Search NASA⌕ Search

Engineering topics

Sengupta, Manajit

Publications and source records attributed to Sengupta, Manajit.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An Evaluation of the Spectral Irradiance Data from the NSRDB

A recent effort by the National Renewable Energy Laboratory (NREL) has led to a new radiative transfer model, the Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT), to efficiently compute spectral irradiances in the plane-of-array (POA). This model has been implemented in the National Solar Radiation Data Base (NSRDB) to provide photovoltaic (PV) resource in both narrowband and broadband wavelengths. This study conducts an evaluation of the spectral irradiances from the PV Resource dataset using surface-based observations at NREL’s Solar Radiation Research Laboratory (SRRL) and University of Oregon (UO). The results demonstrate that the PV resource has a generally good agreement with the long-term observations in both clear-sky and cloudy-sky conditions. Further research is needed to reduce the overestimation of visible irradiances in clear sky conditions and underestimation of near-infrared irradiances in cloudy-sky conditions.

14 SOLAR ENERGY↗
DOE OSTI2021

An Improved Algorithm for Estimating Surface Shortwave Radiation: Preliminary Evaluation With MODIS Products

Cloud parameters, as key inputs in radiative transfer algorithms, have a critical impact on surface shortwave radiation (SSR) computation. By introducing a parameterization of cloud transmittance and reflectance, based on radiative transfer simulations, this study improves the accuracy of an existing physically based model which severely underestimates SSR under thick cloud conditions. The cloud parameterization adopts the single-layer cloud model and simulates cloud transmittances and reflectances by varying cloud optical thickness, cloud particle size, and solar zenith angle. The revised model is applied to estimate instantaneous SSR using Moderate-resolution Imaging Spectroradiometer (MODIS) atmospheric and land products. The retrieved SSR is evaluated against observation data from 41 Baseline Surface Radiation Network (BSRN) stations and is also compared with the MODIS official SSR product. The root mean square error (RMSE) of the estimated instantaneous radiation is approximately 52 and 98 W m -2 under clear-sky and all-sky conditions, respectively. The accuracy of the improved parameterization is higher than that of the original model, and there is no obvious underestimation of SSR in the case of high cloud optical thickness. Therefore, the new algorithm improves the accuracy of SSR estimates in the presence of thick clouds. Retrievals with the improved model also achieve higher accuracy than the MODIS official SSR product (MCD18A1). To conclude, the reliable performance of the scheme at most BSRN stations illustrates that the improved model can be used to map SSR on a global scale.

42 ENGINEERING↗
DOE OSTI2021

Surface albedo spatial variability in North America: Gridded data vs. local measurements

Considering the current booming interest for the large-scale deployment of bifacial photovoltaic modules, the solar industry now requires accurate estimates of broadband surface albedo at high spatial resolution. In this context, the present study evaluates the adequacy and performance over North America of two Moderate Resolution Imaging Spectroradiometer (MODIS) white-sky albedo products (at 500-m and 1-km resolution) and the National Solar Radiation Database (NSRDB) product at 4-km resolution. Two variations of the 500-m MODIS product are also considered: black-sky albedo and all-sky albedo. Albedo observations from 36 radiometric stations during 2011–2015 are analyzed while considering the apparent homogeneity of the surface characteristics over the 4x4 km NSRDB pixel in which they are located. Even at sites where the albedo around the station has been found “homogeneous” in the literature, marked differences are found between the daily observations and the gridded estimates at any spatial resolution. Furthermore, differences in seasonal behavior and between the three different albedo types also impact the accuracy of the albedo estimates, with overtones caused by local specificities and inhomogeneities. All this precludes the desirable evaluation of the local albedo at a specific site of relatively small size compared to its corresponding 4x4-km pixel if only the mean albedo over that pixel is known. Significant discrepancies are also found at snow-impacted sites, most importantly in the case of the NSRDB albedo estimates, which are typically much too high.

