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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2014

Solar Effects of Low-Earth Orbit objects in ORDEM 3.0

Variances in atmospheric density are directly related to the variances in solar flux intensity between 11- year solar cycles. The Orbital Debris Engineering Model (ORDEM 3.0) uses a solar flux table as input for calculating orbital lifetime of intact and debris objects in Low-Earth Orbit. Long term projections in solar flux activity developed by the NASA Orbital Debris Program Office (ODPO) extend the National Oceanic and Atmospheric Administration Space Environment Center (NOAA/SEC) daily historical flux values with a 5-year projection. For purposes of programmatic scheduling, the Q2 2009 solar flux table was chosen for ORDEM 3.0. Current solar flux activity shows that the current solar cycle has entered a period of lower solar flux intensity than previously forecasted in 2009. This results in a deviation of the true orbital debris environment propagation in ORDEM 3.0. In this paper, we present updated orbital debris populations in LEO using the latest solar flux values. We discuss the effects on recent breakup events such as the FY-1C anti-satellite test and the Iridium 33 / Cosmos 2251 accidental collision. Justifications for chosen solar flux tables are discussed.

Vavrin, A. B.↗
NASA NTRS2017

Solar Magnetic Activity and Solar-Stellar Connections

Prediction of solar magnetic activity on various temporal scales is a fundamental element of space weather, which requires a wide range of theoretical and observational expertise in solar phenomena from the deep interior to the corona. Historical observations have revealed many features of cyclic variations of the solar activity; but these data are dramatically insufficient to draw a physical picture of global magnetic field evolution. New observational data, currently available from space missions and ground-based observatories, provide us with detailed information about solar dynamics and magnetism. However, because of the relatively short duration of data series and the great variety of data types and quality, it is challenging to assimilate these data in theoretical models and make reliable forecasts. The recent unexpectedly weak solar activity cycles, as well as observations of rotational and magnetic topology transitions in solar-type stars, suggest that the Sun and its magnetic dynamo are currently in a very interesting evolutionary stage. This could relate to the difficulty in getting a model of the Sun to produce solar-like – rather than anti-solar-like – differential rotation, to reproduce the rotation profile obtained from helioseismology, and to predict solar activity cycles.

magnetic↗
DOE OSTI2021

Distributed Solar Adoption in Orlando: A Household-Level Model for Distribution Resource Planning

Potential for rooftop solar in Florida is massive (47% of retail sales, 3rd overall nationally), yet adoption lags (12th nationally). A 2018 Florida Public Service Commission ruling authorizing solar third-party ownership (leasing) has substantially increased attention on distributed solar in the state. The city of Orlando has committed to a 100% clean-energy target by 2050 and deployment of solar and storage are expected to contribute significantly to reaching the goal. Deployment of customer-adopted solar, unlike utility-procured solar, is uncertain, but known to be spatially correlated with demographic factors and existing adoption. We develop a new method to adapt NREL's dGen model in order to represent building-level agents in adoption forecasts for the Orlando Utility Commission (OUC) service territory. Using the agent-based model we develop projections of solar adoption, subject to scenarios varying future solar costs and valuation, and aggregate adoption predictions by OUC distribution feeder. We find substantial spatial heterogeneity in the projected level of adoption by OUC distribution feeder. For instance, 25% of all projected adoption through 2050 would be concentrated on just 5% of feeders and 88% of projected adoption on 50% of feeders. Because of the uncertainty in adoption, bottoms-up solar adoption forecasting methods at the household-level are integral to long-term resource planning by anticipating system needs as customers increasingly adopt distributed solar, storage, electric vehicles, and other distributed energy resources.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRS2011

Space Weather, Geomagnetic Disturbances and Impact on the High-Voltage Transmission Systems

Geomagnetically induced currents (GIC) affecting the performance of high-voltage power transmission systems are one of the most significant hazards space weather poses on the operability of critical US infrastructure. The severity of the threat was emphasized, for example, in two recent reports: the National Research Council (NRC) report "Severe Space Weather Events--Understanding Societal and Economic Impacts: A Workshop Report" and the North American Electric Reliability Corporation (NERC) report "HighImpact, Low-Frequency Event Risk to the North American Bulk Power System." The NRC and NERC reports demonstrated the important national security dimension of space weather and GIC and called for comprehensive actions to forecast and mitigate the hazard. In this paper we will give a brief overview of space weather storms and accompanying geomagnetic storm events that lead to GIC. We will also review the fundamental principles of how GIC can impact the power transmission systems. Space weather has been a subject of great scientific advances that have changed the wonder of the past to a quantitative field of physics with true predictive power of today. NASA's Solar Shield system aimed at forecasting of GIC in the North American high-voltage power transmission system can be considered as one of the ultimate fruits of those advances. We will review the fundamental principles of the Solar Shield system and provide our view of the way forward in the science of GIC.

