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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Methodology to predict annual yield losses and gains caused by solar module design and materials under field exposure

Solar cells and modules are usually optimized separately under standard test conditions (STC). However, the actual performance under field exposure depends on module design, installation and environmental conditions. This work therefore presents a methodology to predict the annual yield of crystalline silicon solar modules for varying module configurations and environments using a time step simulation approach. We define and quantify 12 effects that affect the annual module performance, starting with solar cells in air under STC to a complete module under realistic conditions. We consider the interaction of optical, thermal and electrical effects in the module and combine it with an angular, spectral and time resolved light source. The model enables understanding the impact of the module environment, such as regions with high wind speeds or high diffuse light, on the annual output of a particular module type. We validate our model with measured power and temperature data for crystalline silicon heterojunction solar cell modules and find an agreement of the annual yield within 0.7%. We quantify the annual gains and losses for this module and identify that major losses are caused by elevated operating temperatures (2.3%) and reflection losses at the front glass (2.6%). Critically, the coupling gains at the cell surface increase significantly by 1.5% when considering angular light irradiance compared to normal incidence. To conclude, we apply our Cell-to-Module-Yield method to a 72-cell mono-crystalline PERC module, where the main annual yield losses are caused by reflection at the front glass and ohmic losses in the connector ribbons.

14 SOLAR ENERGY↗
DOE OSTI2024

A Novel Concentrating Solar Weathering Apparatus for Experimental Validation of Multi-Modal Degradation Models

High-performance coatings for Concentrating Solar Power (CSP) receivers are subjected to remarkable environmental stressors during normal operations. Applied to the receiver tubes, these coatings serve to maximize the solar absorptivity of the receiver, transferring as much heat as possible from the solar collectors into the heat-transfer fluid (HTF). The lifecycle of these coatings is not well-defined, and the harsh operational conditions make them difficult to test. NREL has designed, built, and tested an apparatus to expose these samples to design levels of environmental stress and well beyond, into accelerated and destructive conditions. The chamber is actively cooled, monitored, and has the capability to supply humidification for cycling tests, allowing us to test multi-modal degradation and failure conditions at high temperature, high flux, and high humidity conditions. These conditions can catalyze high-temperature oxidation, mechanical degradation, and other modes of absorptivity loss seen in selective solar receiver coatings. The experimental data can feed lifecycle models for expensive and necessarily resilient materials, offering insights to aid maintenance schedules, technoeconomic analysis, and material industry performance benchmarks. This presentation will demonstrate the apparatus design and performance, as well as initial results for aging on a selective receiver coating.

14 SOLAR ENERGY↗
DOE OSTI2025

Solar Radiation Measurements (CRADA Final Report)

Long-term solar radiation measurements are important for understanding solar resource availability and variability for systems design and deployment of cost-effective solar resource technologies. Additionally high-quality traceable solar measurements are required to validate satellite-based solar resource datasets that provide high-resolution long-term data covering the US. The purpose and intent of this agreement is to collect long-term solar radiation and meteorological measurements from the state-of-the-art facility at the University of Arizona to meet the above needs.

14 SOLAR ENERGY↗
DOE OSTI2023

A Machine Learning–Based Approach to Time-series Wave Identification in the Solar Wind

The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multibranch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of 1 yr of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning–based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Wind Loading on Parabolic Trough Collectors: Wind and Structural Loads Measurements at an Operational Powerplant

Concentrated Solar Power (CSP) is a promising method for using solar power for electricity generation with thermal energy storage for industrial applications. Solar collectors constitute almost 1/3 of the total cost of the power plant. One of the primary drivers of unrealiability of these collectors is wind-driven loading of mirrors, support structures, and drives. To date, the design of the solar collector structures has relied on data from wind tunnels that do not adequately capture the dynamic effects observed at scale. NREL initiated a field measurement campaign at the operational Nevada Solar One (NSO) parabolic trough powerplant. At this plant, parabolic trough solar collectors track the sun from east to west in the course of a day and face varying wind loads depending on the wind properties and the angle of the troughs. The aim of the project is a detailed characterization of prevailing wind and turbulence conditions and resulting operational loads on parabolic troughs, providing insights into structural dynamic response, and generating a comprehensive wind-loading dataset for validating simulations of wind loading on collector structures. The measurements at NSO consist of sonic anemometers on masts at different heights to characterize the incoming flow and conditions at four trough rows at the edge of the trough field. In addition, a Doppler Lidar scans the horizontal plane above the troughs. The wind measurements at NSO have been continuously collecting data since October 2021 and are combined with structural load measurements that started in November 2022. The load measurements were installed on the same four outermost trough rows and include support structure bending moments, drive torque moments, dynamic accelerations of the spaceframe, mirror displacement, and tilt angles. Measurements are planned to continue until May 2023, providing a first-of-a-kind, high-resolution multi-month dataset of combined wind and load measurements. In this presentation, we show first results of the measurement campaign. Based on the measurements, we identified three main factors altering the flow over the parabolic troughs: Wind speed, wind direction, and the angle of the parabolic troughs. Most interactions between incoming wind and the trough field are observed when the wind blows perpendicular to the trough rows. In this case, the first rows experience the highest wind speed and block the downwind rows, creating conditions with decreased wind speed and enhanced turbulence within the trough field. Also, turbulent length scales are smaller after the first row. This leads to the highest static loads (bending and torque moments) at the first row but potentially increased dynamic loads within the field. We illustrate our findings with case studies focused on different wind conditions and will present fatigue analysis to highlight the impact of wind-driven loads on collector structures.

