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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Evaluating WRF-Solar EPS cloud mask forecast using the NSRDB

Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model's capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%-46%, 13%-34%, and 8%-19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%-46%, 16%-33%, and 8%-27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.

14 SOLAR ENERGY↗
DOE OSTI2022

Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)

Hybrid-RL-MPC4CLR was developed as a hybrid controller for active distribution grid critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. The RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while the MPC models grid operations incorporating the RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. The developers formulated the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The software is developed using various software packages in Python. The MPC's optimal power flow (OPF) model is implemented using the Pyomo package, the RL simulation environment is implemented using the MPC simulation with various scenarios of renewable energy and load demand profiles and power outage beginning times, based on the OpenAI Gym framework. The RL agent training is performed using the RLlib Ray package. The RL algorithm is trained offline using historical forecasts of renewable generation and load demand profiles. Simulation analysis and performance tests are conducted using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery.

Eseye, Abinet Tesfaye↗
NASA NTRS1989

A simple analytical formula to compute clear sky total and photosynthetically available solar irradiance at the ocean surface

A simple but accurate analytical formula was developed for computing the total and the photosynthetically available solar irradiances at the ocean surface under clear skies, which takes into account the processes of scattering by molecules and aerosols within the atmosphere and of absorption by the water vapor, ozone, and aerosols. These processes are parameterized as a function of solar zenith angle, aerosol type, atmospheric visibility, and vertically integrated water-vapor and ozone amounts. Comparisons of the calculated and measured total and photosynthetically available solar irradiances for several experiments in tropical and mid-latitude ocean regions show 39 and 14 Wm/sq m rms errors (6.5 and 4.7 percent of the average measured values) on an hourly time scale, respectively. The proposed forumula is unique in its ability to predict surface solar irradiance in the photosynthetically active spectral interval.

Frouin, Robert↗
NASA NTRS1979

Analysis of regression methods for solar activity forecasting

The paper deals with the potential use of the most recent solar data to project trends in the next few years. Assuming that a mode of solar influence on weather can be identified, advantageous use of that knowledge presumably depends on estimating future solar activity. A frequently used technique for solar cycle predictions is a linear regression procedure along the lines formulated by McNish and Lincoln (1949). The paper presents a sensitivity analysis of the behavior of such regression methods relative to the following aspects: cycle minimum, time into cycle, composition of historical data base, and unnormalized vs. normalized solar cycle data. Comparative solar cycle forecasts for several past cycles are presented as to these aspects of the input data. Implications for the current cycle, No. 21, are also given.

Lundquist, C. A.↗
NASA NTRS2009

Validation of Community Models: Identifying Events in Space Weather Model Timelines

I develop and document a set of procedures which test the quality of predictions of solar wind speed and polarity of the interplanetary magnetic field (IMF) made by coupled models of the ambient solar corona and heliosphere. The Wang-Sheeley-Arge (WSA) model is used to illustrate the application of these validation procedures. I present an algorithm which detects transitions of the solar wind from slow to high speed. I also present an algorithm which processes the measured polarity of the outward directed component of the IMF. This removes high-frequency variations to expose the longer-scale changes that reflect IMF sector changes. I apply these algorithms to WSA model predictions made using a small set of photospheric synoptic magnetograms obtained by the Global Oscillation Network Group as input to the model. The results of this preliminary validation of the WSA model (version 1.6) are summarized.

MacNeice, Peter↗
NASA NTRS1996

Heuristic Modeling for TRMM Lifetime Predictions

Analysis time for computing the expected mission lifetimes of proposed frequently maneuvering, tightly altitude constrained, Earth orbiting spacecraft have been significantly reduced by means of a heuristic modeling method implemented in a commercial-off-the-shelf spreadsheet product (QuattroPro) running on a personal computer (PC). The method uses a look-up table to estimate the maneuver frequency per month as a function of the spacecraft ballistic coefficient and the solar flux index, then computes the associated fuel use by a simple engine model. Maneuver frequency data points are produced by means of a single 1-month run of traditional mission analysis software for each of the 12 to 25 data points required for the table. As the data point computations are required only a mission design start-up and on the occasion of significant mission redesigns, the dependence on time consuming traditional modeling methods is dramatically reduced. Results to date have agreed with traditional methods to within 1 to 1.5 percent. The spreadsheet approach is applicable to a wide variety of Earth orbiting spacecraft with tight altitude constraints. It will be particularly useful to such missions as the Tropical Rainfall Measurement Mission scheduled for launch in 1997, whose mission lifetime calculations are heavily dependent on frequently revised solar flux predictions.

