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Multijunction solar cells offer a path to very high conversion efficiency, exceeding 60% in theory. Under ideal conditions, efficiency increases monotonically with the number of junctions. In this study, we explore technical and economic mechanisms acting on tandem solar cells. We find that these mechanisms produce limitations that are the more pronounced the greater the number of junction is and, hence, limit the ideal number of junctions, as well as the corresponding efficiencies. Spectral variations induce current losses in series-connected tandem solar cells. For Denver, we find that these losses reduce achievable harvesting efficiencies to 51% for non-concentrated light, and that they restrict the ideal number of junctions to less than nine. Independently operated solar cells suffer from optical losses with similar consequences. Optical efficiencies of 99% restrict the ideal number of junctions to below ten, and reduce achievable efficiencies by more than 10%. Only architectures with a sequential cell illumination are more resilient to these losses. Restricting available materials reveals that a sufficiently low band gap for the bottom cell of 0.9 eV or below is expedient to realize high efficiencies. Economic considerations show that five junctions or less are economically ideal for most conceivable applications.

To identify abnormal photovoltaic (PV) modules in large‐scale PV plants economically, drone‐mounted infrared (IR) cameras and automated video processing algorithms are frequently used. While most related works focus on the detection of abnormal modules, little has been done to automatically localize those modules within the plant. In this work, we use incremental structure‐from‐motion to automatically obtain geocoordinates of all PV modules in a plant based on visual cues and the measured GPS trajectory of the drone. In addition, we extract multiple IR images of each PV module. Using our method, we successfully map 99.3% of the 35,084 modules in four large‐scale and one rooftop plant and extract over 2.2 million module images. As compared to our previous work, extraction misses 18 times less modules (one in 140 modules as compared to one in eight). Furthermore, two or three plant rows can be processed simultaneously, increasing module throughput and reducing flight duration by a factor of 2.1 and 3.7, respectively. Comparison with an accurate orthophoto of one of the large‐scale plants yields a root mean square error of the estimated module geocoordinates of 5.87 m and a relative error within each plant row of 0.22 m to 0.82 m. Finally, we use the module geocoordinates and extracted IR images to visualize distributions of module temperatures and anomaly predictions of a deep learning classifier on a map. While the temperature distribution helps to identify disconnected strings, we also find that its detection accuracy for module anomalies reaches, or even exceeds, that of a deep learning classifier for seven out of ten common anomaly types. The software is published at https://github.com/LukasBommes/PV-Hawk.

Feasibility of the identification of backsheet (BS) composition of field-aged Si photovoltaic (PV) modules based on the analysis of characteristic patterns of UV-excited fluorescence (UVF) of ethylene vinyl acetate copolymer encapsulant is shown. The approach includes BS identification by vibrational spectroscopies and the formation of a database of BS-related characteristic UVF patterns. A multivariate principal component analysis of the UVF images of unknown samples combined with the UVF pattern database allows the BS type to be identified only from UVF images. The geometric place of particular samples in the principal component plots is indicative of the general degradation status of PV modules. The proposed method can be applied for high-throughput noncontact characterization of solar PV plants.

It is commonly assumed that photovoltaic (PV) modules have a 25-year lifetime. Warranties last this long, and techno-economic projections use this figure when calculating system performance. Yet, PV modules do not stop functioning after this time. Many modules still perform at above 80% of their original power rating after 25 years, and there is neither an economic nor an ecological reason to retire them. In this study, we explore the implications of transitioning from a mindset with finite module-life to a long-term perspective for PV-modules. We introduce an economic model to reflect such a long-term view. Using this model, we show that degradation gains in importance compared to module price. We calculate a cost-entitlement of up to 12ct/W for utility installation when improving annual degradation rates from 0.5% to 0.2%. We show that maintenance becomes more valuable – about 33% compared to a system with 30-year lifetime - and that time-constraints for maintenance disappear. We define a new lifetime metric - the minimum economically useful lifetime of a PV module as the time the module needs to be operated before replacement becomes economically preferable. We find that modules currently installed should be operated for at least 25 years and ideally for 50 years. Our metric links module life with degradation rate and, we calculate that reducing annual degradation rates from 0.5% to 0.2% increases the useful lifetime by a factor of 1.69. Such an increase would defer module end of life by decades and reduce resources required for maintenance by 40%.

Degradation of backsheets (BSs) of commercial silicon PV modules is currently recognized as a source of reduced module performance and module failure. Monitoring of the BS state in the field is possible by using non-destructive and highly informative near-infrared absorption (NIRA) spectroscopy. Application of NIRA for the analysis of multi-layer polyethylene terephtalate (PET) based BSs, which dominate the PV module market, is challenging due to a large variety of possible BS configurations that show only small differences in NIRA spectra. In the present work, a spectroscopic tool for the structural identification of PET-based BSs is introduced. The method is based on a principal component analysis of a database of 250 representative NIRA spectra of BSs of different types. It allows a BS with an unknown structure to be assigned to one of 12 different types based solely on its NIRA spectrum. The identification was successfully validated on a test collection of 45 selected BSs and shown to be feasible for the field deployment. Further automation of NIRA measurements and spectral analysis are expected to elevate the proposed tool to the level of a non-intrusive high-throughput field analysis of the BS composition and state in operating PV module grids.

