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Solar irradiance spectral distribution data normalized and combined with spectral absorptance of satellite surface coatings, obtaining absorbed energy for spectral range
Moderate resolution spectral irradiance measurements in the range 300 nm to 1100 nm have been made of eight radiant sources which are currently being used as solar simulators. Spectral irradiance data are presented in graphical form. To demonstrate the interplay of source spectral distribution and cell spectral response, measurements of short circuit current of five cells of differing response characteristics have been made with these sources. These results are presented in tabular and graphical form.
The analyses of Willson et al. (1981) and Hudson et al. (1981) are extended in correlating the total solar irradiance monitor (ACRIM) data with the routine synoptic sunspot data. At the simplest level, this correlation reveals clearly that the so-called 'missing flux' is truly missing in the sense that large young active regions do produce at net diminution of the irradiance when their spots cross the central meridian. It is pointed out that the irradiance deficit must of course be made up, either promptly or on intermediate time scales; this is because the surface effects cannot perturb the energy generation processes in the interior. In the approach taken here, simple models of the reemission are constructed, the total reemission is scaled to the estimated sunspot deficit, and an attempt is made to measure the parameters of the models by a statistical comparison with ACRIM data.
The Active Cavity Radiometer Irradiance Monitor (ACRIM) total-irradiance data from the Solar Maximum Mission have given a first comprehensive view of solar variability in the stellar sense. Five types of solar variability have been identified thus far. These have small amplitudes, less than a few tenths of one percent, and are at levels generally not yet detectable on other stars. The possible stellar analogs are interesting physically, and in particular may help us to understand solar behavior on longer time scales. The ACRIM data is described from the stellar point of view. The present state of stellar time-series photometry is discussed.
Traditional monofacial photovoltaic (mPV) systems are commonly adopted and well-documented because of their lower upfront costs in comparison to bifacial photovoltaic (bPV) systems. This study investigates how PV technologies impact energy storage in grid-scale hybrid renewable systems, focusing on optimizing and assessing the performance of mPV and bPV technologies integrated with pumped storage hydropower. Using Ludington City, Michigan as a case study and analyzing real-world data such as solar irradiance, ambient temperature, and utility-scale load profiles, the research highlights the operational and economic benefits of bPV systems. The results reveal that bPV systems can pump approximately 10.38% more water annually to the upper reservoir while achieving a lower levelized cost of energy ($0.0578/kWh for bPV vs. $0.0672/kWh for mPV). This study underscores the outstanding potential of bPV systems in enhancing energy storage and management strategies, contributing to a more sustainable and resilient renewable energy future.
At the NASA Goddard Earth Sciences Data and Information Services (GES DISC), we strive to simplify data discovery and data access to our wide range of global climate data, concentrated primarily in the areas of atmospheric composition, atmospheric dynamics, global precipitation, solar irradiance, and several modeling data sets related to land surface hydrology. To help meet user needs, we will demonstrate how you can use the GES DISC knowledge-base resources (HowTo's) and we also encourage community contributions.
A computer automated data acquisition system for atmospheric emittance, and global solar, downwelled diffuse solar, and direct solar irradiances is discussed. Hourly-integrated global solar and atmospheric emitted radiances were measured continuously from February 1981 and hourly-integrated diffuse solar and direct solar irradiances were measured continuously from October 1981. One-minute integrated data are available for each of these components from February 1982. The results of the correlation of global insolation with fractional cloud cover for the first year's data set. A February data set, composed of one-minute integrated global insolation and direct solar irradiance, cloud cover fractions, meteorological data from nearby weather stations, and GOES East satellite radiometric data, was collected to test the theoretical model of satellite radiometric data correlation and develop the cloud dependence for the local measurement site.
