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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Reducing Uncertainty of Fielded Photovoltaic Performance (Final Technical Report)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.

14 SOLAR ENERGY↗
DOE OSTI2025

Technoeconomic Analysis (TEA)

Techno-economic analysis (TEA) for the DuraMAT Consortium includes the following areas: linking solar photovoltaic (PV) technology trends to reliability implications; providing a framework to calculate technology costs, yielding insights useful for research decision-making, proposals, technology selection, and publications; and examining technology tradeoffs considering lifecycle project economics. This poster highlights results from the first and second iterations of the DuraMAT Technology Scouting reports as well as the updated Simplified PV Levelized Cost of Energy (LCOE) Calculator.

14 SOLAR ENERGY↗
DOE OSTI2024

Predicting Instability and the Effect of Wind Loading on Single-Axis Trackers

As PV modules continue to trend toward larger, thinner, and more flexible forms they grow more susceptible to damage from dynamic wind loading. As a result, understanding the impact of wind on PV systems, particularly when mounted on solar-tracking hardware, and identifying robust, stable array layouts and stow strategies is becoming increasingly important for the PV community. In our ongoing DuraMAT project, we are developing an open-source software package, PVade (PV aerodynamic design engineering), to simulate the cascading fluid-structure interaction that occurs within solar-tracking arrays to enable researchers to test hardware, layout, and tracker control changes, leading to enhanced stability and a reduction in wind-driven damage. We will give an overview of the PVade software, highlighting recent user-interface and algorithm developments, before presenting the latest outcomes from our ongoing validation campaign in which we analyze and compare with experimental data obtained from a DuraMAT 1 project. From there, we will present simulated results from a larger, multi-row array and highlight relationships between varying tracker angles and stability as measured by different experimentally validated metrics.

fluid structure interaction↗
DOE OSTI2020

High-Throughput Optical Mapping for Accelerated Stress Testing of PV Module Materials

The study will provide guidance towards the design and application of larger and/or outdoor-use intended instruments. Measurement capability will be benchmarked against existing specimens, examined in a round-robin study using conventional spectrophotometer instruments. Data acquisition and storage will be integrated into the DuraMAT DataHub network along with data visualization tool, facilitating sharing of information.

14 SOLAR ENERGY↗
DOE OSTI2021

Glass/Glass Focus Group: Module Technology and Durability Roadmap

The G/G Focus Group was created one year ago with a mission to identify critical research directions in G/G packaging and how research in DuraMAT 2.0 can drive this area forward. This is a summary of the research on Glass/Glass modules discussed over the past year.

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS,E↗
DOE OSTI2023

Trends in Field and Laboratory Performance of Photovoltaic Modules and Materials

This DuraMAT project intends to identify bill of materials (BOM) and/or process control measures for photovoltaic modules with representative failure modes as informed by accelerated and field tests to guide next steps in module and material design. We have begun work to identify correlation between module field performance and accelerated testing results by cross-comparing a module database of accelerated test results with a module database of fielded systems. We have identified fielded systems with degraded performance, and intend to compare this to the results observed in certification and qualification testing. We also intend to apply lessons and observations from accelerated tests to the field, where it may be possible to mitigate or avoid degradation in the field.

DuraMAT↗
DOE OSTI2023

Follow-Up from the Photovoltaic Reliability Workshop (PVRW): Cost Modeling Capabilities to Evaluate Trends in PV Technologies

NREL's Solar and Storage Techno-Economic Analysis (TEA) team reviews live polling results from the Photovoltaic Reliability Workshop (PVRW) related to module technology trends believed to have the greatest reliability impacts. Then, the team reviews the publicly available tools and inputs necessary for evaluating reliability tradeoffs between initial module and system costs, degradation profiles, and levelized cost of energy (LCOE). These tools include the online Detailed Cost Analysis Models (DCAM) and the DuraMAT LCOE calculator.

cost modeling↗
DOE OSTI2024

Photovoltaics Research at NREL

General overview of NREL research, including scaling to terawatt PV, reliability testing, DuraMAT, perovskites, silicon PV, CdTe, III-V, and tandems.

CdTe↗
DOE OSTI2021

Albedo Data Sets for Bifacial PV Systems

For use by the PV and financial communities to better estimate the performance and to reduce the risk of bifacial PV systems, data sets of ground albedo and associated meteorological data were developed by using existing measurement network data and data contributed by the PV industry. The data sets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Complete information is presented in a user's guide and data are available for download from NREL's DuraMAT website.

albedo↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2025

GeoGridFusion (Open-Source Geospatial Toolkit for Solar Data Integration​) [SWR-25-19]

GeoGridFusion facilitates the usage and storage of gridded geospatial satellite data by users outside of the National Renewable Lab (NREL), particularly those without access to high-performance computing (HPC) resources. This tool builds on work done by the PVDegradationTools project for DuraMAT, with the goal of making our advancements from this project widely accessible. This repo contains utilities to allow for the storage of user downloaded geospatial weather data by providing a local datastore for storage and spatial queries, supporting large-scale analyses without the need for HPC resources.

Ford, Tobin [National Renewable Energy Laboratory ↗
DOE OSTI2026

Defining the 50-Year Module: Luck, Design, or Both?

Poster presenting a new Duramat project that will develop a science-based definition of a "50-year solar panel" by combining reliability data, climate risks, design choices, and long-term cost analysis. The work aims to clarify what is needed to enable longer-lasting, high-value solar systems.

14 SOLAR ENERGY↗
DOE OSTI2020

Albedo Data for Bifacial PV Systems Update

For use by the PV and financial communities to better estimate the performance and to reduce the risk of bifacial PV systems, data sets of ground albedo and associated meteorological data were developed by using existing measurement network data and data contributed by the PV industry. The data sets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Complete information is presented in a user’s guide and data are available for download from NREL’s DuraMAT website.

albedo↗
DOE OSTI2020

Albedo Data Sets for Bifacial PV Systems: Preprint

For use by the PV and financial communities to better estimate the performance and to reduce the risk of bifacial PV systems, data sets of ground albedo and associated meteorological data were developed by using existing measurement network data and data contributed by the PV industry. The data sets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Complete information is presented in a user’s guide and data are available for download from NREL’s DuraMAT website.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

PV Field Performance Including Fleet and Bifacial Field Data

This presentation provides a review of two recent presentations - the 2021 PV Fleet Performance Data Initiative covered by the PVRW presentation, and bifacial system performance presented to a February DuraMAT working group. Details include degradation rate distributions from >6000 inverter channels, monthly performance vs expected, bifacial field studies of energy gain and field degradation.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Updates to the Instant Online PV LCOE Calculator Tool

Levelized cost of energy (LCOE) is an important metric for techno-economic analysis of PV modules, systems, and performance. An NREL team within DuraMAT has developed a simplified PV-specific LCOE calculator as an alternative to the NREL System Advisor Model (SAM) for applications that do not have the degree of detail required by SAM. This webinar will review recent data and new features that have been added to the simplified calculator this year, including breakeven buttons, commercial system options, and inverter loading ratios. It will also review the structure of the source code on GitHub, how to edit a local version of the calculator, and walk through some example use cases for the calculator.

calculator↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