Jordan, P. S.↗
DOE OSTI2020

The Near-infrared Spectrum of the Nuclear Star Cluster: Looking below the Tip of the Iceberg, and Comparisons with Extragalactic Nuclei

Long-slit near-infrared (NIR) spectra of the Galactic nuclear star cluster (NSC) are discussed. The spectra sample the major axis of the NSC out to its half-light radius. The absorption spectrum of the central regions of the NSC is averaged over angular scales of tens of arc seconds in order to sample globular cluster-like total luminosities, and the results are compared with model spectra. The equivalent widths of Na i2.21 μm and Ca 2.26 μm outside of the center of the NSC, where light from nuclear bulge stars contributes a large fraction to the total flux, are consistent with solar chemical mixtures. In contrast, the equivalent widths of Na i2.21 μm and Ca i2.26 μm near the center of the NSC are larger than expected from models with solar chemical mixtures, even after light from the brightest evolved stars is removed. The depths of spectroscopic features changing along the major axis of the NSC are consistent with imaging studies that have found evidence of population gradients in the NSC. That Na i2.21 μm and Ca i2.26 μm are deeper than predicted for solar chemical mixtures over a range of evolutionary states is consistent with previous studies that find that the majority of stars near the center of the NSC formed from material that had nonsolar chemical mixtures. The depths of the Na i2.21 μm and Ca i2.26 μm features in the central regions of the NSC are comparable to those in the nuclear spectrum of the early-type Virgo disk galaxy NGC 4491, and are deeper than in the central spectra of NGC 253 and 7793. A spectrum of nebular emission and the youngest stars near the galactic center is also extracted. The equivalent widths of emission features in the extracted NIR spectrum are similar to those in the nuclear spectrum of NGC 253, and it is argued that this agreement is best achieved if the current episode of star formation near the center of the NSC has been in progress for at least a few megayears.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Solar Neutrino Observations at the Sudbury Neutrino Observatory

The Sudbury Neutrino Observatory (SNO) is a 1000-tonne heavy water Cherenkov detector. Its usage of \dto as target allows the simultaneous measurements of the $\nu_e$ flux from $^8$B decay in the Sun and the total flux of all active neutrino species through the charged-current and the neutral-current interactions on the deuterons. Assuming the standard $^{8}$B shape, the $\nu_e$ component of the $^{8}$B solar neutrino flux is measured to be $\phi_e = 1.76^{+0.05}_{-0.05}{(stat.)}^{+0.09}_{-0.09} {(syst.)} x 10^6 {\rm cm}^{-2} {\rm s}^{-1}$ for a kinetic energy threshold of 5 MeV. The non-\nue component is found to be Φ μτ $\= 3.41^{+0.45}_{-0.45}{(stat.)}^{+0.48}_{-0.45} {(syst.)} x 10^6 {\rm cm}^{-2} {\rm s}^{-1}$. This $5.3\sigma$ difference provides strong evidence for $\nu_{e}$ flavor transformation in the solar neutrino sector. The total active neutrino flux is measured with the neutral-current reaction at a neutrino energy threshold of 2.2 MeV. This flux is determined to be $\phi_{NC} = 5.09^{+0.44}_{-0.43}{(stat.)}^{+0.46}_{-0.43} {(syst.)} x 10^6 {\rm cm}^{-2} {\rm s}^{-1}$, and is consistent with solar model predictions. Assuming an undistorted $^8$B spectrum, the night minus day rate is 14.0$\pm$6.3(stat.)$^{+1.5}_{-1.4}$(sys.)\% of the average rate in the charged-current channel. If the total active neutrino flux is constrained to have no asymmetry, the night-day asymmetry in the $\nu_e$ flux is found to be 7.0$\pm$4.9(stat.)$^{+1.3}_{1.2}$(sys.)\%. A global analysis of all the available solar neutrino data in terms of matter-enhanced oscillations of two active flavors strongly favors the Large Mixing Angle (LMA) solution.

Poon, A. W. P.↗
DOE OSTI2021

Intelligent Prediction of States in Multi-port Autonomous Reconfigurable Solar power plant (MARS)

In power electronics, prediction of states may be used for identification of faults, determination of aging of components, identification of bad data measurements, among others. Prediction of states in power electronics have broadly been based on: (a) physics-based models, (b) data-driven models, and (c) hybrid models. In this paper, data-driven approaches are presented for intelligent prediction of states in multi-port autonomous reconfigurable solar power plant (MARS) and compared. The data-set needed to train the data-driven models based on artificial intelligence (AI) algorithms has been identified and the trained models are evaluated under different extrapolated normal and abnormal operating conditions. The AI algorithms include nonlinear auto-regressive exogenous model (NARX), spiking neural networks (SNN), and decision tree. The models are compared and contrasted. The best model (NARX) is evaluated under different normal and abnormal operating conditions that have indicated accurate prediction.

