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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
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DOE OSTI2026

Challenges, Technological Pathways and Trade-Offs of Perovskite Solar Modules for Long-Term Operation

Perovskite solar modules (PSMs) have emerged as a promising photovoltaic technology due to their high efficiency, low fabrication cost and compatibility with lightweight and flexible applications. However, ensuring long-term reliable performance under real-world conditions remains a critical barrier to commercialization. PSMs degrade through mechanisms that differ substantially from those affecting established technologies such as silicon, particularly under environmental stressors like ultraviolet light, oxygen, temperature cycling and reverse bias. Here we provide an analysis of the degradation pathways specific to perovskite modules and discuss why standard accelerated tests often fail to predict outdoor performance. We conceptualize challenges across material, device and module levels and evaluate strategies to mitigate ion migration, interfacial breakdown and encapsulation failure. By highlighting the need for realistic testing protocols and durable materials, we propose a framework highlighting key challenges, technological pathways and the trade-offs required to extend perovskite module lifetimes towards long-term operation, aiming to guide the development of PSMs capable of a 30-year operational lifetime.

14 SOLAR ENERGY↗
DOE OSTI2024

Modeling and Validation of a Residential Multi-Functional Variable Refrigerant Flow Heat Pump System with Heat Recovery

To bridge the existing gap in modeling the variable refrigerant flow heat pump systems with heat recovery (VRFHR), we developed a suite of dynamic VRFHR system models in Modelica. These models are specifically tailored for residential multi-functional VRFHR (MF-VRFHR) applications, including space conditioning and domestic hot water (DHW) heating, utilizing both the TIL library for HVAC equipment and the Buildings library for thermal load calculations. The development comprises essential component models, including the newly developed heat recovery unit (HRU), along with system models that integrate the heat pump system and building envelope. These system models accommodate various operational modes such as heating-only, cooling-only, and heating-recovery (including heating-dominant and cooling-dominant) modes. Furthermore, we propose an efficient optimization-based model calibration method that identifies critical model parameters while utilizing a small amount of data obtained from either real systems or manufacturer's specifications. We demonstrate the effectiveness of these models and the proposed calibration method for a MF-VRFHR system installed in Richland, WA. The developed models are calibrated and validated using data collected under different operational modes during both heating and cooling seasons. The results show that the models capture the system dynamics and achieve high accuracy, with the coefficient of the variation of the root-mean-square-error less than 15% for variables such as outdoor unit power consumption, compressor speed, space temperature and DHW temperature. The validated models serve as a reliable representation of the MF-VRFHR system, facilitating the development and validation of optimized controls needed to realize the full benefits of integrated heat pump systems. Future research will utilize these models to develop advanced controls and optimize system performance for improved energy efficiency and demand flexibility.

Modeling, Variable refrigerant flow (VRF) systems,↗
DOE OSTI2022

Perovskite Photovoltaics: Venturing Outdoors

Metal halide perovskite solar cells have shown promising performance, but mainly on small-area devices and under laboratory conditions. Now, researchers have demonstrated the fabrication of large-area devices assembled and packaged into modules and reported on their operation outdoors.

field testing↗
DOE OSTI2022

Motivation, benefits, and challenges for new photovoltaic material & module developments

Abstract In the last decade and longer, photovoltaic module manufacturers have experienced a rapidly growing market along with a dramatic decrease in module prices. Such cost pressures have resulted in a drive to develop and implement new module designs, which either increase performance and/or lifetime of the modules or decrease the cost to produce them. In this paper, the main motivations and benefits but also challenges for material innovations will be discussed. Many of these innovations include the use of new and novel materials in place of more conventional materials or designs. As a result, modules are being produced and sold without a long-term understanding about the performance and reliability of these new materials. This has led to unexpected new failure mechanisms occurring few years after deployment, such as potential induced degradation or backsheet cracking. None of these failure modes have been detected after the back then common single stress tests. New accelerated test approaches are based on a combination or sequence of multiple stressors that better reflect outdoor conditions. That allows for identification of new degradation modes linked to new module materials or module designs.