Pullkkinen, A.↗
DOE OSTI2021

Using concentrating-solar-power plants as economic carbon-free capacity resources

Deep decarbonization of electricity systems raises concerns about the need for generating capacity that can maintain reliable energy supply. Given this concern, we explore the ability of concentrating-solar-power plants with thermal energy storage to provide a carbon-free source of such capacity. We develop an approach to assessing the capacity contribution of concentrating-solar-power plants that considers future systems. Such considerations are important, because of potential differences in net-load patterns compared to today (e.g., due to higher renewable-energy penetrations). Using historical data spanning an 18-year period, we demonstrate that concentrating-solar-power plants with thermal energy storage can provide this necessary capacity with little impact on their financial viability. We examine the impact of myopic decisions and imperfect foresight of future system conditions on the operation of concentrating-solar-power plants and the resultant impact on their capacity contributions. We find that imperfect foresight can have limited impacts on such a use of concentrating-solar-power plants, so long as proper forecasting techniques are developed and used. Overall, our work shows that concentrating-solar-power plants with thermal energy storage may have a role to play in delivering reliable electricity supply in a decarbonized energy system.

14 SOLAR ENERGY↗
NASA NTRS1979

A disturbance forecaster's view of the September 1977 events

The solar-geophysical events of 1977 September 7-26 are presented. A variety of disturbance forecasting problems, viz, disc transit of a center of activity with high microwave flux but relatively low meter-wave flux, flares in this region near east limb, central meridian, and west limb with major radio events, and the presence of two coronal holes, were observed. The Sydney daily GEOSYD message and IPS Disturbance Warnings issued during this period are related to the solar geophysical data available, at the time, about these events.

Cook, F. E.↗
NASA NTRS2016

What If We Had A Magnetograph at Lagrangian L5?

Synoptic Carrington charts of magnetic field are routinely used as an input for modelings of solar wind and other aspects of space weather forecast. However, these maps are constructed using only the observations from the solar hemisphere facing Earth. The evolution of magnetic flux on the "farside" of the Sun, which may affect the topology of coronal field in the "nearside," is largely ignored. It is commonly accepted that placing a magnetograph in Lagrangian L5 point would improve the space weather forecast. However, the quantitative estimates of anticipated improvements have been lacking. We use longitudinal magnetograms from the Synoptic Optical Long-term Investigations of the Sun (SOLIS) to investigate how adding data from L5 point would affect the outcome of two major models used in space weather forecast.

Pevtsov, Alexei A.↗
DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).

14 SOLAR ENERGY↗
DOE OSTI2020

Knowing our Sun: data fusion for optimizing space weather forecasts

Outline: Space weather: model for predicting solar wind & polarity; Particle filtering: optimization with Monte Carlo; Simulation & observation: twin tests and real data; Back to Earth: independent work in inertial confinement fusion (ICF)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Data efficiency assessment of generative adversarial networks in energy applications

This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1979

The mechanism of temperature and pressure changes in the Earth's atmosphere during solar flares

The effect of solar flares on the weather on Earth is examined. It is concluded that the processes which arise in the atmosphere are so intricate that a single calculation of solar activity is insufficient for long-range forecasting. However, combined consideration of processes dependent upon the dynamic instability of the atmosphere and the effect of solar activity will contribute to the improvement of long-range forecasts.

Reshetov, V. D.↗
NASA NTRS2014

STEREO as a "Planetary Hazards" Mission

NASA's twin STEREO probes, launched in 2006, have advanced the art and science of space weather forecasting more than any other spacecraft or solar observatory. By surrounding the Sun, they provide previously-impossible early warnings of threats approaching Earth as they develop on the solar far side. They have also revealed the 3D shape and inner structure of CMEs-massive solar storms that can trigger geomagnetic storms when they collide with Earth. This improves the ability of forecasters to anticipate the timing and severity of such events. Moreover, the unique capability of STEREO to track CMEs in three dimensions allows forecasters to make predictions for other planets, giving rise to the possibility of interplanetary space weather forecasting too. STEREO is one of those rare missions for which "planetary hazards" refers to more than one world. The STEREO probes also hold promise for the study of comets and potentially hazardous asteroids.

Heliosphere↗
DOE OSTI2024

HP-FLEX MPC v0.1.0

HP-FLEX MPC is control software developed by Lawrence Berkeley National Laboratory with support from the California Energy Commission (CEC) through EPIC-19-301. HP-FLEX aims to provide load flexibility for heat pumps (HPs) in response to dynamic grid signals (including Time-of-Use, Dynamic Pricing, and Critical Peak Pricing) while maintaining thermostat temperatures within user-specified bounds. The software includes a system-identification module, which models the dynamics of the building envelope with thermostat data, and a control module based on a model predictive controller (MPC) to make optimal decisions. HP-FLEX receives forecasts of outdoor air temperature, solar irradiation, and internal gain (if available), as well as trajectories of energy price, temperature lower and upper bounds over a prediction horizon. It then optimizes heating and cooling capacities to minimize energy cost and peak power (with a user-defined weight on peak power) over the prediction horizon, while maintaining room air temperature within the temperature constraints, and outputs the optimal thermostat setpoints.

Kim, Donghun↗