Detailed knowledge of the irradiance of the sun at ground locations is essential for the design and evaluation of solar energy conversion systems. The primary source of such data is the global network of weather stations. Such stations are often too far apart and for most locations the data available are only daily total irradiance or monthly averages. Solar energy conversion programs require insolation data with considerably higher geographical and temporal resolution. Meteorological satellites gather routinely extensive data on the energy reflected and scattered into space by the earth-atmosphere system. A program has been initiated to use such data for deriving ground insolation for energy conversion. Some of the preliminary results of this program will be discussed.
The difficulty in correlating models of solar irradiance variations from ground-based observations with spacecraft data is examined. Methods for modeling total solar irradiance are reviewed. A photometric program is described which produces sunspot irradiance fluctuation information that is repeatable to within 10 millionths of the mean irradiance. The results from this program are compared with model irradiance fluctuations based on published sunspot and plage areas.
The photometric sunspot index (PSI) was developed to study the effects of sunspots on solar irradiance. It is calculated from the sunspot data published in the Solar-Geophysical Data catalog. It has been shown that the former PSI models overestimate the effect of dark sunspots on solar irradiance; furthermore results of direct sunspot photometry indicate that the contrast of spots depends on their area. An improved PSI calculation is presented; it takes into account the area dependence of the contrast and calculates `true' daily means for each observation using the differential rotation of the spots. Moreover, the observations are screened for outliers which improves the homogeneity of the data set substantially, at least for the period after December 1981 when NOAA started to report data from a few instead of one to two stations. A detailed description of the method is provided. The correlation between the newly calculated PSI and total solar irradiance is studied for different phases of the solar cycles 21 and 22 using bi-variate spectral analysis. The results can be used as a `calibration' of PSI in terms of gain, the factor by which PSI has to be multiplied to yield the observed irradiance change. The factor changes with time from about 0.6 in 1980 to 1.1 in 1990. This unexpected result cannot be interpreted by a change of the contrast relative to the quiet Sun (as it is normally defined and determined by direct photometry) but rather as a change of the contrast between the spots and their surrounding as seen in total irradiance (integrated over the solar disk). This may partly be explained by a change in the ratio between the areas of the spots and the surrounding faculae.
GET-Solar (Generation forecasting and parameter Estimation Tool for Solar) can be used to estimate behind the meter (BTM) solar generation from limited datasets. Using distributed sets of diverse solar sites, but with close spatial proximity, and/or limited historical PV generation information can be used to generate of solar profiles. This is useful for applications where partial solar generation data is available, and on-site irradiance data is not available. GET-Solar calculates PV technical data parameters and calculates output by modeling shading and cloud cover impacts. Clear sky irradiance profiles are used to calculate base generation profiles on which the local shading and cloud cover layers are added. In order to arrive at clear sky generation data, PV mounting information like panel tilt and and angle is needed. For cases where PV mounting information is missing, panel tilt and azimuth angle can be estimated using a non-linear optimization algorithm. GET-Solar can also be used for forecasting applications where historical PV data is available to estimate local shading at various solar positions. This overall framework can be used for estimation of solar parameters, and assisting in analyzing and modeling diverse sets of DER where solar generation is also present in utility meter data. GET-Solar has been developed in Python and uses the Pyomo optimization language.
As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.
The Shuttle SBUV (SSBUV) and NOAA-11 SBUV/2 instruments measured solar spectral UV irradiance during the maximum and declining phase of solar cycle 22. The SSB UV data accurately represent the absolute solar UV irradiance between 200-405 nm, and also show the long-term variations during eight flights between October 1989 and January 1996. These data have been used to correct long-term sensitivity changes in the NOAA-11 SBUV/2 data, which provide a near-daily record of solar UV variations over the 170-400 nm region between December 1988 and October 1994. The NOAA-11 data demonstrate the evolution of short-term solar UV activity during solar cycle 22.