Oreski, G. (ORCID:0000000342239047)↗
DOE OSTI2024

PVDeg: Development of a Streamlined Tool for PV Degradation Modeling

The photovoltaic (PV) industry constantly aims for lower costs, higher-efficiency cells, and improved module designs. These trends lead to using new materials, designs, and manufacturing processes, resulting in a continually changing technological landscape. These changes can potentially introduce new, unknown degradation mechanisms and failure modes that are difficult to diagnose, analyze, test, and model. This introduces uncertainty into the expected lifetime of PV modules of 25 to 50 years. research efforts aim to achieve this while keeping performance degradation at a minimum for decades. This puts considerable pressure on improving the accuracy of long-term durability and reliability assessments. There is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. Because the core of this type of analysis involves calculations that are complicated but ubiquitous for many degradation processes, an enhanced predictive modeling framework will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this work, we present an online tool that can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. The tool will simplify many of the routine computational operations that are common to many degradation studies. The prediction tool will be built modular and published as open source, enabling users to expand on the existing framework. This repository will contain various degradation models and material parameters suitable for the reliability and durability assessment of materials and components deployed outdoors.

degradation↗
DOE OSTI2024

Dual-Image Color Normalization to Enable High-Performance Concentrating Solar Optical Metrology

Concentrating Solar Power (CSP) requires precision mirrors, and these in turn require metrology systems to measure their optical slope. In this project we studied a color-based approach to the correspondence problem, which is the association of points on an optical target with their corresponding points seen in a reflection. This is a core problem in deflectometry-based metrology, and a color solution would enable important new capabilities. We modeled color as a vector in the [R,G,B] space measured by a digital camera, and explored a dual-image approach to compensate for inevitable changes in illumination color. Through a series of experiments including color target design and dual-image setups both indoors and outdoors, we collected reference/measurement image pairs for a variety of configurations and light conditions. We then analyzed the resulting image pairs by selecting example [R,G,B] pixels in the reference image, and seeking matching [R,G,B] pixels in the measurement image. Modulating a tolerance threshold enabled us to assess both match reliability and match ambiguity, and for some configurations, orthorectification enabled us to assess match accuracy. Using direct-direct imaging, we demonstrated color correspondence achieving average match accuracy values of 0.004 h, where h is the height of the color pattern. We found that wide-area two-dimensional and linear one-dimensional color targets outperformed hybrid linear/lateral gradient targets in the cases studied. Introducing a mirror degraded performance under our current techniques, and we did not have time to evaluate whether matches could be reliably achieved despite varying light conditions. Nonetheless, our results thus far are promising.

14 SOLAR ENERGY↗
DOE OSTI2025

BACKFLIP: A Comparison of Market-Benchmark Backsheet Technologies to Novel Non-Fluoro-Based Coextruded Materials and Their Correlation and Impact on PV Module Degradation Rates: Final Results of the Study at 4000 Hours or 2 Years

As the photovoltaic (PV) industry is rapidly expanding around the world, there has been an increasing interest in extending the lifespan of PV modules. Concern has also emerged regarding the recyclability of modules and their component materials, including fluoropolymer-based backsheets. Laminated polyethylene-terephthalate (PET) core backsheets have traditionally been used in the PV industry, but new, co-extruded polyolefin (PO) backsheets show promise as an improved alternative. Mini-module and coupon samples of seven different backsheets (made of layers including contemporary PET and fluoropolymers, novel PO, and polyamide (PA) materials) were run through hygrometric- or UV photolytic-accelerated aging to identify and better understand each material's degradation modes and the backsheets' field reliability. In addition to the artificial aging, the natural weathering methods used in this study are described. The comprehensive set of chemical, mechanical, and structural characterizations at intermittent read points in this study is presented, including: visual appearance and color; gloss; mechanical tensile testing; I-V performance; electroluminescence (EL) imaging; dielectric breakdown; FTIR-chemical structure; X-ray-polymer structure (WAXS); and DSC-crystalline content. After 4000 h of accelerated aging or 2y of outdoor aging, a strong correlation occurs between initial physical characteristics (mechanical tensile test) and operating performance (EL and I-V characteristics).

14 SOLAR ENERGY↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2022

CSP Optical Facilities at the National Renewable Energy Laboratory

The concentrating Solar Power (CSP) Optical Facilities at NREL comprise unique optical tools and facilities for testing and characterizing concentrating solar optical components. These facilities are maintained to aid researchers and industry efforts to reduce market barriers, improve the performance, reduce the cost, and improve the lifetime and reliability of CSP materials, components, subsystems, and integrated concepts, where accurate and timely characterization methods are needed. To reach this goal, NREL is committed to collaborating in ongoing and projected research needs for industry, academia, and government entities, in the development and characterization of low-cost, high-performance reflector and absorber materials in addition to their components-a key element in the conversion of concentrated sunlight into energy. To accomplish this mission, the core laboratory includes the Advanced Optical Materials Laboratory, indoor and outdoor accelerated weather testing tools, "Distant Observer" for parabolic troughs, the High Flux Solar Furnace, and the ultra-accelerated weather testing tools. This equipment enables to rapidly respond to new challenges with novel solutions that align with the goals of the Solar Energy Technology Office (SETO) at the U.S. Department of Energy. The goal of this paper is to present the state-of art-optical facilities available at NREL that can help support the research needs of the CSP community. Therefore, we review here the different components of the CSP Optical Facilities at NREL and highlight their characteristics and importance for the CSP community.