In this study, we introduce a weather data independent self-referencing algorithm for performance analyses of PV power stations based on monitoring data. We introduce this method as an alternative to standard performance ratio analyses, in the case that no meteorological data are available. The self-referencing algorithm utilizes the string with maximum output as a reference. The output of other strings is set in relation to the corresponding value of the reference string, resulting in a quantity we call self-referencing ratio SR. We benchmark this ratio against the established performance ratio, which requires solar irradiance data for calculation. Based on a 9 MWp PV power station with 1,719 strings, we show that we are able to quantify, locate, and visualize the performance of each string in the PV power station. About 10% of the strings are underperforming; most of them are located in the bottom rows of the mounting racks. Shading from adjacent mounting racks is a possible explanation for this finding. Furthermore, IR-images and voltage data indicate a frequent occurrence of strings with a reduced number of modules. An underperformance of less than 2% is challenging to detect, due to the uncertainty range of the chosen dataset.

Increasing deployment of photovoltaic (PV) plants requires methods for automatic detection of faulty PV modules in modalities, such as infrared (IR) images. Recently, deep learning has become popular for this. However, related works typically sample train and test data from the same distribution ignoring the presence of domain shift between data of different PV plants. Instead, we frame fault detection as more realistic unsupervised domain adaptation problem where we train on labeled data of one source PV plant and make predictions on another target plant. We train a ResNet‐34 convolutional neural network with a supervised contrastive loss, on top of which we employ a k‐nearest neighbor classifier to detect anomalies. Our method achieves a satisfactory area under the receiver operating characteristic (AUROC) of 73.3% to 96.6% on nine combinations of four source and target datasets with 2.92 million IR images of which 8.5% are anomalous. It even outperforms a binary cross‐entropy classifier in some cases. With a fixed decision threshold, this results in 79.4% and 77.1% correctly classified normal and anomalous images, respectively. Most misclassified anomalies are of low severity, such as hot diodes and small hot spots. Our method is insensitive to hyperparameter settings, converges quickly, and reliably detects unknown types of anomalies making it well suited for practice. Possible uses are in automatic PV plant inspection systems or to streamline manual labeling of IR datasets by filtering out normal images. Furthermore, our work serves the community with a more realistic view on PV module fault detection using unsupervised domain adaptation to develop more performant methods with favorable generalization capabilities.

The significant capital expense of photovoltaics manufacturing has made it difficult for new cell and module technologies to enter the market. We present two technoeconomic models that analyze the sustainable growth of perovskite manufacturing for an R2R single-junction technology and a perovskite-silicon tandem module, focusing on the impacts of economies of scale and average selling price on profitability. We establish a cost range of $3.30/W to $0.53/W for flexible modules manufactured in factory sizes ranging from 0.3 MW/year to 1 GW/year. In addition, we model the cost to manufacture a tandem module consisting of a single-junction perovskite cell stacked in 4-terminal configuration onto a silicon cell and show how an existing manufacturer can grow at a faster rate by co-investing in tandems. Our analyses highlight potential routes to market for perovskite photovoltaics and the possibility to sustainably grow a photovoltaics manufacturing company even in markets with higher labor rates. We show how technoeconomic modeling of cleantech products versus scale can be an important tool in assisting a more rapid uptake of new energy technologies that often struggle to leave the lab. Our analyses highlight potential routes to market for perovskite photovoltaics and the possibility to sustainably grow a photovoltaics manufacturing company even in markets with higher labor rates. More generally, although technoeconomic modeling has proven to be a useful tool for assessing cleantech industries as they are and the long-term potential of new technologies once they reach scale - we encourage other cost modelers to quantify the impact of economies of scale during manufacturing growth to help in the search for viable and sustainable market on-ramps for their technologies. The significant capex of photovoltaics manufacturing has made it difficult for new cell and module technologies to enter the solar power market. We show how technoeconomic modeling of cleantech products versus scale can be an important tool in assisting the commercialization of new energy technologies that often struggle to leave the lab with our analyses focusing on potential routes to market for perovskite photovoltaics.

Richard E. Smalley, in 2003, defined the Terawatt (TW) Challenge as “Adapting our energy infrastructure to simultaneously address diminishing oil resources and rising levels of atmospheric CO 2.” Smalley, best known for the discovery of C 60, for which he received the 1996 Nobel Prize in Chemistry, continued to address the challenges of anthropomorphic and natural global energy flows until he passed away in 2005. Smalley challenged the world to transform the energy sector. He envisioned electricity transmitted by high-voltage direct current (DC) lines from massively deployed solar plants in sunny areas and remotely sited nuclear plants. He also envisioned using advanced batteries for local storage of energy. To meet the needs of ~10 people in a world with a dwindling oil supply, Smalley asserted that the world would need to transform its fossil-fuel-driven 14-TW (average power) energy used in 2003 to a largely renewable-energy-driven 30–60 TW (average power) in 2050. This would be possible only if solar-electricity costs could be drastically reduced. The challenges associated with this transition have been called the “Terawatt Challenge.” Fifteen years later, solar-module costs have been reduced by tenfold and annual deployment of solar photovoltaic (PV) modules has grown by a factor of 100,from ~1 gigawatt (GW) in 2004 to ~100 GW in 2018, with a total of 500 GW installed worldwide, producing 2% of the planet’s electricity. As global installed solar generating capacity approaches1 TW, we revisit Smalley’s TW challenge to identify what has changed and quantify the TW Challenge for a baseline scenario and for two scenarios designed as upper and lower bounds determined by the degree we implement electrification and storage. In this paper, we show that the energy choices we make today will dramatically affect the magnitude of future global energy requirements.