Debnath, Suman↗
NASA NTRS2019

Forecasting the Ambient Solar Wind with Numerical Models. I. On the Implementation of an Operational Framework

The ambient solar wind conditions in interplanetary space and in the near-Earth environment are determined by activity on the Sun. Steady solar wind streams modulate the propagation behavior of interplanetary coronal mass ejections and are themselves an important driver of recurrent geomagnetic storm activity. The knowledge of the ambient solar wind flows and fields is thus an essential component of successful space weather forecasting. Here, we present an implementation of an operational framework for operating, validating, and optimizing models of the ambient solar wind flow on the example of Carrington Rotation 2077. We reconstruct the global topology of the coronal magnetic field using the potential field source surface model (PFSS) and the Schatten current sheet model (SCS) and discuss three empirical relationships for specifying the solar wind conditions near the Sun, namely the Wang–Sheeley (WS) model, the distance from the coronal hole boundary model (DCHB), and the Wang-Sheeley–Arge (WSA) model. By adding uncertainty in the latitude about the sub-Earth point, we select an ensemble of initial conditions and map the solutions to Earth by the Heliospheric Upwind eXtrapolation (HUX) model. We assess the forecasting performance from a continuous variable validation and find that the WSA model most accurately predicts the solar wind speed time series (RMSE≈83 km/s). We note that the process of ensemble forecasting slightly improves the forecasting performance of all solar wind models investigated. We conclude that the implemented framework is well suited for studying the relationship between coronal magnetic fields and the properties of the ambient solar wind flow in the near-Earth environment.

Reiss, Martin A.↗
NASA NTRS1979

Development of an Accelerated Test Design for Predicting the Service Life of the Solar Array at Mead, Nebraska

An accelerated life test is described which was developed to predict the life of the 25 kW photovoltaic array installed near Mead, Nebraska. A quantitative model for accelerating testing using multiple environmental stresses was used to develop the test design. The model accounts for the effects of thermal stress by a relation of the Arrhenius form. This relation was then corrected for the effects of nonthermal environmental stresses, such as relative humidity, atmospheric pollutants, and ultraviolet radiation. The correction factors for the nonthermal stresses included temperature-dependent exponents to account for the effects of interactions between thermal and nonthermal stresses on the rate of degradation of power output. The test conditions, measurements, and data analyses for the accelerated tests are presented. Constant-temperature, cyclic-temperature, and UV types of tests are specified, incorporating selected levels of relative humidity and chemical contamination and an imposed forward-bias current and static electric field.

Gaines, G. B.↗
NASA NTRS1983

A study of plasmaspheric density distributions for diffusive equilibrium conditions

The plasmaspheric density distribution has been modeled for a range of solar cycle, seasonal and diurnal conditions with a magnetic flux tube dependent diffusive equilibrium model by using experimentally determined values of ionospheric parameters at 675 km as boundary conditions. Data is presented in terms of plasmaspheric H(+) and He(+) density contours, total flux tube content, and equatorial plasma density for a range of L-values from 1.15 to 3.0. The variation of equatorial density with L-value shows good agreement with the 1/L exp 4 dependence observed experimentally. The results show that the model predicts larger solar cycle and diurnal variation in equatorial plasma density than observed using whistler techniques. However, the whistler method requires a model to deduce the equatorial density and is therefore open to interpretation. Seasonal variations are rather artificial since in this general model no attempt has been made to match equatorial densities for flux tubes emanating from the winter and summer hemispheres.

Li, W.↗
NASA NTRS1985

The cold solar wind

From 3 to 10 percent of the time, the solar wind has a radial proton temperature between 15,000 K and 3500 K (the lowest recorded temperature). This 'cold' component shows some very important features that make it worthy of special study. It shows a temperature dependence on helicentric distance that is nearly adiabatic. When extrapolated back to 3 R(s), assuming adiabatic cooling, the resulting temperature for this cold component is in good agreement with coronal temperatures near the sonic point. Most importantly, the proton velocities and densities associated with this cold component match those predicted by solar wind models that do not incorporate significant wave heating. It is concluded that this is one solar wind component that fits well with appropriate models.

