Simulations of solar cell absorption enhancement using resonant modes of a nanosphere array

We propose an approach for enhancing the absorption of thin-film amorphous silicon solar cells using periodic arrangements of resonant dielectric nanospheres deposited as a continuous film on top of a thin planar cell. We numerically demonstrate this enhancement using 3D full field finite difference time domain simulations and 3D finite element device physics simulations of a nanosphere array above a thin-film amorphous silicon solar cell structure featuring back reflector and anti-reflection coating. In addition, we use the full field finite difference time domain results as input to finite element device physics simulations to demonstrate that the enhanced absorption contributes to the current extracted from the device. We study the influence of a multi-sized array of spheres, compare spheres and domes and propose an analytical model based on the temporal coupled mode theory

Outdoor performance of a thin-film gallium-arsenide photovoltaic module

We deployed a 855 cm2 thin-film, single-junction gallium arsenide (GaAs) photovoltaic (PV) module outdoors. Due to its fundamentally different cell technology compared to silicon (Si), the module responds differently to outdoor conditions. On average during the test, the GaAs module produced more power when its temperature was higher. We show that its maximum-power temperature coefficient, while actually negative, is several times smaller in magnitude than that of a Si module used for comparison. The positive correlation of power with temperature in GaAs is due to temperature-correlated changes in the incident spectrum. We show that a simple correction based on precipitable water vapor (PWV) brings the photocurrent temperature coefficient into agreement with that measured by other methods and predicted by theory. The low operating temperature and small temperature coefficient of GaAs give it an energy production advantage in warm weather

Solar cell efficiency enhancement via light trapping in printable resonant dielectric nanosphere arrays

Resonant dielectric structures are a promising platform for addressing the key challenge of light trapping in thin-film solar cells. We experimentally and theoretically demonstrate efficiency enhancements in solar cells from dielectric nanosphere arrays. Two distinct amorphous silicon photovoltaic architectures were improved using this versatile light-trapping platform. In one structure, the colloidal monolayer couples light into the absorber in the near-field acting as a photonic crystal light-trapping element. In the other, it acts in the far-field as a graded index antireflection coating to further improve a cell which already included a state-of-the-art random light-trapping texture to achieve a conversion efficiency over 11%. For the near-field flat cell architecture, we directly fabricated the colloidal monolayer on the device through Langmuir–Blodgett deposition in a scalable process that does not degrade the active material. In addition, we present a novel transfer printing method, which utilizes chemical crosslinking of an optically thin adhesion layer to tether sphere arrays to the device surface. The minimally invasive processing conditions of this transfer method enable the application to a wide range of solar cells and other optoelectronic devices. False-color SEM image of an amorphous silicon solar cell with resonant spheres on top

The 2020 photovoltaic technologies roadmap

Over the past decade, the global cumulative installed photovoltaic (PV) capacity has grown exponentially, reaching 591 GW in 2019. Rapid progress was driven in large part by improvements in solar cell and module efficiencies, reduction in manufacturing costs and the realization of levelized costs of electricity that are now generally less than other energy sources and approaching similar costs with storage included. Given this success, it is a particularly fitting time to assess the state of the photovoltaics field and the technology milestones that must be achieved to maximize future impact and forward momentum. This roadmap outlines the critical areas of development in all of the major PV conversion technologies, advances needed to enable terawatt-scale PV installation, and cross-cutting topics on reliability, characterization, and applications. Each perspective provides a status update, summarizes the limiting immediate and long-term technical challenges and highlights breakthroughs that are needed to address them. In total, this roadmap is intended to guide researchers, funding agencies and industry in identifying the areas of development that will have the most impact on PV technology in the upcoming years

