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This poster provides an overview of the best practices and standards development for the collection and use of solar resource data for solar energy applications.
Nimbus-7 Earth Radiation Budget (ERB) solar measurements extend from November 16, 1978, to December 13, 1993, but with data gaps in 1992 and 1993. The measurements include the total solar irradiance plus six broadband spectral components. The Channel 10c total irradiance data appears very stable, and the calibration, well done. A number of characterization problems remain in the spectral measurements. In the original program, the solar and Earth flux measurements were intermixed and spread over about 170 computer tapes. For easier access, the solar data have been collected into two compact data sets. All of the data are collected into 14 Summary Solar Tapes (SST's). In addition, two Channel 10c Solar Tapes (CST's) give a separate listing of the stable total solar irradiance measurements. Channel 10c calibration and orbital irradiance values are given on separate PC disks. This document gives data descriptions and formats, together with quality control and calibration procedures.
With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data input to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.
With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data inputs to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.
As the world increasingly seeks low-carbon energy solutions, solar power emerges as the most abundant resource on our planet. However, the challenge of effectively harnessing this energy is crucial in the coming years. Solar energy applications such as photovoltaics, solar heating and cooling, and concentrating solar power use different technologies to capitalize on sunlight. Each system has unique capabilities and requirements, underscoring the need for reliable information about solar resources across diverse installations, from residential rooftops to large-scale power plants. This is especially important for substantial projects, often exceeding $1 billion in construction costs. Before embarking on such ventures, it is imperative to obtain accurate data concerning solar resource quality and reliability at specific sites. Developers require detailed historical information, including seasonal, daily, hourly, and, ideally, subhourly variability to effectively predict a power plant's annual performance. Without these vital data, financial analyses fall short. Moreover, with the growing adoption of distributed photovoltaics, integrating these generation sources becomes critical to maintaining grid reliability and stability. By accurately forecasting generation patterns, utilities and system operators can facilitate greater integration of solar energy, thus ensuring the operational stability of the grid. The complexity and importance of these issues have prompted the foremost experts in the field to collaborate under the auspices of the International Energy Agency's (IEA's) Photovoltaic Power Systems Programme (PVPS) Task 16 to publish this handbook, which summarizes state-of-the-art information about all these topics. The efforts focus on providing reliable data and insights that can help shape our investments in solar energy and drive a sustainable future.
Computer programs used to process data that will be used in the evaluation of collector efficiency and solar system performance are described. The program, TAPFIL, reads data from an IBM 360 tape containing information (insolation, flowrates, temperatures, etc.) from 48 operational solar heating and cooling test sites. Two other programs, CHPLOT and WRTCNL, plot and tabulate the data from the direct access, unformatted TAPFIL file. The methodology of the programs, their inputs, and their outputs are described.
Data obtained by insturments on the Ulysses spacecraft during its rapid sweep through >90 degrees of solar latitude, crossing the solar equator in early 1995, were combined with data obtained near Earth by the WIND spacecraft to study the spatial structure of the solar wind and to compare with different models of the interplanetary field derived from solar observations.
Realizing the SETO 2030 mission of reducing solar energy costs to 3-5 c/kWh will require innovative enabling research on effective, cost-efficient integration of local PV within distribution systems. However, the intermittent and variable nature of PVs compels operators to impose conservative hosting capacity constraints. Given the extremely high variability of (intermittent and unpredictable) solar energy generation, relaxing the capacity constraints (which are currently around 15%) and achieving 100% or greater integration of renewables will require a fundamental transformation of the power grid via the utilization of exponentially larger amounts of AMI enabled fine-grained data. To address the challenges in increasing the penetration of renewable energy based DERs, this project envisions an Enhanced System Layer (ESL) at the distribution network level that is reliable, cost-effective and scalable to millions of Distributed Energy Resources (DERs)/devices. This includes developing: 1) Transformative and highly scalable machine learning based predictive analytics tools that plug into distribution system planning and provide real-time situational awareness at the distribution level for short and long-term operational planning. The tools will be built using novel data-driven energy models of millions of active nodes with AMI, 2) Adaptive stochastic analysis and optimization algorithms for real-time grid operations, 3) Dynamic Scenario Analysis using parallel Cloudenabled implementations with < 1 minute computational cycle times.
The SunPy Project is a community of scientists and software developers creating an ecosystem of Python packages for solar physics. The project includes the sunpy core package as well as a set of affiliated packages. The sunpy core package provides general purpose tools to access data from different providers, read image and time series data, and transform between commonly used coordinate systems. Affiliated packages perform more specialized tasks that do not fall within the more general scope of the sunpy core package. In this article, we give a high-level overview of the SunPy Project, how it is broader than the sunpy core package, and how the project curates and fosters the affiliated package system. We demonstrate how components of the SunPy ecosystem, including sunpy and several affiliated packages, work together to enable multi-instrument data analysis workflows. We also describe members of the SunPy Project and how the project interacts with the wider solar physics and scientific Python communities. Finally, we discuss the future direction and priorities of the SunPy Project.
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.
