeff was studied for five different solar cell technologies using an optical simulation model and ranges from 74% for single junction amorphous silicon solar cells to 82% for CIGS solar cells. The simulations also show that the longer wavelength part of the spectrum is hardly absorbed by the active semiconductors, but mostly by free carrier absorption in the transparent conductive oxide film present in these devices.

References[edit | edit source]

  1. Chan, N., T. Young, H. Brindley, B. Chaudhuri, and N.J. Ekins-Daukes. “Variation in Spectral Irradiance and the Consequences for Multi-junction Concentrator Photovoltaic Systems.” In 2010 35th IEEE Photovoltaic Specialists Conference (PVSC), 003008 –003012, 2010
  2. Riordan, C., and R. Hulstron.“What Is an Air Mass 1.5 Spectrum?”Conference Record of the Twenty First IEEE Photovoltaic Specialists Conference, 1990, 1085 –1088 vol.2, 1990.
  3. Faine, P., Sarah R. Kurtz, C. Riordan, and J.M. Olson. “The Influence of Spectral Solar Irradiance Variations on the Performance of Selected Single-junction and Multijunction Solar Cells.” Solar Cells 31, no. 3 (June 1991): 259–278. doi:10.1016/0379-6787(91)90027-M
  4. Nann, S., and K. Emery. “Spectral Effects on PV-device Rating.” Solar Energy Materials and Solar Cells 27, no. 3 (August 1992): 189–216. doi:10.1016/0927-0248(92)90083-2.
  5. Suzuki, Mamoru, Junta Nose, Akihiko Nakano, Yoshihiro Imura, and Makoto Igarashi. “Outdoor Measurement of Multi-junction Solar Cells and Modules Based on the Reference Sunlight Method.” Solar Energy 60, no. 2 (February 1997): 63–70. doi:10.1016/S0038-092X(96)00164-8.
  6. D. L. King, “Photovoltaic module and array performance characterization methods for all system operating conditions,” AIP Conference Proceedings, vol. 394, no. 1, pp. 347–368, Feb. 1997.
  7. M. W. Wanlass, S. P. Ahrenkiel, R. K. Ahrenkiel, D. S. Albin, J. J. Carapella, A. Duda, J. F. Geisz, S. Kurtz, T. Moriarty, R. J. Wehrer, and B. Wernsman, “Lattice-mismatched approaches for high-performance, III-V photovoltaic energy converters,” in Conference Record of the Thirty-first IEEE Photovoltaic Specialists Conference, 2005, 2005, pp. 530 – 535.
  8. M. Peters, J. C. Goldschmidt, P. Löper, B. Groß, J. Üpping, F. Dimroth, R. B. Wehrspohn, and B. Bläsi, “Spectrally-Selective Photonic Structures for PV Applications,” Energies, vol. 3, no. 2, pp. 171–193, Jan. 2010.
  9. J. L. Gray, A. W. Haas, J. R. Wilcox, and R. J. Schwartz, “Efficiency of multijunction photovoltaic systems,” in 33rd IEEE Photovoltaic Specialists Conference, 2008. PVSC ’08, 2008, pp. 1 –6.
  10. Mitchell, B., “Four-junction spectral beam-splitting photovoltaic receiver with high optical efficiency,” Progress in Photovoltaics, vol. 19, no. Nr.1, pp. 61–72, 2011.
  11. C. A. Gueymard, “Interdisciplinary applications of a versatile spectral solar irradiance model: A review,” Energy, vol. 30, no. 9, pp. 1551–1576, Jul. 2005.
  12. D. R. Myers, “Solar radiation modeling and measurements for renewable energy applications: data and model quality,” Energy, vol. 30, no. 9, pp. 1517–1531, Jul. 2005.
  13. D. L. King, G. M. Galbraith, W. E. Boyson, S. Gonzalez, A. T. Murray, J. W. Ginn, and W. I. Bower, “Array Performance Characterization and Modeling for Real-Time Performance Analysis of Photovoltaic Systems,” in Conference Record of the 2006 IEEE 4th World Conference on Photovoltaic Energy Conversion, 2006, vol. 2, pp. 2308 –2311.