14 SOLAR ENERGY↗
DOE OSTI2021

An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis

Uncertainty in predicting solar energy resources introduces major challenges in power system management and necessitates the development of reliable probabilistic solar forecasts. As the first part of the development of probabilistic forecasts based on the Weather Research and Forecasting model with solar extensions (WRF-Solar), this study presents a tangent linear approach to identify input variables responsible for the largest uncertainties in predicting surface solar irradiance and clouds. A tangent linear analysis is capable of efficiently investigating sensitivities of output variables with respect to various input variables of WRF-Solar because this approach avoids the computational burden of perturbing the initial conditions of individual input variables. We develop tangent linear models (TLMs) for six WRF-Solar physics packages that control the formation and dissipation of clouds and solar radiation, and we evaluate the validity of TLMs using a linearity test. The tangent linear sensitivity analysis is conducted under various scenarios based on satellite observations and model simulations to consider realistic input conditions. A simple method is used to quantify the impact of the uncertainty of input variables on the output variables from the TLMs. The results demonstrate that uncertainties in the output variables that are the focus of this study—including global horizontal irradiance, direct normal irradiance, cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes—are highly sensitive to uncertainties in 14 input variables. This study indicates that the tangent linear method can identify key variables of physics modules in WRF-Solar that can be stochastically perturbed to generate ensemble-based probabilistic forecasts.

14 SOLAR ENERGY↗
DOE OSTI2021

Quantifying the Solar Energy Resource for Puerto Rico

After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Data Base (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties which were then used to compute solar radiation. The high-resolution datasets were validated against ground measurements and the error metrics were found to be similar to the original lower resolution dataset. Using 20 years of downscaled data from the NSRDB multi-year capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multi-year data provides the ability to understand variability in capacity factors due to variability in weather over a long period of time. For Puerto Rico the coastal regions were found to have significant higher capacity factors than inland. Using land-use and terrain information a technical potential analysis was conducted for Puerto Rico. This analysis restricted single PV plant development to a maximum of 100 MW nameplate capacity. The nameplate capacity for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most of the interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a PV plant observed in a municipality was 10 MW. The maximum capacity within a county was 2,000 MW. Further a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to be far beyond the maximum peak load for the island. However, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first and ignores the complex decision paths for new power plant development. Therefore, this analysis can only be treated as illustrative. Finally this study investigates the impact of inter-annual variability of resource using a variety of metrices including probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by over 10% year to year. This clearly indicates the risks involved in using any particular year of data and clearly points to the use of multi-year data to reduce some of the risks related to variability in weather.

14 SOLAR ENERGY↗
DOE OSTI2021

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Third Edition

As the world looks for low-carbon sources of energy, solar power stands out as the single most abundant energy resource on Earth. Harnessing this energy is the challenge for this century. Photovoltaics, solar heating and cooling, and concentrating solar power (CSP) are primary forms of energy applications using sunlight. These solar energy systems use different technologies, collect different fractions of the solar resource, and have different siting requirements and production capabilities. Reliable information about the solar resource is required for every solar energy application. This holds true for small installations on a rooftop as well as for large solar power plants; however, solar resource information is of particular interest for large installations because they require substantial investment, sometimes exceeding 1 billion dollars in construction costs. Before such a project is undertaken, the best possible information about the quality and reliability of the fuel source must be made available. That is, project developers need reliable data about the solar resource available at specific locations, including historic trends with seasonal, daily, hourly, and (preferably) subhourly variability to predict the daily and annual performance of a proposed power plant. Without these data, an accurate financial analysis is not possible. Additionally, with the deployment of large amounts of distributed photovoltaics, there is an urgent need to integrate this source of generation to ensure the reliability and stability of the grid. Forecasting generation from the various sources will allow for larger penetrations of these generation sources because utilities and system operators can then ensure stable grid operations. Developed by the foremost experts in the field who have come together under the umbrella of the International Energy Agency’s Solar Heating and Cooling Task 46, this handbook summarizes state-of-the-art information about all these topics.