A techno-economic analysis of perovskite-silicon tandem solar modules is presented, outlining the most viable pathway for designing cost-effective, commercially viable tandems. We explore the cost-performance trade-off for silicon bottom cells in perovskite-silicon tandems, and evaluate the potential of using low-cost, lower-efficiency silicon bottom cells, on the basis of levelized cost of electricity (LCOE), compared to the higher-efficiency, higher-cost bottom cells that have been the primary focus of most perovskite-silicon tandem research efforts. We fabricate a cost-effective four-terminal silicon-perovskite tandem using a low-cost multicrystalline bottom cell and calculate the device LCOE. We then extend this analysis by modeling performance and LCOE of similar tandems instead using high-efficiency silicon bottom cells, enabling direct comparison of a low-cost and a high-efficiency tandem. Lastly parametric analyses are performed to more broadly examine the bottom-cell cost-performance trade-off. We show that low-cost silicon, even at the detriment of efficiency, is the more likely path to make perovskite-silicon tandems commercially viable and enable future reductions in LCOE, given both current and near-future silicon technology. We lay out a clear economic motivation for pursuing low-cost silicon bottom cells in perovskite-silicon tandems, showing that they can achieve a 15–20% relative LCOE reduction compared to the single-junction sub-cells. This is a 2–3 times greater relative LCOE reduction compared with using high-efficiency silicon. Furthermore, we show that the advantage of using low-cost silicon bottom cells is robust to and benefits from expected market trends, such as falling system costs and advanced, low-cost manufacturing. This work provides a clear pathway to cost-effective tandems, outlines the benefits for existing multicrystalline silicon manufacturers to investing in tandem development, and points out a clear mismatch between commercial viability and current research efforts.

Reliable and durable solar power plants require PV modules with high-grade polymer encapsulants and backsheets (BSs). For performance analyses of PV installations fast, reliable and non-destructive methods for determining composition and degradation state of polymer components need to be developed. Here, we show that the structure of some common polymer BSs can be determined in the field in real time by analyzing near-infrared transmission (NIRT) spectra collected under illumination with natural sunlight. The potential of this “Solar-NIRT” method was probed by field measurements on a multi-MW PV power plant where four major BS types were identified by multispectral cross-sectional Raman imaging. Additionally, degradation of a particular BS type was found to result in distinct changes in NIRT spectra allowing the degraded BSs to be classified as a separate type. Principal component analysis (PCA) applied to a collection of 62 Solar-NIRT spectra allowed to create a map of five clusters, each corresponding to a particular BS type. The feasibility of using the PCA cluster map for the identification of unknown samples was shown on a test set of 13 different BSs. The Solar-NIRT is relatively fast, non-invasive, selective, can be upgraded to a non-contact regime making it a promising tool for high-throughput characterization.

Thermography is a frequently used and appreciated method to detect underperforming Photovoltaic modules in solar power stations. With the review, we give insights on two aspects: (a) are the developed measurement strategies highly efficient (about 1 module s−1) to derive timely answers from the images for operators of multi-Mega Warr peak power stations, and (b) do Photovoltaic stakeholders get answers on the relevance of thermal anomalies for further decisions. Following these questions, the influence of measurement conditions, image and data collection, image evaluation as well as image assessment are discussed. From the literature it is clear that automated image acquisition with manned and unmanned aircrafts allow to capture more than 1 module s−1. This makes it possible to achieve almost identical measurement conditions for the modules; however, it is documented to what extent the increase in speed is achieved at the expense of image resolution. Many image processing tools based on machine learning (ML) have been developed and show the potential for analysis of infrared (IR) images and defect classification. There are different approaches to evaluating IR anomalies in terms of impact on performance, yield or degradation, of individual modules or modules in a string configuration. It is clear that the problem is very complex and multi-layered. On the one hand, information on the electrical interconnection is necessary, and on the other hand, there is a lack of sufficient and suitable data sets to adapt existing computer vision tools to Photovolatics. This is where we see the greatest need for action and further development to increase the expressiveness of IR images for PV stakeholder. We conclude with recommendations to improve the outcome of IR-images and encourage the generation of suitable public data sets of IR-footage for the development of ML tools.

Over the last years, a significant number of inverter shutdowns most likely due to degraded backsheets have been reported by operators, investors, and manufacturers. We investigated water ingress into different backsheets, and the resulting risk for inverter shutdowns. For studying pending insulation issues of inverters, we analyzed exemplarily a 5-MWp photovoltaic (PV) power station with 20,530 PV modules and 314 inverters. For backsheet identification on-site near-infrared absorption spectroscopic measurements of 518 PV modules from 20 inverters were carried out. In the lab, wet leakage tests provide kinetic data of insulation resistances for different backsheet materials. Historic ground impedance data logged by the inverter were evaluated to show the temporal evolution of inverters with different backsheets. We observe different kinetics and drops in leakage resistance for different backsheet types based on polyamide (PA) and air-side fluorinated coating (FC), and distinguish between two types of behavior. “PA-like” is characterized by moderate leakage resistance loss and slower performance reduction with regard to insulation in the field. “FC-like” is characterized by high susceptibility to water and a high leakage resistance loss rate for PV modules after 6–8 years of operation. These characteristics are indicated when measuring inverter GI in dependence of temperature and humidity. For PA-based BSs, the reduction in ground impedance (GI) is steady over 8 years. We observe incidences with GI below the critical value of 400 kΩ on 2.8% of all days. For FC-based BSs, we also observe steady decrease of average GI initially, yet after 6 years in the field, we see a sudden and rapid decline for 20% of those inverters. GI falling below the critical value is observed on up to 5% of all days, with the trend increasing. Therefore we, expect this problem to become even more severe for longer operating times.