  14. S. Nann and C. Riordan, “Solar spectral irradiance under overcast skies [solar cell performance effects],” in , Conference Record of the Twenty First IEEE Photovoltaic Specialists Conference, 1990, 1990, pp. 1110 –1115 vol.2.
  15. A. Marti, P. A. Davies, J. Olivan, C. Algora, M. J. Terron, J. Alonso, J. C. Maroto, G. L. Araujo, J. C. Minano, G. Sala, and A. Luque, “High efficiency photovoltaic conversion with spectrum splitting on GaAs and Si cells located in light confining cavities,” in , Conference Record of the Twenty Third IEEE Photovoltaic Specialists Conference, 1993, 1993, pp. 768 –773.
  16. H. T. Nguyen and J. M. Pearce, “Estimating potential photovoltaic yield with r.sun and the open source Geographical Resources Analysis Support System,” Solar Energy, vol. 84, no. 5, pp. 831–843, May 2010.
  17. R. W. Andrews and J. M. Pearce, “Prediction of energy effects on photovoltaic systems due to snowfall events,” in 2012 38th IEEE Photovoltaic Specialists Conference (PVSC), 2012, pp. 003386 –003391.
  18. S. Younes, R. Claywell, and T. Muneer, “Quality control of solar radiation data: Present status and proposed new approaches,” Energy, vol. 30, no. 9, pp. 1533–1549, Jul. 2005.
  19. T. Aoki and M. Fukabori, “Effects of snow physical parameters on spectral albedo and bidirectional reflectance of snow surface,” Journal of Geophysical Research, vol. 105, no. D8, pp. 10219–10236, 2000.
  20. U. Feister and R. Grewe, “SPECTRAL ALBEDO MEASUREMENTS IN THE UV and VISIBLE REGION OVER DIFFERENT TYPES OF SURFACES,” Photochemistry and Photobiology, vol. 62, no. 4, pp. 736–744, 1995.
  21. R. Gottschalg, D. G. Infield, and M. J. Kearney, “Experimental study of variations of the solar spectrum of relevance to thin film solar cells,” Solar Energy Materials and Solar Cells, vol. 79, no. 4, pp. 527–537, Sep. 2003.
  22. B. Marion, “A method for modeling the current–voltage curve of a PV module for outdoor conditions,” Progress in Photovoltaics: Research and Applications, vol. 10, no. 3, pp. 205–214, 2002.
  23. K. Emery, “The rating of photovoltaic performance,” IEEE Transactions on Electron Devices, vol. 46, no. 10, pp. 1928 –1931, Oct. 1999.
  24. R. P. Kenny, A. Ioannides, H. Müllejans, W. Zaaiman, and E. D. Dunlop, “Performance of thin film PV modules,” Thin Solid Films, vol. 511–512, no. 0, pp. 663–672, Jul. 2006.
  25. K. Emery, J. Burdick, Y. Caiyem, D. Dunlavy, H. Field, B. Kroposki, T. Moriarty, L. Ottoson, S. Rummel, T. Strand, and M. W. Wanlass, “Temperature dependence of photovoltaic cells, modules and systems,” in , Conference Record of the Twenty Fifth IEEE Photovoltaic Specialists Conference, 1996, 1996, pp. 1275 –1278.
  26. T. Zdanowicz, T. Rodziewicz, and M. Zabkowska-Waclawek, “Theoretical analysis of the optimum energy band gap of semiconductors for fabrication of solar cells for applications in higher latitudes locations,” Solar Energy Materials and Solar Cells, vol. 87, no. 1–4, pp. 757–769, May 2005.
  27. R. P. Kenny, E. D. Dunlop, H. A. Ossenbrink, and H. Müllejans, “A practical method for the energy rating of c-Si photovoltaic modules based on standard tests,” Progress in Photovoltaics: Research and Applications, vol. 14, no. 2, pp. 155–166, 2006.