14 SOLAR ENERGY↗
DOE OSTI2021

Fast All-sky Radiation Model for Solar applications (FARMS) [SWR-16-18]

The Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute cloudy irradiance. Radiative transfer (RT) models simulating broadband solar radiation have been widely used by atmospheric scientists to model solar resources for various energy applications such as operational forecasting. Due to the complexity of solving the RT equation, the computation under cloudy conditions can be extremely time consuming though many approximations (e.g. two-stream approach and delta-M truncation scheme) have been utilized. Thus, a more efficient RT model is crucial for model developers as a new option for approximating solar radiation at the land surface with minimal loss of accuracy. We have developed a fast all-sky radiation model for solar applications (FARMS) using the simplified clear-sky RT model, REST2, and simulated cloud transmittances and reflectances from the Rapid Radiation Transfer Model (RRTM) with a sixteen-stream Discrete Ordinates Radiative Transfer (DISORT). Simulated lookup tables (LUTs) of cloud transmittances and reflectances were created by varying cloud optical thicknesses, cloud particle sizes, and solar zenith angles. Equations with optimized parameters were fitted to the cloud transmittances and reflectances to develop the model. Using this model the all-sky solar irradiance at the land surface can be computed rapidly by combining REST2 with the cloud transmittances and reflectances. This new RT model is more than 1000 times faster than those currently utilized in solar resource assessment and forecasting since it does not explicitly solve the RT equation for each individual cloud condition. Our results indicate the accuracy of the fast radiative transfer model is comparable to or better than two-stream approximation in term of computing cloud transmittance and solar radiation.

Xie, Yu↗
DOE OSTI2021

Estimating Surface Solar Irradiance Using Meteosat-8 Satellite for India and Surrounding Regions (2017-2019)

Using the Physical Solar Model, Version 3 (PSM V3), the National Renewable Energy Laboratory—in collaboration with the University of Wisconsin, the National Oceanic and Atmospheric Administration, and Solar Consulting Services—produced modeled surface solar irradiance based on Meteosat-8 satellite cloud information. PSM V3 implements a physics-based radiative transfer approach to model surface solar irradiance using cloud properties processed from raw satellite data and ancillary data such as aerosol optical depth and precipitable water vapor from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Further, the modeled surface solar irradiance was validated using 10 high-quality ground measurement data from the Baseline Surface Radiation Network. The modeled data showed good correlation with the ground measurement, and the overall uncertainty is less than 35% for all validation locations. Because of the satellite viewing geometry, the Indian and European locations demonstrated higher uncertainties than the African locations, where the Meteosat-8 satellite has a more nadir viewing geometry. Further, instruction on how to access the data is provided.

14 SOLAR ENERGY↗
DOE OSTI2021

Improved field evaluation of reference cells using spectral measurements

Output from an IMT reference solar cell mounted on a one-axis tracking surface is investigated using spectral measurements covering the 350–1650 nm range for selected days throughout the year. Comparisons are made to a Class-A pyranometer also mounted on the one-axis tracking surface. Systematic biases over the day and year are observed in the ratio of the reference cell’s measurements to reference pyranometer measurements. This systematic bias is associated with the spectral, temperature, and angle-of-incident effects that differ between the reference cell and the reference pyranometer. The comparison is done for selected clear and totally cloudy days to determine the magnitude of the effects and to characterize the influence of these effects on the reference cell measurements. A model to calculate the reference cell output based on spectral irradiance and reference cell temperature is introduced.

14 SOLAR ENERGY↗
DOE OSTI2021

Use of Physics to Improve Solar Forecast: Physics-Informed Persistence Models for Simultaneously Forecasting GHI, DNI, and DHI