The Shuttle Solar Backscatter Ultraviolet (SSBUV) and NOAA-II Solar Backscatter Ultraviolet/2 (SBUV/2) instruments measured solar spectral Ultraviolet (UV) irradiance during the maximum and declining phase of solar cycle 22. The SSB UV data accurately represent the absolute solar UV irradiance between 200-405 nanometers, and also show the long-term variations during eight flights between October 1989 and January, 1996. These data have been used to correct long-term sensitivity changes in the NOAA-II SBUV/2 data, which provide a near-daily record of solar UV variations over the 170 to 400 nanometer region between December 1988 and October 1994. The NOAA-II data demonstrate the evolution of short-term solar UV activity during solar cycle 22.
NOAA's planned Total and Spectral Solar Irradiance Sensor (TSIS) mission will fly along with the NOAA user service payloads Advanced Data Collection System (ADCS) and Search and Rescue Satellite Aided Tracking (SARSAT). In ' order to guarantee continuity in the 33-year solar irradiance climate data record, TSIS must be launched in time to overlap with current on-orbit solar irradiance instruments. Currently TSIS is moving towards a launch rcadin~ss date of January 2015. TSIS provides for continuation of the Total Irradiance Monitor (TIM) and the Spectral Irradiance Monitor (SIM) ,currently onboard NASA's Solar Radiation and Climate Experiment (SORCE) platform, launched in January 2003. The difficulty of ensuring continuity has increased due to the launch failure of NASA's Glory mission with its improved TIM. Achieving the needed overlap must now rely on extending SORCE. and maintaining the TSIS schedule. TSIS is one component of a NASA-NOAA joint program (JPSS) planned to transition certain climate observations to operational mode. We summarize issues of continuity, improvements being made to the TIM and 81M sensors, and plans to provide for traceability of total and spectral irradiance measurements to ground-based cryogenic standards.
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.
The Solar Dynamics Observatory (SDO) was launched on February 11,2010 into partly cloudy skies over Cape Canaveral, Florida. SDO moved into a 28 degree inclined geosynchronous orbit over the longitude of the ground station in New Mexico. SDO is the first Space Weather Mission in NASA's Living With a Star Program. SDO's main goal is to understand and predict those solar variations that influence life on Earth and our technological systems. The SDO science investigations will determine how the Sun's magnetic field is generated and structured, how this stored magnetic energy is released into the heliosphere as the solar wind, energetic particles, and variations in the solar irradiance. The SDO mission consists of three scientific investigations (AIA, EVE, and HMI), a spacecraft bus, and a dedicated Ka-band ground station to handle the 150 Mbps data flow. SDO continues a long tradition of NASA missions providing calibrated solar spectral irradiance data, in this case using multiple measurements of the irradiance and rocket underflights of the spacecraft. The other instruments on SDO will be used to explain and develop predictive models of the solar spectral irradiance in the extreme ultraviolet. Science teams at LMSAL, LASP, and Stanford are responsible for processing, analyzing, distributing, and archiving the science data. We will talk about the building of SDO, its launch, and the data and science it will provide to NASA.
The Solar Dynamics Observatory (SDO) was launched on February 11, 2010 into the partly cloudy skies above Cape Canaveral, Florida. Over the next month SDO moved into a 28 degree inclined geosynchronous orbit at the longitude of the ground station in New Mexico. SDO is the first Space Weather Mission in NASA's Living With a Star Program. SDO's main goal is to understand and predict those solar variations that influence life on Earth and our technological systems. The SDO science investigations will determine how the Sun's magnetic field is generated and structured, how this stored magnetic energy is released into the heliosphere as the solar wind, energetic particles, and variations in the solar irradiance. The SDO mission consists of three scientific investigations (AIA, EVE, and HMI), a spacecraft bus, and a dedicated Ka-band ground station to handle the 150 Mbps data flow. SDO continues a long tradition of NASA missions providing calibrated solar spectral irradiance data, in this case using multiple measurements of the irradiance and rocket underflights of the spacecraft. The other instruments on SDO will be used to explain and develop predictive models of the solar spectral irradiance in the extreme ultraviolet. Science teams at LMSAL, LASP, and Stanford are responsible for processing, analyzing, distributing, and archiving the science data. We will talk about the launch of SDO and describe the data and science it is providing to NASA.