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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Observations and Simulations of Formation of Broad Plasma Depletions Through Merging Process

Broad plasma depletions in the equatorial ionosphere near dawn are region in which the plasma density is reduced by 1-3 orders of magnitude over thousands of kilometers in longitude. This phenomenon is observed repeatedly by the Communication/Navigation Outage Forecasting System (C/NOFS) satellite during deep solar minimum. The plasma flow inside the depletion region can be strongly upward. The possible causal mechanism for the formation of broad plasma depletions is that the broad depletions result from merging of multiple equatorial plasma bubbles. The purpose of this study is to demonstrate the feasibility of the merging mechanism with new observations and simulations. We present C/NOFS observations for two cases. A series of plasma bubbles is first detected by C/NOFS over a longitudinal range of 3300-3800 km around midnight. Each of the individual bubbles has a typical width of approx 100 km in longitude, and the upward ion drift velocity inside the bubbles is 200-400 m/s. The plasma bubbles rotate with the Earth to the dawn sector and become broad plasma depletions. The observations clearly show the evolution from multiple plasma bubbles to broad depletions. Large upward plasma flow occurs inside the depletion region over 3800 km in longitude and exists for approx 5 h. We also present the numerical simulations of bubble merging with the physics-based low-latitude ionospheric model. It is found that two separate plasma bubbles join together and form a single, wider bubble. The simulations show that the merging process of plasma bubbles can indeed occur in incompressible ionospheric plasma. The simulation results support the merging mechanism for the formation of broad plasma depletions.

plasma bubbles↗
DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration: Preprint

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions, i.e., the reserve requirement, renewable (wind and solar) power predictions, and load demand forecasts. We formulate 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. 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 RL algorithm is trained off-line using historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine and battery. Case studies demonstrated that the proposed method outperforms other operating reserve determination methods.

distribution system↗
DOE OSTI2022

Technology Scouting Report: Reliability Implications of Recent PV Module Technology Trends

This webinar links recent and anticipated PV module technology trends - such as the transition to n-type cells, increasing module sizes, and new encapsulants - with potential increases and reductions in reliability risk. Market trends are drawn from industry data and projections, and corresponding reliability implications are synthesized from interviews with researchers and industry experts. The webinar is an interim step toward developing a technology scouting report.

forecast↗
DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. We formulate 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 RL algorithm is trained offline using a historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery. Case studies demonstrated that the proposed method outperforms other policies with static operating reserves.

distribution system↗
DOE OSTI2022

Field Study of Grid-connected Heat Pump Water Heaters in the Southeast U.S.: The Next Right Thing

The integration of grid-connected functionality and advanced control algorithms into heat pump water heaters (HPWH) offers the capability to shift load with minimal customer impact. This capability provides a flexible grid resource to utilities, while the increased energy efficiency of HPWHs offers customers a lower electric bill. The combined utility/customer value from grid-connected HPWHs is compelling in the Southeast U.S. where residential electric water heating is prevalent and utility load management is common. This paper presents the results of a HPWH load shifting study conducted in Central Florida using the CTA-2045 standard. Building upon previous research in the Pacific Northwest, this study consisted of approximately 45 occupied homes equipped with HPWHs undergoing load shifting strategies weekly for over a year. Curtailment durations ranged from three to five hours in the morning, and four to five hours in the evening to coincide with high-value periods for utility coincident load for system-wide electric demand reductions. During the morning and afternoon, a one- or two-hour load-up event preceded curtailment. Baseline data were collected across varied Florida weather in which no load shifting events were implemented. Results from traditional load shifting strategies were analyzed across seasons and used to devise unique load shifting approaches to increase renewable energy use during periods of high solar energy generation. Regional impacts are forecasted for large-scale implementation of strategies. Lessons learned and recommendations are also provided for how utilities, manufacturers, and regional planners can maximize load shifting benefits from grid-connected HPWHs.

Heat pump water heaters, residential building, loa↗
DOE OSTI2023

Model-Free Probabilistic Forecasting of Nodal Voltages in Distribution Systems

As the penetration of distributed energy resources (DERs) into distribution systems increases, so does the interest in forecasting relevant system variables to help mitigate the associated challenges. One such challenge is the more frequent occurrence of excessive voltages in distribution systems with higher shares of DERs. Accurate and reliable estimates together with forecasts of system states (i.e., nodal voltages) will therefore play a key role in improving the utilization of these variable and uncertain sources while mitigating potential operational risks. Whilst recent literature has explored machine learning (ML) methods for voltage estimation and their extrapolation for a short-time period into the future, few have taken uncertainty quantification into account, and these methods have not yet been translated into operations. This paper discusses the advantages offered by probabilistic voltage forecasts and proposes a non-parametric Bayesian method suitable for forecasting nodal voltages at short-term time horizons while accounting for uncertainties in load and distributed photovoltaic (PV) generation. We demonstrate the value of the proposed Gaussian process (GP) model for a case study using historical forecasts and observation data.