accelerated weather testing↗
DOE OSTI2025

Medium Voltage Testbed for Comparing Advanced Power Line Sensors vs. Transformers with Electrical Grid Events

Electrical utilities have relied upon potential transformers (PTs) and current transformers (CTs) for very accurate metering and to provide reliable signals for protective relays. These devices measure phase voltages and currents and are commissioned by electrical engineers. PTs/CTs can detect and react to various electrical anomalies that could adversely affect electrical grid operations. Less expensive alternative sensing technologies offer the possibility of wider deployment, particularly in grids that employ distributed energy resources. In this work, the performance of an advanced medium-voltage sensor is compared with that of a reference PT and a CT and experimentally evaluated for different power grid scenarios on an advanced outdoor power line sensor testbed at the U.S. Department of Energy’s Oak Ridge National Laboratory. The sensor is based on a capacitive divider for voltage monitoring and a Rogowski coil with an integrator for current monitoring. This study simulated a power grid model based on a utility circuit at the Riverside EPB of Chattanooga. The simulation circuit was created with MATLAB/Simulink software and was integrated into an RT-LAB project to run with the OP4510 real-time simulator at the OPLST. During the tests, the real-time simulations were run for 40 s, and the signal to record the test event with the power meter was set at 30 s for the event-trigger circuit. The advanced outdoor power line sensor testbed has a real-time simulator that is used to generate transient scenarios (e.g., electrical faults, capacitor bank operation, and service restoration), while the analog signals are recorded by the same high-resolution power meter. The behaviors of analog signals, harmonic components, total harmonic distortion, and crest factors are assessed for this power line sensor and compared with those of the reference PT/CT because of the absence of testing standards for advanced outdoor power line sensors. The results showed that this OPLS technology responded identically to the PT and CT under all conditions.

Piesciorovsky, Emilio [ORNL]↗
DOE OSTI2022

Simulation, Challenge Testing and Validation of Solutions for Residential and Commercial Occupancy Sensing/Counting and CO2 Sensing (Final Report)

Carbon dioxide sensors used for monitoring and control applications in buildings are known to be sensitive to long term drift and can be affected by variations in temperature, humidity, other gas interferents, pressure and other factors that cannot be corrected through the typical auto-calibration methods often embedded in the sensor electronics. This investigation was part of the Category D defined in the ARPA-E Saving Energy Nationwide in Structures With Occupancy Recognition (SENSOR) Financial Assistance Funding Opportunity Announcement No. DE-FOA-0001737, FFDA No. 81.135, January 18, 2017. The FOA defined target criteria for the evaluation of the Category C commercial CO2 sensors selected for review and development through the ARPA-E SENSOR program and charged the Iowa State Category D team with developing an evaluation methodology for Category C sensors in accordance with these criteria. As part of the work, the Iowa State team chose to engage the standards community in the development of the evaluation methodology. Standard development work in the D22.05 Indoor Air and D22.03 Ambient Atmospheres and Source Emissions subcommittees of ASTM International Committee D22 on Air Quality on the evaluation of low cost sensors and provision of guidance for using indoor carbon dioxide concentrations to evaluate indoor air quality and ventilation is of particular relevance to the Category D efforts. To date, three draft standards with specific input by the Iowa State Category D team have passed the subcommittee balloting stage and are being balloted in December 2022 and January 2023 in the main D22 committee. In addition to the aforementioned standards to which the PI was a contributing author, intellectual property developed in this project pertaining to “An Automated System for Evaluation of the Long Term Performance of Carbon Dioxide Sensors” and “An Automated Ground Truth System for Real-Time Reliability Assessment, Capture of Control Decisions and Energy Savings Measurements for Occupancy Recognition Systems and Other Applications” are the other major deliverables of the project. These standards and the developed intellectual property are expected to benefit the public in a number of ways stemming from their provision of means to benchmark the performance of carbon dioxide and other sensor systems. In particular, there is high interest in techniques to demonstrate the performance of low-cost carbon dioxide sensors which can be used for typical HVAC applications (e.g., demand controlled ventilation, outdoor and indoor air monitoring, estimation of occupancy, etc.).

42 ENGINEERING↗
DOE OSTI2024

From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.