Prediction theory for solar flare importance using matrix analysis computer programming
In most hydrodynamic cases, the existence of a turbulent flow superimposed on a mean flow is caused by a shear instability in the latter. Boussinesq suggested the first model for the turbulent Reynolds stresses bar-(u(sub i)u(sub j)) in which the mean shear S(sub ij) is the cause (or source) of turbulence represented by the stress bar-(u(sub i)u(sub j)). In the case of solar differential rotation, exactly the reverse physical process occurs: turbulence (which must pre-exist) generates a mean flow which manifests itself in the form of differential rotation. Thus, the Boussinesq model is wholly inadequate because in the solar case, cause and effect are reversed. Since the Boussinesq model is inadequate, one needs an alternative model for the Reynolds stresses. We present a new dynamical model for the Reynolds stresses, convective fluxes, turbulent kinetic energy, and temperature fluctuations. The complete model requires the solution of 11 differential equations. We then introduce a set of simplifying assumptions which reduce the full dynamical model to a set of algebraic Reynolds stress models. We explicitly solve one of these models that entails only one differential equation. The overall agreement with the data is obtained with a model that is neither phenomenological nor one that requires a full numerical simulation, since it is algebraic in nature. The new model can play an important role in understanding the complex physics underlying the interplay between solar differential rotation and convection, as many physical processes can naturally be incorporated into the model.
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.
The Frenchman's Reef Data Acquisition and Control System (DACS), Sunlogger model 10000-1 was evaluated to determine the accuracy of control functions and the performance of the system in the environment of the planned installation site. The Sunlogger system was verified to perform normally in an environment of 100 F and approximately 90% humidity. It is recommended however that the Sunlogger be installed in a controlled environment with temperatures and humidity significantly lower than those referenced above.
Solar collector performance data obtained from tests with a simulator was modified for real-life conditions. The data obtained with the simulator was corrected for the variable conditions of ambient temperature, wind, incident angle, flow rate, etc., that are encountered outdoors. Modification of simulator data was accomplished by combining experiment with theory. The technique was demonstrated by application to a spectrally selective and a nonselective type of collector. This kind of modified simulator collector performance data should be valuable in solar systems analysis and for collector performance ranking based on all-day calculated conditions.
One of our primary accomplishments under grant NAGW-35381 was the systematic derivation and compilation, for the first time, of physical parameters for all gamma-ray flares detected by the SMM GRS during its ten year lifetime. The flare parameters derived from the gamma-ray spectra include: bremsstrahlung fluence and best-fit power-law parameters, narrow nuclear line fluence, positron annihilation line fluence, neutron capture line fluence, and an indication of whether or not greater than 10 MeV emissions were present. We combined this compilation of flare parameters with our plots of counting rate time histories and flare spectra to construct an atlas of gamma-ray flare characteristics. The atlas time histories display four energy bands: 56-199 kev, 298526 keV, 4-8 MeV, and 10-25 MeV. These energy bands respectively measure nonrelativistic bremsstrahlung, trans-relativistic bremsstrahlung, nuclear de-excitation, and ultra-relativistic bremsstrahlung. The atlas spectra show the integrated high-energy spectra measured for all GRS flares and dissects them into electron bremsstrahlung, positron annihilation and nuclear emission components. The atlas has been accepted for publication in the Astrophysical Journal Supplements and is currently in press. The atlas materials were also supplied to the Solar Data Analysis Center at Goddard Space Flight Center and were made available through a web site at the University of New Hampshire. Since a uniform methodology was adopted for deriving the flare parameters, this atlas will be very useful for future statistical and correlative studies of solar flares-three independent groups are presently using it to correlate interplanetary energetic particle measurements with our gamma-ray measurements. A better model for the response of the GRS instrument to high energy radiation was also developed. A refined response model was needed because the old model was not adequate for predicting the first and second escape peaks associated with strong nuclear lines nor could it accurately describe the Compton continuum shape. The new response was developed using a GEANT based simulation code and tested against preflight calibration data. The refinement of the response model and the removal of systematic errors now allow more detailed spectral studies of the GRS gamma-ray measurements. This refined response function was supplied to the Solar DAC at Goddard and was also made available via a web site at the University of New Hampshire.
Solar-Geophysical Data Number 499, March 1986, Part 1 (Prompt Reports); Data for February 1986, January 1986 and Late Data, contains the following: Detailed index for 1985 to 1986; Data for February 1986--(IUWDS alert periods (Advance and worldwide), Solar activity indices, Solar flares, Solar radio emission, Vostok inferred interplanetary magnetic field polarity, Stanford mean solar magnetic field); data for January 1986--(Solar active regions, Sudden ionospheric disturbances, Solar radio spectral observations, Cosmic ray measurements by neutron monitor, Geomagnetic indices, Radio propagation indices); Late data-(Solar active regions, Solar radio spectral observations Culgoora, Cosmic ray measurements by neutron monitor, Calcium plage data).
Solar-Geophysical Data Number 498, February 1986, Part 1 (Prompt Reports), Data for January 1986, December 1985 and Late Data contains the following:detailed index for 1985-86; data for January 1986--(IUWDS alert periods (advanced and worldwide), Solar activity indices, Solar flares, Solar radio emission, Vostok inferred interplanetary magnetic field polarity, Stanford mean solar magnetic field); data for December 1985--(Solar active regions, Sudden ionospheric disturbances, Solar radio spectral observations, Cosmic ray measurements by neutron monitor, Geomagnetic indices, Radio propagation indices); late data--(Solar radio emission Nancay interferometric chart December 1985, Solar radio spectral observations Culgoora May 1985, Geomagnetic indices sudden commencements November 1985, Calcium plage data).