Predicting photovoltaic soiling losses using environmental parameters: An update

This study presents an investigation on the correlations between soiling losses and environmental parameters at 41 locations in the United States, with the aim of analyzing the possibility of predicting soiling losses at a site even when soiling data are not available. The results of this work, which considers the largest pool of soiling data points systematically investigated so far, confirm that a single-variable regression based on particulate matter concentration returns the best correlations with soiling, with adjusted coefficients of determination up to 70%, corresponding to RMSE as low as 0.9%. Among the various particulate matter datasets investigated, a gridded Environment Protection Agency dataset is for the first time found to return correlations similar to those obtained by interpolating particulate matter monitoring station data. We discuss in detail the different interpolation techniques used to process the particulate matter concentrations because they can greatly impact the correlations. Specifically, the correlation coefficients between soiling and particulate matter range between 70% and less than 20%, depending on the interpolation methods and monitoring distance. Spatial interpolation methods based on inverse distance weighting are found to return better correlations than a nearest neighbor or a simple average approach, especially when large distances are considered. Similarly, the effects of different rain thresholds used to calculate the length of the dry periods are examined. An enhanced two-variable regression is found to achieve higher-quality correlations, with adjusted R 2 of 90% (RMSE = 0.55%), also suggesting that high and low soiling locations might be differentiated depending on fixed particulate matter or rainfall thresholds

Quantifying soiling loss directly from PV yield

Soiling of photovoltaic (PV) panels is typically quantified through the use of specialized sensors. Here, we describe and validate a method for estimating soiling loss experienced by PV systems directly from system yield without the need for precipitation data. The method, termed the stochastic rate and recovery (SRR) method, automatically detects soiling intervals in a dataset, then stochastically generates a sample of possible soiling profiles based on the observed characteristics of each interval. In this paper, we describe the method, validate it against soiling station measurements, and compare it with other PV-yield-based soiling estimation methods. The broader application of the SRR method will enable the fleet scale assessment of soiling loss to facilitate mitigation planning and risk assessment

Mapping photovoltaic soiling using spatial interpolation techniques

In this paper, we present a new soiling map developed at the National Renewable Energy Laboratory, showing data from 83 sites in the United States. Soiling has been measured through soiling stations or extracted by photovoltaic system performance data using referenced techniques. The data on the map have been used to conduct the first regional analysis of soiling distribution in the United States. We found that most of the soiling occurs in the southwestern United States, with Southern California counties experiencing the greatest losses because of the high particulate matter concentrations and the long dry periods. Moreover, we employed five spatial-interpolation techniques to investigate the possibility of estimating soiling at a site using data from nearby sites. We found that coefficients of determination of up to 78% between estimated and measured soiling ratios, meaning that, by using selective sampling, soiling losses can be predicted using the data on the map with a root-mean-square error of as low as 1.1%

Local variability in PV soiling rate

We examine the correlation between monthly PV system soiling rates over a multi-year period within two groups of First Solar soiling stations in separate regions of California as a function of site separation. The results demonstrate large seasonal variations in monthly soiling rates, with seasonal patterns that differ between sites. Monthly patterns at nearby sites were more highly correlated, suggesting that soiling rates can be strongly affected by local geography and weather. For the two regions studied, soiling rate pattern correlation diminished beyond a distance on the order of 50 km

Raw Data and Code for "Optical Approaches for Passive Thermal Management in c-Si Photovoltaic Modules" Slauch, Deceglie, Silverman, Ferry 2021

Files are sorted into folders depending on the figure in the manuscript to which they pertain. Many figures use data from simulation or experimental results. The results are placed in each folder as obtained from the experimental apparatus or simulation software in .csv format. MATLAB code written to support this work is provided. Input files for ray tracing simulations (SunSolve, PVLighthouse) and finite-element simulations (TOMCAT, NREL) are provided. SunSolve is third-party software to which NREL pays a subscription and the provided files are only compatible with that software as far as the authors are aware.These data comprise the raw experimental and simulation results of, and the computer code written to support, the work described in the manuscript "Optical Approaches for Passive Thermal Management in c-Si Photovoltaic Modules" submitted by the listed authors to the publication Joule, and are here submitted in accordance with the data archiving policy of the journal. These data contain primarily results from the third-party ray tracing software "SunSolve" published by PVLighthouse (www.pvlighthouse.au) and results from the finite-element simulation software "TOMCAT", published by the National Renewable Energy Laboratory (https://github.com/NREL/pv_tomcat). Additionally, the code written as a part of this work (MATLAB) has been provided.This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office under award number Award Number DE-EE0008542