Bolme, David↗
DOE OSTI2022

Development and Validation of Algorithms That Analyze Communicating Thermostat Data to Identify Enclosure Retrofit Opportunities

Annual energy savings of up to $\$ 4$ to $\$ 5$ billion could be achieved nationwide through basic insulation and heating system retrofits of existing homes. However, current utility energy efficiency programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and online advertising that lack specificity about home-specific retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments (HEAs) that are inconvenient to homeowners, expensive, and of variable accuracy. We developed computational algorithms that automatically analyze communicating thermostat (CT) heating data that could be used to increase the customer uptake of insulation and air sealing energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. The algorithms are based on an extended second-order grey-box model that characterizes a building’s thermal response using lumped elements, coupled with an empirical model of infiltration that accounts for both wind and stack effects. The basic parameters of the model correspond to actual physical parameters of the home, i.e., the home’s overall R-value of and the building envelope ACH50. Unlike the conventional approach, which estimates model parameters based on the best fit to the observed time-dependent room temperature, our approach derives correlations between the daily heating system runtime and temperature difference (indoor-outdoor) that are more robust to data quality issues in real-world applications. We also used HEA data for algorithm development and validation. With the help of our utility partners, Eversource and National Grid, we obtained data sets for hundreds of Massachusetts homes. For each home, these data sets included three sets of information anonymized by the utility: (1) CT data (HVAC runtime, room temperature, and, for some vendors, outdoor temperature and wind speed) collected by the CT vendor (one of three) over a heating season, (2) HEA report performed by the HEA vendor (same vendor for all homes), (3) Monthly utility gas bills coincident with the CT data (3 to 24 per home, depending on availability). For some homes, we also obtained blower-door test results. Initially, we applied the algorithms developed to homes with a single CT and then extended them to homes with two CTs by using an equivalent home approach. Finally, we developed algorithms for prediction of energy savings and a methodology of comparing our predictions with those generated by HEAs. The main technical results indicate that we can reliably identify homes with insulation and/or air sealing retrofit opportunities and provide accurate savings predictions. Our hypothesis is that the algorithms could be applied to utility energy efficiency programs to identify homes that could realize significant energy savings from insulation and/or air sealing retrofits. This information could then be used to reach out to those homes with highly customized outreach, thereby delivering increased program energy savings and cost-effectiveness. This would: Significantly increase the uptake rate of on-site HEAs, and Significantly increase the fraction of HEAs resulting in ECM implementation. To test these hypotheses, we designed and conducted a randomized controlled trial (RCT). The RCT results suggest that personal messaging leads to a two- to five-fold increase in the HEA uptake rate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Rapid advances enabling high-performance inverted perovskite solar cells

Perovskite solar cells (PSCs) that have a positive–intrinsic–negative (p–i–n, or often referred to as inverted) structure are becoming increasingly attractive for commercialization owing to their rapid increase in power conversion efficiency, easily scalable fabrication, reliable operation and compatibility with various perovskite-based tandem device configurations. In this report we review key material and device considerations for making highly efficient and stable p–i–n PSCs. First, we summarize key advances in charge transport materials, which were critical to the rapid power conversion efficiency progress. Second, we discuss promising perovskite compositions and fabrication methods. We highlight various additive engineering approaches to improve the perovskite layer as well as interface engineering strategies that target either the buried or top perovskite surface layer. Third, we review progress in tandem devices, focusing on optimization of the interconnection layer. Next, we summarize the status and strategies for improving p–i–n PSC stability, especially considering the challenges of outdoor applications. We also provide prospects for future research directions and challenges.

14 SOLAR ENERGY↗
DOE OSTI2022

Benchmarking Demand Flexibility in Commercial Buildings and Flattening the Duck – Addressing Baseline and Commissioning Challenges

With the transition from our traditional electric grid to a cleaner grid with renewable power generation, there is a need to enable building loads to be flexible. Load shedding and shifting will be essential for flattening the “Duck” for decarbonization. This paper explored the trend in the timing of DR events as a reflection of the grid’s needs using recent four years of event data from 203 retail stores in 11 states. The events are becoming significantly shorter with 2-hour duration being the most popular; shifting to late afternoon and early evening is another trend beyond California. Benchmarking will be essential for accounting DF as a reliable grid resource. This paper addresses a challenging aspect of benchmarking – inaccuracies in counterfactual baseline methods can introduce significant DF metrics variations in addition to weather and building characteristics related factors. The conventional “10/10” with adjustment baseline method has inherent limitation by design for load shifting applications. Therefore, it is imperative to identify alternative methods. This study compared three hourly regression baseline methods with “10/10” methods using two groups of commercial buildings that participated in DR programs: (1) 121 big-box retail stores, and (2) 11 office buildings in CA. The 14-day hourly outdoor temperature regression method was found to produce least error in the tested datasets and is promising for load shifting. The paper also pointed out that commissioning issues can also be a significant barrier for achieving consistent DF performance, which building managers and utilities should be aware of.