The individual causes for power loss of photovoltaic modules are investigated for quite some time. Recently, it has been shown that the power loss of a module is, for example, related to the fraction of inactive areas. While these areas can be easily identified from electroluminescense (EL) images, this is much harder for photoluminescence (PL) images. With this work, we close the gap between power regression from EL and PL images. We apply a deep convolutional neural network to predict the module power from PL images with a mean absolute error (MAE) of 4.4 ± 4.0 % or 11.7 ± 9.5 W P . Furthermore, we depict that regression maps computed from the embeddings of the trained network can be used to compute the localized power loss. Finally, we show that these regression maps can be used to identify inactive regions in PL images as well.

Increasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In this work, we develop a computer vision tool for the semi‐automatic extraction of PV modules from thermographic UAV videos. We use it to curate a dataset containing 4.3 million IR images of 107,842 PV modules from thermographic videos of seven different PV plants. To demonstrate its use for automated PV plant inspection, we train a ResNet‐50 to classify ten common module anomalies with more than 90% test accuracy. Experiments show that our tool generalizes well to different PV plants. It successfully extracts PV modules from 512 out of 561 plant rows. Failures are mostly due to an inappropriate UAV trajectory and erroneous module segmentation. Including all manual steps our tool enables inspection of 3.5 MWp to 9 MWp of PV installations per day, potentially scaling to multi‐gigawatt plants due to its parallel nature. While we present an effective method for automated PV plant inspection, we are also confident that our approach helps to meet the growing demand for large thermographic datasets for machine learning tasks, such as power prediction or unsupervised defect identification.

Summary Herein, we introduce the value of efficiency (VOE)—a metric defining the allowable cost for innovation by analyzing the co-dependence of levelized cost of electricity (LCOE) on manufacturing cost and efficiency. Historically, VOE values have dropped rapidly and will further, yet slower, decline in the future. Additionally, VOE values for residential and utility installations converge at different levels, indicating a stronger diversification of these market segments. Similarly, local differences in VOE suggest a regional market diversification. Advanced concepts like n-type monocrystalline PERC silicon solar cells could be economically preferable to multicrystalline silicon solar cells in some countries but may fare worse in others, including China and India. Extending the analysis to tandem photovoltaics, we confirm its potential to improve LCOEs for residential applications but foresee challenges for utility markets. Finally, we explore detrimental impacts of degradation and show that competing for lower LCOEs demands matching degradation rates of established technologies.

Summary Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique perovskite-inspired compositions within a 2-month period, with 87% exhibiting band gaps between 1.2 and 2.4 eV, which are of interest for energy-harvesting applications. We utilize a fully connected deep neural network to classify compounds based on experimental X-ray diffraction data into 0D, 2D, and 3D structures, more than 10 times faster than human analysis and with 90% accuracy. We validate our methods using lead-halide perovskites and extend the application to lead-free compositions. The wider synthesis window and faster cycle of learning enables the realization of a multi-site lead-free alloy series, Cs3(Bi1-xSbx)2(I1-xBrx)9. We reveal the non-linear band-gap behavior and transition in dimensionality upon simultaneous alloying on the B-site and X-site of Cs3Bi2I9 with Sb and Br.

Summary What role have batteries to play in the transition toward terawatt levels of photovoltaics? In this perspective, we attempt to answer this question by looking at technical, economic, and ecological features of PV-battery systems. We argue that the window of opportunity for batteries lies in the capacities of a few kWh/kWP, the exact amount depending on various factors including battery cost, degradation rate, location, load profile, diversification of renewable energy sources, and interconnections. Using a simple PV plus battery model, we illustrate that such storage capacities efficiently reduce fluctuations in electricity generation, enabling higher PV adoption rates at competitive costs, and with a carbon footprint that is at least five times lower than that of the current energy mixes. Using sensible capacities, batteries are a powerful companion for solar energy, yet technical, economic, and policy innovations are needed to expand adoption. We see longer battery lifetimes and low capacity degradation rates are the most impactful technological parameters. Economic efforts should aim to reduce balance of plant costs and create better market opportunities for stationary storage, whereas policies should provide a strong regulatory framework to facilitate multipurpose usage and sector coupling.

Automated inspection plays an important role in monitoring large‐scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules, but have not been used to determine the power of a module. However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string. By now, this is commonly determined by measurements that require to discontact or even dismount the module, rendering a regular inspection of individual modules infeasible. In this work, we bridge the gap between electroluminescense measurements and the power determination of a module. We compile a large dataset of 719 electroluminescense measurements of modules at various stages of degradation, especially cell cracks and fractures, and the corresponding power at maximum power point. Here, we focus on inactive regions and cracks as the predominant type of defect. We set up a baseline regression model to predict the power from electroluminescense measurements with a mean absolute error (MAE) of 9.0 ± 8.4WP (4.0 ± 3.7%). Then, we show that deep learning can be used to train a model that performs significantly better (7.3 ± 6.5WP or 3.2 ± 2.7%) and propose a variant of class activation maps to obtain the per cell power loss, as predicted by the model. With this work, we aim to open a new research topic. Therefore, we publicly release the dataset, the code, and trained models to empower other researchers to compare against our results. Finally, we present a thorough evaluation of certain boundary conditions like the dataset size and an automated preprocessing pipeline for on‐site measurements showing multiple modules at once.

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, CdTe) in four locations: Singapore (fully humid equatorial climate), Colorado (cold arid), Brazil (warm, humid, 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 energyyield differences between Si solar cells and CdTe solar cells with an average error of less than 1.5 ± 0.5% ascompared to over 5% when using standard testing conditions.

Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multiframe superresolution (MFSR) to a sequence of low-resolution measurements. In addition, the rectification and removal of lens distortion simplifies subsequent analysis. Therefore, we propose to fuse this preprocessing with standard MFSR algorithms. This is advantageous, because we omit a separate processing step, the motion estimation becomes more stable and the spacing of high-resolution pixels on the rectified module image becomes uniform w.r.t. the module plane, regardless of perspective distortion. We present a comprehensive user study showing that MFSR is beneficial for defect recognition by human experts and that the proposed method performs better than the state-of-the-art. Furthermore, we apply automated crack segmentation and show that the proposed method performs $3\times$ better than bicubic upsampling and $2\times$ better than the state-of-the-art for automated inspection.

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy–Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows—with appropriate caveats—comparison of forecasts made using different models, across different locations and time periods. Refereed/Peer-reviewed

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Energy yield is a key metric for evaluating the performance of photovoltaic systems. It describes the total amount of energy generated by a photovoltaic (PV) installation over a given period, typically a year, and depends on physical properties of the solar cell like efficiency, band gap and temperature coefficient, as well as the operating conditions in a given location. Because the response of a solar cell to these conditions varies, two photovoltaic technologies may have a different energy yield, even if their lab efficiency is identical. Predicting energy yield accurately is important to system operators and installers to estimate the technical and economic performance of a PV installation. In this paper, we summarize our findings about satellite based energy yield predictions of solar cells with various technologies.

Summary Silicon-based multi-junction (tandem) technology is one potential route to the next breakthrough for terrestrial photovoltaic conversion. Significant progress has been made in tandem solar cells. To move forward, development of tandem module technology is essential. Here, we theoretically compare five possible tandem module architectures with multiple material combinations by modeling their outdoor performance ratios around the globe. This framework aids in evaluating future module designs and technology pathways by relating lab-based efficiency to field performance and even project financing. We find that tandem performance ratios depend notably on climate conditions. In general, three-terminal and mechanically voltage-matched modules show exceptionally good outdoor performance. Furthermore, we investigate the implications of technical performance on manufacturing cost globally and find that tandems are promising in high-value markets in arid climates, where a three to five times increase in the current silicon module manufacturing cost is allowed under an optimistic scenario.

This study provides experimental validation of a previously published optimal design theory for photovoltaic (PV)-powered electrodialysis reversal (EDR) desalination systems. The prior work describes the co-optimization of PV and EDR subsystems, and flexible operation to accommodate daily and annual solar irradiance variability, significantly reducing water cost. This study presents the fabrication of a PV-EDR pilot system designed using the co-optimization theory and field testing results from the rural village of Chelluru, India. Testing in the field enabled observation and evaluation of real-world factors on system performance, resulting in updates to the previous theory to include unaccounted factors that affect costs, including: filling and draining of water tanks, salt and water accumulation in tanks from prior batches, unexpected energy losses due to locally purchased converters, and scaling in the ED stack. Therefore, water cost in the PV-EDR pilot system was updated from previous estimates based on field performance. The estimated capital cost and lifetime cost of the Chelluru system are 34% and 45% lower, respectively, than the corresponding costs if the PV-EDR system was designed using conventional design practice. The theory and experimental insights presented in this paper will enable desalination engineers to better design and optimize PV-EDR systems.

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

Summary This work presents a worldwide analysis on the yield potential and cost effectiveness of photovoltaic farms composed of monofacial fixed-tilt and single/dual (1T/2T) tracker installations, as well as their bifacial counterparts. Our approach starts by estimating the irradiance reaching both the front and rear surfaces of the modules for the different system designs (validated based on data from real photovoltaic systems and results from the literature) to estimate their energy production. Subsequently, the overall system cost during their 25-year lifetime is factored in, and the levelized cost of electricity (LCOE) is obtained. The results reveal that bifacial-1T installations increase energy yield by 35% and reach the lowest LCOE for the majority of the world (93.1% of the land area). Although dual-axis trackers achieve the highest energy generation, their costs are still too high and are therefore not as cost effective. Sensitivity analyses are also provided to show the general robustness of our findings.

In this paper, we discuss the value of efficiency (VOE)-a metric to assess the maximum allowable cost for innovation in photovoltaic technology. We derive VOE by analyzing the co-dependence of levelized cost of electricity (LCOE) on manufacturing cost and efficiency and use the tool to explore different scenarios and technologies. Exploring historical trends indicates that a stronger market diversification between utility and residential applications could develop. Regional trends indicate that another diversification could also happen across different countries. Finally, we also explore detrimental economic effects of degradation and show that competing for lower LCOEs demands achieving similar degradation rates as established technologies do.

We address the challenge in depositing ultra-thin GaAs cells (

Photovoltaics (PV) can provide power autonomy to sensors and communication devices comprising the Internet of Things (IoT). An outstanding challenge is to create design rules that transform intermittent and non-dispatchable solar energy into persistent, stable, and low-cost power for sensors. These design rules govern optimal system architecture design and optimal power consumption patterns. In previous work, we considered (i) the variability of solar insolation across different time scales (hourly, daily, and seasonal); (ii) the solar cell device characteristics; and (iii) the power consumption of the sensing and communication modules. From these constraints, we described a methodology to calculate the necessary energy storage capacity to reliably power the device over several years. The system size can then be optimized to minimize cost or volume. In this paper, we describe a methodology to improve the reliability of solar-powered IoT devices in the event of outlier conditions (i.e., periods of anomalously low insolation that reduce battery state-of-charge). This theoretical study, based on actual sensor and insolation data, describes how an adaptive power consumption scheme decreases the dimensions of solar cell and battery as well as system cost, assuming flexible operating scheme is allowed. The adaptive power consumption scheme means the data acquisition and transmission rate — which governs the power consumption profile — changes based on availability of stored energy in the battery and recent solar insolation. The results show for 3% reduction in average data transmission, the cost of the power components (i.e. battery and solar panel) can be reduced by 10% and the volume of the battery by 30%. The design is constrained by the available power as well as the minimum expected system performance, using the rate of acquired data from the sensor. We find that implementing an adaptive power consumption scheme based on available battery capacity for solar powered sensor can reduce battery capacity by 90%, while ensuring that data transmission rate occasionally varies but never extinguishes completely.

