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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Diagnosing Model Errors in Simulation of Solar Radiation on Inclined Surfaces

Transposition models have been widely used in the solar energy industry to simulate solar radiation on inclined PV panels. Following numerous studies comparing the performance of transposition models, this paper aims to understand the quantitative uncertainty in the state-of-the-art transposition models and the sources leading to the uncertainty. Our results show significant differences between two highly used isotropic transposition models with one substantially underestimating the diffuse plane-of-array (POA) irradiances when diffuse radiation is perfectly isotropic. In the empirical transposition models, the selection of empirical coefficients and land surface albedo can both result in uncertainty in the output. This study can be used as a guide for future development of physics-based transposition models.

14 SOLAR ENERGY↗
DOE OSTI2020

An improved method for direct incident solar radiation calculation from hourly solar insolation data in building energy simulation

Solar radiation considerably influences the energy consumption of buildings and the power production of building integrated photovoltaic (BIPV) systems. Hourly solar insolation (Wh/m 2 ), represented as the amount of solar irradiance collected on the ground during a 1-h period, is the most common solar radiation data available and widely used in weather files applied in building energy modeling programs (BEMPs). Because the solar beam and position vary over time, the use of hourly insolation data as the input might result in errors in the estimation of the direct incident solar radiation on a particular surface. In this study, methods used in BEMPs for direct incident solar radiation calculations are first analyzed, and an improved method adopting a new algorithm for estimating the solar irradiance is proposed. Furthermore, the algorithm assumes that the solar irradiance changes linearly within a 1-h period and can be estimated based on the solar irradiance at the half clock and slope. The collected direct normal solar irradiance data of 2016 from eight solar radiation stations in China were used to demonstrate the proposed method and evaluate its performance by comparing the results with those from three conventional methods used in BEMPs along with the ground truth measurements. In addition, in this study, factors affecting the accuracy of the calculation results are explored. The results of the estimated direct incident solar radiation show that the proposed method achieves the best accuracy, followed by the methods used in DOE-2, EnergyPlus, and DeST. The proposed method guarantees that the hourly direct solar insolation will remain the same and reflects the variation in the direct solar irradiance across a 1-h time frame. The proposed method can be adopted in BEMPs to improve the accuracy of the solar radiation calculation, thereby improving the accuracy of the simulated building performance and the BIPV production.

14 SOLAR ENERGY↗
DOE OSTI2025

Potential Effects of Climate Change and Solar Radiation Modification on Renewable Energy Resources

Solar radiation modification (SRM) is a possible deliberate approach to decrease or reflect incoming solar radiation with the goal of reducing global temperatures, which have increased over the last decades due to high atmospheric greenhouse gas concentrations. Stratospheric aerosol injection, specifically, has shown potential for successfully reducing global temperatures in climate model simulations. Despite the growing literature in the areas of climate change and SRM, their combined effects on renewable energy generation, a climate change mitigation strategy, have not been addressed. In this review paper, we synthesize previous literature on the possible effects of climate change and SRM on renewable energy resources (i.e., wind energy, solar energy, biomass energy, and hydropower), review the status of climate change and SRM research, and explore potential effects of SRM on renewable energy primarily in the Continental United States (CONUS), but with global perspectives as well. We discuss the research challenges and impacts of SRM on renewable energy and conclude by discussing the potential implications of SRM for renewables for SRM governance and policy. This work is not advocating for or against SRM. It is highlighting an important potential impact for future decision makers.

Kumler, Andrew [National Renewable Energy Laborato↗
DOE OSTI2021

Progress on the National Solar Radiation Data Base (NSRDB): A New DNI Computation

This study introduces a new technique to compute direct normal irradiance (DNI) for improving the National Solar Radiation Data Base (NSRDB). A finite-surface integration algorithm is developed to compute solar radiation in differential solid angles and efficiently infer its contribution to a surface perpendicular to the solar direction. A lookup table of cloud bi-directional transmittance distribution function (BTDF) is developed by use of the discrete ordinates radiative transfer (DISORT) model for possible solar and observing directions and various cloud optical and microphysical properties. In each solar incident direction, DNI is given by the cloud BTDFs from approximately 200 differential solid angles. The simulated DNI is calibrated and evaluated using surface observations at the National Renewable Energy Laboratory's (NREL's) Solar Energy Research Laboratory (SRRL) and the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Probabilistic Forecast of All-Sky Solar Radiation Using Enhanced WRF-Solar: Preprint