CSP collectors↗
DOE OSTI2023

NSRDB (National Solar Radiation Database Software) [SWR-23-77]

This repository contains all of the methods for the NSRDB data processing pipeline. The NSRDB is a serially complete collection of hourly and half-hourly values of meteorological data and the three most common measurements of solar radiation: global horizontal, direct normal and diffuse horizontal irradiance.

Benton, Brandon↗
DOE OSTI2025

Spurious solar-wind effects on acceleration noise in LISA Pathfinder

Spurious solar-wind effects are a potential noise source in future Laser Interferometer Space Antenna (LISA) measurements. One noise coupling mechanism is constrained by estimating solar-wind effects on acceleration noise in LISA Pathfinder (LPF). While LISA is designed for drag-free differential measurement, predicting the realistic impact both bounds the operational environment and assesses whether LISA could provide serendipitous space-weather observations. Data from NASA's Advanced Composition Explorer (ACE), situated at the L1 Lagrange point, serves as a reliable source of solar-wind data. The data sets are compared over the 114 d time period from 1 March 2016 to 23 June 2016. This period gives the longest readily-available open data set, without interference from other commissioning activities. To evaluate space weather effects, the data from both satellites are formatted, gap-filled/interpolated, and fast-Fourier transformed for amplitude spectral density and coherence comparisons. Solar wind effects are not seen in a coherence plot between LPF and ACE; modest coherence in the planned LISA observational frequency band can be attributed to chance. This result indicates that measurable correlation due to solar-wind acceleration noise over 3 month timescales will be a negligible noise source. LISA is unlikely to inform solar wind measurements routinely. Another source of noise from the Sun, solar radiation pressure, is estimated to impart greater acceleration noise, but has yet to be analyzed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗
DOE OSTI2024

Siting Analysis of a Solar-Nuclear-Desalination Integrated Energy System

Nuclear power is typically deployed as a baseload generator. Increased penetration of variable renewables motivates combining nuclear and renewable technologies into Integrated Energy Systems (IES) to improve dispatchability, component synergies and, through cogeneration, address multiple markets. However, combining multiple energy resources heavily depends on the proper selection of each system’s location and design limitations. In this paper, co-siting options for IES that couple nuclear and concentrating solar power (CSP) with thermal desalination are investigated. A comprehensive siting analysis is performed that utilizes global information survey data to determine possible co-siting options for nuclear and solar thermal generation in the United States. Viable co-siting options are distributed across the Southwestern U.S., with the greatest concentration of siting options in the southern Great Plains, although siting with higher solar direct normal irradiance is possible in other states such as Arizona and New Mexico. Brackish water desalination is also attractive across the southwest U.S. due to high water stress, but for brackish water desalination reverse osmosis (an electricity driven process) is most cost- and energy-efficient, which does not require co-siting with the thermal generator. The most attractive state for nuclear and thermal desalination (which is more attractive when using seawater) is Texas, although other areas may become attractive as water stress increases over the coming decades. Co-siting of all CSP and thermal desalination is challenging as attractive CSP sites are not coastal.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Absolute colors and phase coefficients of asteroids

Context: We use the phase curves of small bodies to measure absolute magnitudes and, together with complementary theoretical and laboratory results, to understand the micro- and macroscopic properties of their surfaces. Although we can observe asteroids up to phase angles of about 30 deg, the range of phase angles covered by outer Solar System objects usually does not go further than 7-10 deg for centaurs and 2 deg for trans-Neptunian objects, and a linear relation between magnitude and phase angle may be assumed. Aims: We aim to directly compare data taken for objects in the inner Solar System (inside the orbit of Jupiter) with data for centaurs and trans-Neptunian objects. Methods: We use the SLOAN Moving Objects Catalog (MOC) data to construct phase curves restricted to phase angles of less than or equal to 7.5 deg, which is compatible with the angles observed for the trans-Neptunian and Centaur population. We assume a linear model for the photometric behavior to obtain absolute magnitudes and phase coefficients in the ugirz, V, and R filters. Results: We obtain absolute magnitudes in seven filters for over 4000 objects. Our comparison with outer Solar System objects suggests the surfaces have a common property: intrinsically redder objects become blue with increasing phase angle, while the opposite happens for intrinsically bluer objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2022

IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2020

Understanding and improving model representation of aerosol optical properties for a Chinese haze event measured during KORUS-AQ

KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May–June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM 2.5 , often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8–16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m 2 g –1 observed average) and light-scattering enhancement factor (ƒ(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm –3 in the model vs. observed average of 1.5 g cm –3 ) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %–28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation.

54 ENVIRONMENTAL SCIENCES↗