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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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DOE OSTI2024

PV Lifetime Project (2024 NREL Annual Report)

DOE’s PV Lifetime project was initiated in 2016 with the goal of accurately characterizing the early-life evolution of photovoltaic (PV) field performance. Different PV cell and module technologies result in different initial degradation rates due to effects like light-induced degradation (LID) and light & elevated temperature-induced degradation (LeTID). To accurately characterize the initial field degradation of maximum power (Pmp) requires the use of high-accuracy indoor IV curve measurements at standard test conditions. Therefore, PV modules involved in this study are removed from the field once or twice per year and brought indoors for measurement under constant temperature and irradiance conditions.

14 SOLAR ENERGY↗
DOE OSTI2023

Global horizontal spectral irradiance in Albuquerque - Data and Resources

The Photovoltaic Systems Evaluation Laboratory (PSEL) at Sandia National Laboratories (SNL) in Albuquerque, NM has measured global horizontal spectral irradiance nearly continuously from September 2019 to March 2022. During this time other broadband irradiance measurements (global horizontal, direct normal, diffuse horizontal and global normal) and weather variables were also recorded. For this dataset PV Performance Labs (PVPL) has pulled together data from both sources to assemble a calendar year spectral data set for use in photovoltaic research. It is composed of twelve segments of 15 days each, beginning with January 1-15 and ending with December 1-15. The segments are all taken from the two-year period October 2019 to September 2021.

14 SOLAR ENERGY↗
DOE OSTI2023

Global normal spectral irradiance in Albuquerque - Data and Resources

The Photovoltaic Systems Evaluation Laboratory (PSEL) at Sandia National Laboratories (SNL) in Albuquerque, NM has measured global normal spectral irradiance nearly continuously from August 2013 to April 2018. During this time other broadband irradiance measurements (global horizontal, direct normal, diffuse horizontal and global normal) and weather variables were also recorded. For this dataset PV Performance Labs (PVPL) has pulled together data from both sources to assemble a full calendar year spectral data set for use in photovoltaic research. It is composed of eight continuous segments of different durations taken from the two-year period September 2013 to August 2015.

photovoltaics↗
DOE OSTI2025

Comprehensive Evaluation of Agrivoltaics Research: Breadth, Depth, and Insights for Future Research

Agrivoltaics integrates agricultural production with solar energy generation to address challenges related to land use, food security, and renewable energy development. This study provides the most comprehensive evaluation to date of global agrivoltaic research, aiming to classify the literature, identify strengths and gaps, and guide future work. We systematically screened over 3000 English-language publications through 2023 for relevant agrivoltaic publications. A total of 670 studies were categorized in the InSPIRE Data Portal across five agrivoltaic activities and multiple hierarchical themes, including physical, biological, technological, social, and crosscutting domains. We found that research was concentrated on crop production, microclimate dynamics, and PV performance, with gaps in areas like human health, wildlife, policy, and standardized methodologies. Although the U.S. emphasizes animal grazing and habitat-based systems in practice, most U.S.-based studies focused disproportionately on crop production. The analysis revealed uneven geographic and topical representation and highlighted a lack of integrated, interdisciplinary approaches. This study concludes that while agrivoltaic research has grown rapidly, more coordinated efforts could support standardized data collection, address overlooked ecological and social impacts, and align research focus with real-world system implementation, ultimately improving the scalability and successful deployment of agrivoltaic systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Limerick Safety-Related Instrumentation and Control Upgrade Human Factors Engineering Preliminary Validation Report

This document is a results summary report for the human factors engineering (HFE) preliminary validation (PV) performed for the Limerick Generating Station (LGS) Safety-Related (SR) Instrumentation and Control (I&C) Upgrade Project at the Idaho National Laboratory (INL) Human Systems Simulation Laboratory (HSSL). This occurred during the week of February 20, 2023.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Reliability, Cost and Performance Risks of PV Connector Failures

This presentation is to guide interactive discussion by posing questions to workshop participants. Topic is reliability, maintenance cost, and performance risk associated with failure of PV connectors which make electrial connections between components.

connector↗
DOE OSTI2022

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

photovoltaics↗
DOE OSTI2023

AI and ML Applications for PV Reliability and System Performance

This poster discusses AI and ML topics in PV reliability and system performance. In particular, automated metadata extraction and QA for fielded solar installations is covered for the PV Fleets Project. Additionally, statistical learning topics for the PVInsight Project are addressed, as well as development of the PV Validation Hub.