This study presents enhancements of the Weather Research and Forecasting model with solar extensions (WRF-Solar) to provide probabilistic forecasts of solar radiation. Our approach builds ensemble WRF-Solar runs by introducing stochastic perturbations of variables that produce the largest uncertainties in predicting surface irradiance and clouds. The key variables are identified using tangent linear sensitivity analysis of six physics packages responsible for all-sky irradiance variability. An optimal strategy to stochastically perturb the selected variables is developed and applied to WRF-Solar to generate ensemble members for day-ahead solar prediction. The National Solar Radiation Database (NSRDB) is used to validate the ensemble forecast at arbitrary locations on the model grid. Preliminary results indicate that the proposed technique can potentially produce WRF-Solar ensembles providing reliable information of solar prediction uncertainty. This study describes the implemented methodology and initial results as well as future research to improve ensemble-based probabilistic forecasts with WRF-Solar.

all-sky↗
DOE OSTI2020

Integration of Total-Sky Imager Data with a Physics-Based Smart Persistence Model for Intra-Hour Forecasting of Solar Radiation

Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Our previous study introduced a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposed the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle were accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique was used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction were predicted for future time steps using a two-stream approximation and a 5-minute exponential weighted moving average, respectively. The model evaluation indicated the estimation and forecast of cloud fraction mostly contributed to the uncertainty of the PSPI though it overcame the persistence and smart persistence models in all forecast time horizons between 5 and 60 minutes. This study aims to enhance the PSPI by ingesting surface-based observations of cloud fraction from a total sky imager (TSI). The estimation and forecast of cloud albedo is correspondingly improved by utilizing the cloud fraction observations and thus leads to more accurate GHI forecast. Various time-series analysis methods are also investigated on the forecasting of cloud fraction and cloud albedo for further improving the GHI forecast. These improvements are valuable for many applications, such as forecasting energy use for buildings, grid operations, and ultimately bringing down the cost of solar energy.

14 SOLAR ENERGY↗
DOE OSTI2019

Novel stochastic methods to predict short-term solar radiation and photovoltaic power

Solar forecasting has evolved towards becoming a key component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents two novel stochastic forecasting models for solar PV by utilizing historical measurement data to outline a short-term high-resolution probabilistic behavior of solar. First, an uncertain basis functions method is used to forecast both solar radiation and PV power. Three possible distributions are considered for the uncertain basis functions - Gaussian, Laplace, and Uniform distributions. Second, stochastic state-space models are applied to characterize the behaviors of solar radiation and PV power output. A filter-based expectation-maximization and Kalman filtering mechanism is employed to recursively estimate the system parameters and state variables. This enables the system to accurately forecast small as well as large fluctuations of the solar signals. The introduced forecasting models are suitable for real-time tertiary dispatch controllers and optimal power controllers. The PV forecasting models are tested using solar radiation and PV power measurement data collected from a 13.5 kW PV panel installed on the rooftop of our laboratory. Furthermore, the results are compared with standard time series forecasting mechanisms and show a substantial improvement in the forecasting accuracy of the total energy produced.

14 SOLAR ENERGY↗
DOE OSTI2019

Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble

Solar radiation data are crucial for the design and evaluation of solar energy systems, climatic studies, water resources management, estimating crop productivity, etc. As so, for locations where direct measurements are not available, reliable models may be developed to estimate solar radiation from more readily available data. In this study, two artificial intelligence (AI)-based models including artificial neural network and adaptive neuro-fuzzy inference systems, three temperature-based empirical models including Meza–Varas, Hargreaves–Samani, and Chen, and a conventional multi-linear regression (MLR) model were employed for multi-region daily global solar radiation estimation for Iraq. To ensure appropriate selection of input variables, sensitivity analysis was conducted to determine the dominant parameters. Finally, two ensemble approaches, neural average ensemble and simple average ensemble, were applied to improve the performance of the single models. For this purpose, daily meteorological data of maximum temperature (T{sub max}), minimum temperature (T{sub min}), mean temperature (T{sub mean}), relative humidity (R{sub H}), and wind speed (U{sub 2}) were obtained from January 2006 to December 2016 from four major cities in Iraq representing, north, west, south, and east regions. The results revealed that temperatures (T{sub max}, T{sub mean}, T{sub min}) and relative humidity are the dominant parameters. While temperature-based empirical models and MLR model could be employed to achieve reliable results, AI-based models are superior in performance to other models. Also promising improvement in daily global solar radiation modeling could be achieved by model ensemble. The results of this study affirmed that the provided ensemble approaches can increase the performance of single models up to 19.19%, 7.59%, and 16.81% in training, validation, and testing steps, respectively.