Liu, Jingjing↗
DOE OSTI2026

Comprehensive assessment of metrology techniques for heliostat efficiency and performance evaluation

Concentrating solar power plants, specifically central receiver type systems and their heliostat field, are struggling with negative reputation in the USA, due to perceived underperformance and reliability issues. This is in part due to a lack of standards for performance assessment as well as overly simplified techno-economical models. A better understanding of influences and losses along the solar radiation path from the sun, across the solar collector to the receiver, increases the fidelity of heliostat efficiency assessment as well as solar field performance predictions. Such data are currently scarce and require a complete set of metrology capabilities to evaluate direct solar irradiance, sun shape, atmospheric attenuation, reflectance, collector shape, slope errors and total beam dispersion. In preparation for establishing a 3rd party metrology platform in collaboration with Sandia National Labs, NLR conducted a scoping study on available metrology. We present an extensive overview of techniques and commercial systems for each category. Our work includes an analysis to increase understanding of strengths and limitations of the many techniques used for surface shape and slope measurement. This applies to a controlled, indoor or outdoor laboratory environment assessing a single heliostat.

14 SOLAR ENERGY↗
DOE OSTI2020

Post-occupancy evaluation of indoor environmental quality in ten nonresidential buildings in Chongqing, China

Indoor environmental quality (IEQ) is an essential element of sustainable building performance. To advance knowledge of indoor environmental quality in Chinese nonresidential buildings, this study conducted post-occupancy evaluation (POE) in varied use areas within schools, hospitals, offices, hotels and shopping malls through environmental measurements and occupant surveys in 10 buildings over four seasons in Chongqing, China. Measured thermal conditions were more frequently within comfort standards in winter than in summer according to the predicted mean vote – predicted percentage dissatisfied model; but the model did not reliably predict thermal satisfaction. There was insufficient illuminance in 28% of studied areas and high illuminance in shopping malls led to complaints about glare. Sound pressure exceeded the Chinese guideline in all buildings with shopping malls having significantly higher levels than other buildings. CO 2 was generally below the Chinese national guideline of 1000 ppm; the two percent of measurements that exceeded the guideline occurred in classrooms, hospital wards and underground supermarkets. Formaldehyde did not exceed the limit level of 0.1 mg/m3 in any space. Most of the buildings did not provide effective protection from outdoor fine particulate matter (PM2.5), and infiltration from outdoors was the main factor for exceeding health-based guidelines indoors. Shopping malls had lower IEQ satisfaction than offices, schools and hospitals. Sound was the factor with the lowest satisfaction level among the four principal environmental factors. Overall, this study demonstrates value of POE on identifying deficiencies in indoor environment and provides a reference for assessment of IEQ in nonresidential buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Long-term impact of light- and elevated temperature-induced degradation on photovoltaic arrays

Low levelized cost of electricity (LCOE) has been identified as critical for widespread adoption of photovoltaics (PV) without subsidies. Maintaining decades-long high-energy production is often an under-recognized opportunity in meeting cost goals because component lifetimes are not fully quantified at the time of manufacture. Whereas certain standardized tests minimize risk of early failure, there is little guidance to quantitatively predict degradation (or lack thereof) over decades, based on accelerated tests. In this article, we move toward bridging the understanding between indoor accelerated tests and outdoor performance data, with the goal of predicting energy yield with enough accuracy to inform financial decisions. Light- and elevated temperature-induced degradation (LETID) in p-type Si modules is analyzed in terms of impact on long-term module performance and thus LCOE. A method to predict the progression of LETID, using fixed kinetic constants and a numerical solution to the basic reaction rate equations, is detailed. Predictions are compared against both published data and that new to this study. These data include both indoor accelerated tests and fielded modules. We use the results in financial models to derive LCOE of modules in different climates with varying amounts of LETID, including uncertainty. Cost models based on the predictions indicate that LETID has a significant and climate-dependent impact on LCOE. Finally, we show that - even given the uncertainties identified in the study - these financial calculations can provide useful guidance to quantify risk based on accelerated test results. The analysis serves as an example of developing a predictive approach to PV reliability using physics of failure.

14 SOLAR ENERGY↗