algorithm↗
DOE OSTI2024

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

14 SOLAR ENERGY↗
DOE OSTI2022

Accelerating Simulation for High-Fidelity PV Inverter System Reliability Assessment with High-Performance Computing

The overall cost of photovoltaic (PV) systems has shown a downward trend during the last decade; however, PV inverter failures account for the highest cost of operation and maintenance. To address this, reliability tools with powerful computation and better accuracy are required for the lifetime prediction and degradation evaluation of PV inverters. This paper proposes an event-driven parallel computing-based simulator. The proposed simulator applies high-performance computing techniques and other accessory optimization techniques-including cluster merging, adaptive model updates, and steady-state identification-to make reliability assessments for PV inverters under given input mission profiles and operating conditions with high efficiency and high fidelity. The main idea of the simulator and its workflow are introduced. Then, a demo PV inverter system simulator is implemented, and the speedup of the total simulations of the switching model reaches 123.03 times.

high-performance computing↗
DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗
DOE OSTI2021

CIGS photovoltaics: reviewing an evolving paradigm

Copper indium selenide chalcopyrite-structure alloys with gallium (CIGS) are unique among the highest performing photovoltaic (PV) semiconductor technologies. They are structurally disordered, nonstoichiometric materials that have been engineered to achieve remarkably low bulk nonradiative recombination levels. Nevertheless, their performance can be further improved. This review adopts a fundamental thermodynamic perspective to comparatively assess the root causes of present limitations on CIGS PV performance. The topics of selectivity and passivation of contacts to CIGS and its multinary alloys are covered, highlighting pathways to maximizing the electrochemical potential between those contacts under illumination. Further, an overview of absorber growth methods and resulting properties is also provided. We recommend that CIGS researchers consider strategies that have been successfully implemented in the more mature wafer-based GaAs and Si PV device technologies, based on the paradigm of an idealized PV device design using an isotropic absorber with minimal nonradiative recombination, maximal light trapping, and both electron-selective and hole-selective passivated contacts. We foresee that CIGS technology will reach the 25% efficiency level within the next few years through enhanced collection and reduced recombination. To significantly impact power-generation applications, cost-effective, manufacturable solutions are also essential.

14 SOLAR ENERGY↗
DOE OSTI2025

PV Reference Cells for Outdoor Use: Stability Over Four Years of Deployment

Photovoltaic (PV) reference cells are frequently used to evaluate the performance of PV power plants. They provide a measure of irradiance that strongly correlates with the electrical output of PV modules, which makes them very useful for detecting short-term anomalies or long-term degradation in PV power plant output. One very important quality is long-term stability. This report presents observations about the stability of a set of 22 commercial reference cells that have been in continuous operation for a period of four years at the Solar Radiation Research Laboratory (SRRL) site at the National Renewable Energy Laboratory (NREL). The 22 reference cells at the SRRL represent 10 different models from 6 manufacturers.

14 SOLAR ENERGY↗
DOE OSTI2022

Best practices for photovoltaic performance loss rate calculations

Abstract The performance loss rate (PLR) is a vital parameter for the time-dependent assessment of photovoltaic (PV) system performance and health state. Although this metric can be calculated in a relatively straightforward manner, it is challenging to achieve accurate and reproducible results with low uncertainty. Furthermore, the temporal evolution of PV system performance is usually nonlinear, but in many cases a linear evaluation is preferred as it simplifies the assessment and it is easier to evaluate. As such, the search for a robust and reproducible calculation methodology providing reliable linear PLR values across different types of systems and conditions has been the focus of many research activities in recent years. In this paper, the determination of PV system PLR using different pipelines and approaches is critically evaluated and recommendations for best practices are given. As nonlinear PLR assessments are fairly new, there is no consent on how to calculate reliable values. Several promising nonlinear approaches have been developed recently and are presented as tools to evaluate the PV system performance in great detail. Furthermore, challenges are discussed with respect to the PLR calculation but also opportunities for differentiating individual performance losses from a generic PLR value having the potential of enabling actionable insights for maintenance.

14 SOLAR ENERGY↗