Although two-terminal perovskite-silicon tandem solar cells have rapidly increased in efficiency, they have only been demonstrated with n-type silicon, which currently constitutes less than 5% of the global photovoltaics market. In this paper, we realize the first two-terminal perovskite tandem with p-type silicon by developing a recombination contact that enables voltage addition without damaging either subcell. We find that silicon interband recombination contacts are limited by a SiO x charge-extraction barrier, which forms during oxidative top-cell fabrication. A sputtered 30-nm indium tin oxide layer is found to protect the silicon cell surface from oxidation, while forming a recombination contact with the p-type nickel oxide hole transport layer for the perovskite top cell. Using this recombination contact we achieve voltage addition between the perovskite top cell and aluminum back-surface field p-type silicon bottom cell. We also find that minimizing moisture on the nickel oxide surface is important for achieving a stable open-circuit voltage under illumination. The recombination contact developed herein could play an important role in near-future developments.

Solar photovoltaics (PV) manufacturing has experienced dramatic worldwide growth in recent years, enabling a reduction in module costs, and a higher adoption of these technologies. Continued sustainable price reductions, however, require strategies focused in further technological innovation, minimization of capital expenditures, and optimization of supply chain flows. We present a framework: Techno-economic Integrated Tool For Tariff And Transportation (TIT-4-TAT), that enables the study of these different strategies by coupling a techno-economic model with a tariff and transportation algorithm to optimize supply chain layouts for PV manufacturing under equally-weighted objectives. We demonstrate the use of this framework in a set of interacting countries (Mexico, China, USA, and Brazil) and two extreme tariff scenarios: no tariffs, and high tariff levels imposed. Results indicate that introducing tariffs between countries significantly increase the minimum sustainable price for solar PV manufacturing, alter the optimal manufacturing locations, and render a more expensive final solar PV module price which can hinder the adoption rates required to mitigate climate change. Recommendations for stakeholders on the optimization process, and techno-economic drivers are presented based on our results. This framework may be utilized by policymakers for the spatially-resolved planning of incentives, labor and manufacturing programs, and proper import tariff designs in the solar PV market.

Degradation of backsheets (BS) and encapsulant polymer components of silicon PV modules is recognized as one of the main reasons for losses in PV plant performance and lifetime expectations. Here, we report first insights into the correlation between BS composition of PV-modules and PV power station performance by using a combination of lab- and field-imaging, as well as spectroscopic and electrical characterizations. Using field-suitable near-infrared absorption (NIRA) spectroscopy, the BS structure of 518 PV-modules, 2.5 percent of the PV-modules in a 5 MWp PV power station, was identified on-site. The variance of the BS composition was found to be heterogeneous across PV-modules of the same power class from the same manufacturer. Polymaide-based BS cause in 10 out of 100 inverters ground impedance values below 400 kOhm, which is a typical threshold for inverters connecting to the grid. For primer-based BS this low value is reached 20 times. We conclude these numbers best as possible from the available monitoring data of inverters associated with BS-types from NIRA. Challenging is the identification of degraded primer-based BSs, since visually they look healthy and undistinguishable to well performing BSs. The present results demonstrate that a deeper understanding of the relationship between bill-of-materials and performance of PV-modules is necessary to avoid/minimize inverter shut-downs, and that it can be achieved by using combinations of selected field and lab characterization methods with monitoring data., 23 pages, 9 figures

Wide bandgap mixed halide perovskites ABX3, in which X can be I, Br, or Cl, are promising materials to form highly efficient optoelectronic devices, because the optical bandgap can be controlled over a wide range by variation of the halogen composition. However, significant nonradiative losses must be overcome to approach the efficiency limit of single-junction solar cells. Here, we present a high throughput-based investigation of the influence of processing parameters on nonradiative losses in the perovskite bulk. We perform antisolvent crystallization during spin coating and vary the solvent type, its volume, and the temperature of the subsequent annealing step. We use the photoluminescence quantum yield (PLQY) as a proxy to the presence of nonradiative losses and PL spectra as a qualitative probe for sample morphology. Using Gaussian process regression, we find that we can reliably predict PLQY from the PL spectral shape. This means that the PL spectral shape conveys the essential photophysics controlling PL quenching and thus nonradiative charge recombination. In comparison with scanning electron micrographs and x-ray diffraction data, we find that nonradiative losses in polycrystalline perovskite films are caused by increased domain size dispersion. Our method provides a simple and fast structure-sensitive in-line probe for fast morphology optimization in a high-throughput fashion.