Freeman, J. W.↗
DOE OSTI2022

Modeling the Conformer-Dependent Electronic Absorption Spectra and Photolysis Rates of Methyl Vinyl Ketone Oxide and Methacrolein Oxide

Criegee intermediates are important atmospheric oxidants, formed via the reaction of ozone with volatile alkenes emitted into the troposphere. Small Criegee intermediates (e.g., CH 2 OO and CH 3 CHOO) are highly reactive, and their removal via unimolecular decay or bimolecular chemistry dominates their atmospheric lifetimes. As the molecular complexity of Criegee intermediates increases, their electronic absorption spectra show a bathochromic shift within the solar spectrum relevant to the troposphere. In such cases, solar photolysis may become a competitive contributor to their atmospheric removal. In this article, we report the conformer-dependent simulated electronic absorption spectra of two four-carbon-centered Criegee intermediates, methyl vinyl ketone oxide (MVK-oxide) and methacrolein oxide (MACR-oxide). Both MVK-oxide and MACR-oxide contain four low-energy conformers, which are convoluted in the experimentally measured spectra. Here, we deconvolute each conformer and estimate contributions from each of the four conformers to the experimentally measured spectra. We also estimate the photolysis rates and predict that solar photolysis should be a more competitive removal process for MVK-oxide and MACR-oxide (cf. CH 2 OO and CH 3 CHOO).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Computer Vision on Edge Devices for the Short Term Prediction of Cloud Cover

Edge Computing and IoT are important pieces of today's technological landscape. Here, we build a low-cost IoT sensor for sky imaging and program it using AWS GreenGrass, one of the leading IoT platforms. We demonstrate remote reprogramming of this device to load software that predicts sun shading events through the linear advection method, which is a baseline algorithm that can be used to benchmark algorithmic improvements in future work. Some future directions for sky imaging research are enumerated.

14 SOLAR ENERGY↗
DOE OSTI2022

Future-proofing photovoltaics module reliability through a unifying predictive modeling framework

Solar energy, especially photovoltaics (PV), plays a significant role in the global energy transition required for decarbonization. Technological advancements in increasing efficiencies coupled with cost reductions through economies of scale made PV a cost-competitive energy source set to exceed the milestone of 1 TW globally deployed capacity in 2022. However, ongoing downward price pressure coupled with increasing service life expectations creates tremendous challenges for reliability engineers in ensuring the safe and reliable operation of PV modules and systems over the anticipated service life. Today's reliability research efforts aim for module lifetimes of up to 50 years. Still, our current tools fall short in accurately assessing degradation mechanisms and failure modes over such extended periods. We argue that the well-established PV reliability learning cycle needs to be accelerated to keep up with the rapid technological advancements and high expectations that are put on PV and its role in the global energy transition. In this article, we explore the evolution of the PV reliability learning cycle and highlight the significance that predictive modeling capabilities will have on future PV module reliability. We propose creating a unifying modeling framework - which once established will enable the holistic assessment of PV module reliability, accelerate the PV reliability learning cycle, and bring us closer to quantitative service life predictions of PV modules.

14 SOLAR ENERGY↗
DOE OSTI2021

EMOS (Energy Management Optimization System) [SWR-21-46]

EMOS software performs a real-time hierarchal optimal control for energy systems like multi-port electric vehicles charging site with distributed energy resources (DERs) and energy storage systems (ESSs). It gathers information in real-time from electric vehicles, power grid, and DERs, solves a multi-objective energy management optimization problem, and output setpoint for chargers, ESS converters, DERs converters, and grid converters. The software incorporates a novel integration of two control tasks: a) An Energy Management Optimization (EMO), which is the brain of EMOS controller that gathers information in real-time from EVs [e.g., battery size, state-of-charge (SOC), desired SOC, and charge acceptance curve], grid (e.g., electricity price, allowed feeder capacity, ramp rate limit, and reactive power), and DERs (e.g., prediction for solar generation for PV systems). It solves a control optimization problem in real-time to find optimal setpoint for ESSs power dispatch, EVs charging rate, and grid inverters. The objectives are to (1) minimize the charging cost considering grid energy, demand charges, and battery energy, (2) minimize charging time to meet fast charging criterion, (3) keep high energy level on ESSs at the end of an operating period, while satisfying constraints related to grid, EVs, and power converters. b) Real-Time Energy Management System (RT-EMS) is a rule-based algorithm that has faster response than EMO. It receives optimal setpoint from EMO and actual measurements from the system and modify the setpoint to compensate for any fast disturbance in the system, until a new optimum solution is received. Fast disturbances may include vehicle connect/disconnect, unpredicted variation in DERs profiles, variation in grid voltage, errors in PV generation prediction, and others. In addition, RT-EMS regulates voltage at point of common coupling (PCC) by managing reactive power of grid converters.

Mohamed, Ahmed↗