58 GEOSCIENCES↗
DOE OSTI2018

Effect of Orientation of Ice Crystals in Cirrus on Direct and Scattered Solar Radiation Fluxes

The problem of solar radiation transfer by cirrus containing horizontally oriented ice crystals urgent today is considered. The results of a complex radiation experiment are described in which solar radiation fluxes at the Earth’s surface are measured and high-level clouds are vertically sensed by a polarization lidar. The lidar allows the full backscattering phase matrix (BSPM) to be determined. The degree of orientation of ice crystals in cirrus is estimated from the form of the BSPM elements and their interrelations. It is shown experimentally for the first time that the degree of orientation of crystalline particles in cirrus has a significant effect on the direct and scattered solar radiation fluxes arriving at the Earth’s surface.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Study of the processes of degradation of the optical properties of mesoporous and macroporous silicon upon exposure to simulated solar radiation

The effect of solar radiation on the surface composition of mesoporous and macroporous silicon is studied by infrared spectroscopy, Raman spectroscopy, and photoluminescence measurements in order to analyze the possibility of using these materials as a material for solar-power engineering. The studies are conducted in the laboratory environment, with the use of a solar-radiation simulator operating under conditions close to the working conditions of standard silicon solar cells. The studies show that, in general, the materials meet the requirements of solar-power engineering, if it is possible to preclude harmful effects associated with the presence of heat-sensitive and photosensitive bonds at the nanomaterial surface by standard processing methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. We report on average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.

14 SOLAR ENERGY↗
DOE OSTI2018

National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations for 1998-2023. The NSRDB is updated annually and provides foundational information to support U.S. Department of Energy programs, research, industry and the general public. The NSRDB provides time-series data at 30-minute resolution of resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. Additionally time series data at 5 minutes for the US and 10 minutes for North, Central and South America at 2 km resolution are produced from the next generation of GOES satellites and made available from 2019. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms developed by the University of Wisconsin. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate solar irradiance (GHI, DNI, and DHI). The Global Horizontal Irradiance (GHI) is computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, FARMS is used to compute GHI and FARMS DNI is used to compute the Direct Normal Irradiance (DNI). The PATMOS-X model uses radiance images in visible and infrared channels from the Geostationary Operational Environmental Satellite (GOES) series of geostationary weather satellites. Ancillary variables needed to run REST2 and FARMS (e.g., aerosol optical depth, precipitable water vapor, and albedo) are derived from NASA's Modern Era-Retrospective Analysis (MERRA-2) dataset. Temperature and wind speed data are also derived from MERRA-2 and provided for use in NREL's System Advisor Model (SAM) to compute PV generation.

aerosol↗
DOE OSTI2021

A Comparison of Time Series Gap-Filling Methods to Impute Solar Radiation Data

Complete solar resource datasets play a critical role at every stage of solar project phases. However, measured or modeled solar resource data come with significant uncertainties and usually suffer from several issues, including but not limited to, data gaps, data quality issue, etc. In order to mitigate these issues an appropriate data imputation method should be implemented to build a complete and reliable temporal (and spatial) database. Being motivated by this, in this study we compare the performances of eight different gap filling methods extensively by creating random and artificial data gaps in (i) hourly irradiance data for one year using a few locations of the National Solar Radiation Database (NSRDB) and (ii) one-minute ground measurement dataset from Surface Radiation Budget Network (SURFRAD) stations.

clearness index↗
DOE OSTI2020

India Solar Resource Data: National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) provides high-resolution solar radiation data for long periods of time, which allows user to have a clear understanding of the spatial and temporal correlation of the data to determine the true impact of solar variability on the grid. This fact sheet specifically outlines solar resource data for India included in NSRDB.

energy systems↗
DOE OSTI2018

A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique

Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Here, we introduce a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique is used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction are predicted for future time steps using a two-stream approximation and a 5-min exponential weighted moving average, respectively. Our model evaluation using the long-term observations of GHI at NREL's Solar Radiation Research Laboratory (SRRL) shows that the PSPI has a better performance than the persistence and smart persistence models in all forecast time horizons between 5 and 60 min, which is more significant in cloudy-sky conditions. Finally, compared to the persistence and smart persistence models, the PSPI does not require additional observations of various atmospheric parameters but is customizable in that additional observations, if available, can be ingested to further improve the GHI forecast. An advanced technology of cloud forecast is also expected to improve the future performance of the PSPI.

14 SOLAR ENERGY↗