ObjectivesWe integrated an established participant-centred active vaccine safety surveillance system with a cloud-based pharmacy immunisation-recording program in order to measure adverse events following immunisation (AEFI) reported via the new surveillance system in pharmacies, compared with AEFI reported via an existing surveillance system in non-pharmacy sites (general practice and other clinics).DesignA prospective cohort study.Participants and settingIndividuals >10 years receiving influenza immunisations from 22 pharmacies and 90 non-pharmacy (general practice and other clinic) sites between March and October 2020 in Western Australia. Active vaccine safety surveillance was conducted using short message service and smartphone technology, via an opt-out system.Outcome measuresMultivariable logistic regression was used to assess the primary outcome: differences in proportions of AEFI between participants immunised in pharmacies compared with non-pharmacy sites, adjusting for confounders of age, sex and influenza vaccine brand. A subgroup analysis of participants over 65 years was also performed.ResultsOf 101 440 participants (6992 from pharmacies; 94 448 from non-pharmacy sites), 77 498 (76.4%) responded; 96.1% (n=74 448) within 24 hours. Overall, 4.8% (n=247) pharmacy participants reported any AEFI, compared with 6% (n=4356) non-pharmacy participants (adjusted OR: 0.87; 95% CI: 0.76 to 0.99; p=0.039). Similar proportions of AEFIs were reported in pharmacy (5.8%; n=31) and non-pharmacy participants (6; n=1617) aged over 65 years (adjusted OR: 0.94; 95% CI: 0.65 to 1.35; p=0.725). The most common AEFIs in pharmacy were: pain (2%; n=104), tiredness (1.9%; n=95) and headache (1.7%; n=88); and in non-pharmacy sites: pain (2.3%; n=1660), tiredness (1.9%; n=1362) and swelling (1.5%; n=1121).ConclusionsHigh and rapid response rates demonstrate good participant engagement with active surveillance in both pharmacy and non-pharmacy participants. Significantly fewer AEFIs reported after pharmacist immunisations compared with non-pharmacy immunisations, with no difference in older adults, may suggest different cohorts attend pharmacy versus non-pharmacy immunisers. The integrated pharmacy system is rapidly scalable across Australia with global potential.

Flat-panel tandem solar cells have demonstrated the potential to exceed the efficiencies of their single-junction constituents. However, robust design rules for tandem solar cells are currently lacking, slowing the development of cost-effective implementations of this technology. A double-junction solar cell with four-terminal (4T) architecture stacks two electrically independent subcells and avoids current-matching losses, resulting in two main advantages over the conventional integrated two-terminal (2T) architecture: a higher energy yield and a loosened constraint on material bandgap combinations. Because both subcells are contacted independently in a 4T tandem, multiple stacked semitransparent contacts are needed, causing optical and series resistance losses. Moreover, for stationary flat-panel tandems, contacts need to be optimized for a varying direction of incident sunlight. In this study, we develop a framework for optimizing metal grid contacts for 4T tandem solar cells and quantify the electrical and optical loss associated with these contacts. We also explore the range of conditions for which it is beneficial to align metal grid contact fingers. We find that, for most applications, the front and back contacts of the top cell should be aligned, resulting in a decrease in energy yield loss between $\text{5}{{\% }}\;{\text{and}}\;15{{\% }}$ , while aligning the bottom cell contacts is not beneficial and may even reduce total energy yield. Finally, we show that for nonideally matched subcell pairings, the contacts loss in a 4T tandem is not enough to counter the yield benefit of 4T over 2T tandems, while the contact loss may make the yield for more ideally matched subcells be comparable for 2T and 4T devices.

Non-concentrating tandem solar cells offer the potential to increase conversion efficiencies beyond 30%. Of particular interest are configurations with a silicon bottom cell – to leverage existing manufacturing infrastructure – and III-V compound semiconductor top cells to combine high efficiencies with long-term stability and experience in applications. In this work we investigate the GaAs/GaAs/Si triple-junction architecture. GaAs and Si form a non-ideal bandgap combination, which poses a challenge to the current matching requirement. As band-to-band absorption in GaAs is almost two thirds of that in Si, absorption can be balanced by replacing the GaAs top junction with a GaAs/GaAs double junction. This opens up a possibility for an integrated two terminal solar cell for the GaAs-Si material system. In this study, we numerically evaluate the efficiency and energy yield potential of the GaAs/GaAs/Si triple-junction architecture. We find that, with state-of-the-art material quality, the GaAs/GaAs/Si architecture has the potential to achieve 33.0% efficiency, and harvesting efficiencies between 31.4% and 32.1%. We fabricated a GaAs/GaAs/Si four-terminal tandem solar cell with a mathematically combined efficiency of 20.4%.

Communication range in passive radio-frequency identification (RFID) front-end devices is a critical barrier in the real-world implementation of this low-cost technology. Purely passive RFID tags power up by harvesting the limited RF energy transmitted by the interrogator, and communicate by backscattering the incident signal. This mode of communication keeps manufacturing costs below a few cents per tag, but the limited power available at the tag undermines long-range deployment. In this paper, we present an approach to use photovoltaics (PV) to augment the available energy at the tag to improve read range and sensing capabilities. We provide this extra-energy to the RFID integrated circuit (IC) using minimum additional electronics yet enabling persistent sensor-data acquisition. Current and emerging thin-film PV technologies have significant potential for being very low-cost, hence eliminating the barrier for implementation and making of PV-RFID wireless sensors. We reduce the long-range PV-RFID idea to practice by creating functional prototypes of: 1) a wireless building environment sensor to monitor temperature and 2) an embedded tracker to find lost golf balls. The read range of PV-RFID is enhanced eight times compared to conventional passive devices. In addition, the PV-RFID tags persistently transmit large volumes of sensor data (>0.14 million measurements per day) without using batteries. For communication range and energy persistence, we observe good agreement between calculated estimates and experimental results. We have also identified avenues for future research to develop low-cost PV-RFID devices for wireless sensing in the midst of the other competitive wireless technologies, such as Bluetooth, ZigBee, long range (LoRa) backscatter etc.