  28. S. R. Williams, T. R. Betts, T. Helf, R. Gottschalg, H. G. Beyer, and D. G. Infield, “Modelling long-term module performance based on realistic reporting conditions with consideration to spectral effects,” in Proceedings of 3rd World Conference on Photovoltaic Energy Conversion, 2003, 2003, vol. 2, pp. 1908 –1911 Vol.2.
  29. J. J. Pérez-López, F. Fabero, and F. Chenlo, “Experimental solar spectral irradiance until 2500 nm: results and influence on the PV conversion of different materials,” Progress in Photovoltaics: Research and Applications, vol. 15, no. 4, pp. 303–315, 2007.
  30. R. Gottschalg, T. R. Betts, D. G. Infield, and M. J. Kearney, “On the importance of considering the incident spectrum when measuring the outdoor performance of amorphous silicon photovoltaic devices,” Measurement Science and Technology, vol. 15, no. 2, pp. 460–466, Feb. 2004.
  31. Wiscombe, Warren J., and Stephen G. Warren. “A Model for the Spectral Albedo of Snow. I: Pure Snow.” Journal of the Atmospheric Sciences 37, no. 12 (December 1980): 2712–2733. doi:10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2.
  32. Wiscombe, Warren J., and Stephen G. Warren. “A Model for the Spectral Albedo of Snow II: Snow Containing Atmospheric Aerosols.” Journal of the Atmospheric Sciences 37, no. 12 (December 1980): 2712–2733. doi:10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2.
  33. Warren, Stephen G., Richard E. Brandt, and Thomas C. Grenfell. “Visible and Near-ultraviolet Absorption Spectrum of Ice from Transmission of Solar Radiation into Snow.” Applied Optics 45, no. 21 (2006): 5320. doi:10.1364/AO.45.005320.
  34. Schwander, Harry, Bernhard Mayer, Ansgar Ruggaber, Astrid Albold, Gunther Seckmeyer, and Peter Koepke. “Method to Determine Snow Albedo Values in the Ultraviolet for Radiative Transfer Modeling.” Applied Optics 38, no. 18 (June 20, 1999): 3869–3875. doi:10.1364/AO.38.003869.
  35. [1] D. J. Friedman, “Progress and challenges for next-generation high-efficiency multijunction solar cells,” Current Opinion in Solid State and Materials Science, vol. 14, no. 6, pp. 131–138, Dec. 2010.
  36. B. Mitchell, G. Peharz, G. Siefer, M. Peters, T. Gandy, J. C. Goldschmidt, J. Benick, S. W. Glunz, A. W. Bett, and F. Dimroth, “Four-junction spectral beam-splitting photovoltaic receiver with high optical efficiency,” Progress in Photovoltaics: Research and Applications, vol. 19, no. 1, pp. 61–72, 2011.
  37. W. G. J. H. M. van Sark, A. Meijerink, R. E. I. Schropp, J. A. M. van Roosmalen, and E. H. Lysen, “Enhancing solar cell efficiency by using spectral converters,” Solar Energy Materials and Solar Cells, vol. 87, no. 1–4, pp. 395–409, May 2005. .
  38. I. Bhattacharya and S. Y. Foo, “Effects of Gallium-Phosphide and Indium-Gallium-Antimonide semiconductor materials on photon absorption of multijunction solar cells,” in IEEE SoutheastCon 2010 (SoutheastCon), Proceedings of the, 2010, pp. 316 –319.
  39. R. R. King, D. Bhusari, A. Boca, D. Larrabee, X.-Q. Liu, W. Hong, C. M. Fetzer, D. C. Law, and N. H. Karam, “Band gap-voltage offset and energy production in next-generation multijunction solar cells,” Progress in Photovoltaics: Research and Applications, vol. 19, no. 7, pp. 797–812, 2011.
  40. ] R. Santbergen, J. M. Goud, M. Zeman, J. A. M. van Roosmalen, and R. J. C. van Zolingen, “The AM1.5 absorption factor of thin-film solar cells,” Solar Energy Materials and Solar Cells, vol. 94, no. 5, pp. 715–723, May 2010.