Observation-based statistical models have been widely used in forecasting solar energy; however, existing models often lack a clear relation to physics and are limited largely to global horizontal irradiance (GHI) forecasts over relatively short time horizons (< 1 hour). Incorporating physics into observation-based models, increasing forecast time horizons and developing a model system for forecasting not only GHI but also direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) remain challenging, especially under cloudy conditions because of complex cloud-radiation interactions. This work attempts to address these challenges by developing a hierarchy of four new physics-informed persistence models that can be used to simultaneously forecast GHI, DNI and DHI. The decade-long measurements (1998 to 2014) at the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM)'s Southern Great Plains (SGP) Central Facility site are used to evaluate the performance of the new models. Overall, the results show that the new physics-informed forecast models generally outperform the simple and smart persistence models, and improve the forecast accuracy at lead times from 1.25 hours up to 6 hours. Further analysis reveals that the forecast error is highly related to the error and temporal variability of the assumed cloud predictor. The best model for forecasting different radiative components can be explained by the relationship between solar irradiances and cloud properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

AATTENUATION—The Atmospheric Attenuation Model for CSP Tower Plants: A Look-Up Table for Operational Implementation

Attenuation of solar radiation between the receiver and the heliostat field in concentrated solar power (CSP) tower plants can reduce the overall system performance significantly. The attenuation varies strongly with time and the average attenuation at different sites might also vary strongly from each other. If no site specific attenuation data is available, the optimal plant design cannot be determined and rough estimations of the attenuation effect are required leading to high uncertainties of yield analysis calculations. The attenuation is caused mainly by water vapor content and aerosol particles in the lower atmospheric layer above ground. Although several on-site measurement systems have been developed during recent years, attenuation data sets are usually not available to be included during the plant project development. An Atmospheric Attenuation (AATTENUATION) model to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity, and barometric pressure measurements was developed and validated by the authors earlier. The model allows the accurate estimation of attenuation for sites with low attenuation and gives an estimation of the attenuation for less clear sites. However, the site-dependent coefficients of the AATTENUATION model had to be developed individually for each site of interest, which required time-consuming radiative transfer simulations, considering the exact location and altitude, as well as the pre-dominant aerosol type at the location. This strongly limited the application of the model despite its typically available input data. In this manuscript, a look-up table (LUT) is presented which enables the application of the AATTENUATION model at the site of interest without the necessity to perform the according complex radiative transfer calculations for each site individually. This enables the application of the AATTENUATION model for virtually all resource assessments for tower plants and in an operational mode in real time within plant monitoring systems around the world. The LUT also facilitates the generation of solar attenuation maps on the basis of long-term meteorological data sets which can be considered during resource assessment for CSP tower plant projects. The LUTs are provided together with this manuscript as supplementary files. The LUT for the AATTENUATION model was developed for a solar zenith angle (SZA) grid of 1°, an altitude grid of 100 m, 7 different standard aerosol types and the standard AFGL atmospheres for mid-latitudes and the tropics. The LUT was tested against the original version of the AATTENUATION model at 4 sites in Morocco and Spain, and it was found that the additional uncertainty introduced by the application of the LUT is negligible. With the information of latitude, longitude, altitude above mean sea level, DNI, relative humidity (RH), ambient temperature (Tair), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest.

14 SOLAR ENERGY↗
DOE OSTI2020

Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)

The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). Specifically, the coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Köppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS. For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period.

14 SOLAR ENERGY↗
DOE OSTI2020

Australian Solar Energy Forecasting System (ASEFS) (CRADA CRD-14-541 Final Report)

This project forms a part of the broader project with the same name which is funded by ASI under the US-Australia Solar Energy Collaboration (Broader Project). The Broader Project involves a number of other participants, being the University of New South Wales, the University of South Australia, the Australian Energy Market Operator Limited and the Commonwealth of Australia acting through the Bureau of Meteorology (Other Participants). NREL and CSIRO will each work with the Other Participants in the performance of this CRADA. However, for the avoidance of doubt, this CRADA does not cover the whole of the Broader Project and only relates to the work described in this Joint Work Statement, which is to be carried out by NREL. In the course of this project, NREL may be provided with materials belonging to CSIRO or the Other Participants, which it may use solely for the purpose of performing the project described in this Joint Work Statement. Similarly, CSIRO may provide NREL project materials to Other Participants pursuant to Article IX or XV of the CRADA, for the purpose of performing research forming part of the Broader Project in collaboration with CSIRO.

14 SOLAR ENERGY↗