Crystalline silicon comprises 90% of the global photovoltaics (PV) market and has sustained a nearly 30% cumulative annual growth rate, yet comprises less than 2% of electricity capacity. To sustain this growth trajectory, continued cost and capital expenditure (capex) reductions are needed. Thinning the silicon wafer well below the industry-standard 160 μm, in principle reduces both manufacturing cost and capex, and accelerates economically-sustainable expansion of PV manufacturing. In this analysis piece, we explore two questions surrounding adoption of thin silicon wafers: (a) What are the market benefits of thin wafers? (b) What are the technological challenges to adopt thin wafers? In this analysis, we re-evaluate the benefits and challenges of thin Si for current and future PV modules using a comprehensive technoeconomic framework that couples device simulation, bottom-up cost modeling, and a sustainable cash-flow growth model. When adopting an advanced technology concept that features sufficiently good surface passivation, the comparable efficiencies are achievable for both 50 μm wafers and 160 μm ones. We then quantify the economic benefits for thin Si wafers in terms of poly-Si-to-module manufacturing capex, module cost, and levelized cost of electricity (LCOE) for utility PV systems. Particularly, LCOE favors thinner wafers for all investigated device architectures, and can potentially be reduced by more than 5% from the value of 160 μm wafers. With further improvements in module efficiency, an advanced device concept with 50 μm wafers could potentially reduce manufacturing capex by 48%, module cost by 28%, and LCOE by 24%. Furthermore, we apply a sustainable growth model to investigate PV deployment scenarios in 2030. It is found that the state-of-the-art industry concept could not achieve the climate targets even with very aggressive financial scenarios, therefore the capex reduction benefit of thin wafers is advantageous to facilitate faster PV adoption. Lastly, we discuss the remaining technological challenges and areas for innovation to enable high-yield manufacturing of high-efficiency PV modules with thin Si wafers., U.S. Department of Energy (DOE) (Award DE-EE0007535)

Indoor photovoltaic cells have the potential to power the Internet of Things ecosystem, including distributed and remote sensors, actuators, and communications devices. As the power required to operate these devices continues to decrease, the type and number of nodes that can now be persistently powered by indoor photovoltaic cells are rapidly growing. This will drive significant growth in the demand for indoor photovoltaics, creating a large alternative market for existing and novel photovoltaic technologies. With the re-emergence of interest in indoor photovoltaic cells, we provide an overview of this burgeoning field focusing on the technical challenges that remain to create energy autonomous sensors at viable price points and to overcome the commercial challenges for individual photovoltaic technologies to accelerate their market adoption. The Internet of Things (IoT) ecosystem promises large networks of connected devices collecting the big data upon which our medical, manufacturing, infrastructure, and energy industries will be monitored and optimized. Billions of wireless sensors are expected to be installed over the coming decade, with almost half to be located inside buildings. Currently, the use of batteries to power these devices places significant constraints on their power consumption, where the range and frequency of data transmission are curtailed to achieve sufficient battery life, and the range of applications is also limited to the ones that allow battery replacement. Additional operation and maintenance costs are also incurred by providing replacement batteries. Indoor photovoltaics has the potential to solve these hardware issues, providing greater reliability and operational lifetimes in wireless sensor networks. Persistently powering individual nodes by harvesting ambient light using small ∼cm2 photovoltaic cells is becoming possible for more and more wireless technologies and devices. Characterizing IPV cells is a growing research field with the performance of a considerable number of different PV technologies having now been measured under ambient light sources. Given the interest in commercializing different photovoltaic cells in this growing market, we discuss here the outstanding research questions that must be answered by the indoor photovoltaic community to enable self-powered, indoor-located IoT nodes. ©2019 Elsevier Inc., EU Horizon 2020 research & innovation program - Marie Skłodowska-Curie grant (agreement no. 746516), GS1-MIT AutoID labs collaboration, DOE-NSF ERF for Quantum Energy and Sustainable Solar Technologies (QESST), Singapore's National Research Foundation t- Singapore MIT Alliance for Research & Technology’s “Low energy electronic systems (LEES)” IRG

While the installed photovoltaic capacity grows to a terawatt scale, effects of global climate change unfold. The question arises, how a changing climate, and especially raising temperatures, will affect the performance of PV installations in the future. In this paper we present an estimate of the reduction in energy yield for silicon PV installations due to global warming in the year 2100. Using IPCC global warming scenarios and published temperature coefficients for todays silicon PV panels, we project median reductions in annual energy output of today's silicon solar panel of 15kWh per kWP, with reductions up to 50kWh per kWP in some areas. Higher efficiency cells and advanced cell and module architectures can significantly reduce these losses. Cell concepts with higher voltage are particularly effective in reducing temperature induced losses., 3 pages, 3 figures

Solar energy has the potential to play a central role in the future global energy system because of the scale of the solar resource, its predictability, and its ubiquitous nature. Global installed solar photovoltaic (PV) capacity exceeded 500 GW at the end of 2018, and an estimated additional 500 GW of PV capacity is projected to be installed by 2022–2023, bringing us into the era of TW-scale PV. Given the speed of change in the PV industry, both in terms of continued dramatic cost decreases and manufacturing-scale increases, the growth toward TW-scale PV has caught many observers, including many of us (1), by surprise. Two years ago, we focused on the challenges of achieving 3 to 10 TW of PV by 2030. Here, we envision a future with ∼10 TW of PV by 2030 and 30 to 70 TW by 2050, providing a majority of global energy. PV would be not just a key contributor to electricity generation but also a central contributor to all segments of the global energy system. We discuss ramifications and challenges for complementary technologies (e.g., energy storage, power to gas/liquid fuels/chemicals, grid integration, and multiple sector electrification) and summarize what is needed in research in PV performance, reliability, manufacturing, and recycling.