distribution system↗
NASA NTRS2022

Identification of Flux Rope Orientation via Neural Networks

Geomagnetic disturbance forecasting is based on the identification of solar wind structures and accurate determination of their magnetic field orientation. For nowcasting activities, this is currently a tedious and manual process. Focusing on the main driver of geomagnetic disturbances, the twisted internal magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a convolutional neural network’s (CNN) ability to predict the embedded magnetic flux rope’s orientation once it has been identified from in situ solar wind observations. Our work uses CNNs trained with magnetic field vectors from analytical flux rope data. The simulated flux ropes span many possible spacecraft trajectories and flux rope orientations. We train CNNs first with full duration flux ropes and then again with partial duration flux ropes. The former provides us with a baseline of how well CNNs can predict flux rope orientation while the latter provides insights into real-time forecasting by exploring how accuracy is affected by percentage of flux rope observed. The process of casting the physics problem as a machine learning problem is discussed as well as the impacts of different factors on prediction accuracy such as flux rope fluctuations and different neural network topologies. Finally, results from evaluating the trained network against observed ICMEs from Wind during 1995–2015 are presented.

Thomas Narock↗
NASA NTRS1976

A search for short-term meteorological effects of solar variability in an atmospheric circulation model

A set of numerical experiments is carried out to test the short-range sensitivity of the Goddard Institute for Space Studies global atmospheric general-circulation model to changes in solar constant and ozone amount. These experiments consist of forecasts initiated with actual atmospheric data. One set of forecasts is made with a standard version of the model; another set uses the model modified by very different values of the solar constant (two-thirds and three-halves of the standard value) and of the ozone amount (zero and twice the standard amount). Twelve-day integrations with these very large variations show such small effects that the effects of realistic variations would almost certainly be insignificant meteorologically on this time scale.

Somerville, R. C. J.↗
NASA NTRS2015

Improving the Monitoring of Crop Productivity Using Spaceborne Solar-Induced Fluorescence

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change.

Guan, Kaiyu↗
DOE OSTI2024

Evaluation of the EarthSHAB Stratospheric Solar Hot Air Balloon Flight Prediction Model Using Balloon Trajectory Data

Abstract The heliotrope is a solar balloon design which is constructed out of painter’s plastic, and the exterior is coated in charcoal powder. Darkening the plastic gives the balloon a high solar absorptance, which allows it to ascend into the lower stratosphere and float for hours at a time. The balloons have previously been used to lift scientific instruments into the stratosphere to study chemical explosions, earthquakes, and stratospheric aerosols. They have also been proposed as a platform for planetary exploration. Flight predictions are crucial to preflight planning to reduce safety risks and meet flight objectives. However, there exists a wide range of possible flight paths due to varying environmental conditions and solar balloon configurations. EarthSHAB is one such software that was designed to support flight planning using the weather forecasts and balloon properties to predict the flight path of a solar balloon. We compare EarthSHAB-simulated flight paths to a set of observed flight paths for the 3.5-m diameter heliotrope design called the “Cloudskimmer.” Using the criteria that the modeled paths must fall within 5% of the observations to be considered successful, we found that EarthSHAB successfully predicted the Cloudskimmer ascent rate and average float altitude 10% and 90% of the time, respectively. We also found that the average difference in the observed and predicted landing locations was 97 km and landing times were 54 ± 38 min. Significant deviations between the observed and predicted ascent rates and excursions at float were found to be associated with heavy payloads and convective cloud development, respectively. Significance Statement Solar balloons are used to lift scientific instruments into the lower stratosphere for hours at a time to study chemical explosions, earthquakes, stratospheric aerosols, and more. The flight paths of solar balloons can be difficult to predict due to variability in their design and surrounding environment. We evaluate the accuracy of EarthSHAB, a software that predicts the altitude profile and horizontal trajectory of a solar balloon using inputs such as the weather, balloon size, and balloon mass. Our results suggest that for the balloon design used in this study, EarthSHAB is best suited for modeling the behavior of balloons with lightweight payloads that do not fly within or directly above clouds.

Lien, Jessica M. [Sandia National Laboratories, Al↗
NASA NTRS2012

Toward the Probabilistic Forecasting of High-latitude GPS Phase Scintillation

The phase scintillation index was obtained from L1 GPS data collected with the Canadian High Arctic Ionospheric Network (CHAIN) during years of extended solar minimum 2008-2010. Phase scintillation occurs predominantly on the dayside in the cusp and in the nightside auroral oval. We set forth a probabilistic forecast method of phase scintillation in the cusp based on the arrival time of either solar wind corotating interaction regions (CIRs) or interplanetary coronal mass ejections (ICMEs). CIRs on the leading edge of high-speed streams (HSS) from coronal holes are known to cause recurrent geomagnetic and ionospheric disturbances that can be forecast one or several solar rotations in advance. Superposed epoch analysis of phase scintillation occurrence showed a sharp increase in scintillation occurrence just after the arrival of high-speed solar wind and a peak associated with weak to moderate CMEs during the solar minimum. Cumulative probability distribution functions for the phase scintillation occurrence in the cusp are obtained from statistical data for days before and after CIR and ICME arrivals. The probability curves are also specified for low and high (below and above median) values of various solar wind plasma parameters. The initial results are used to demonstrate a forecasting technique on two example periods of CIRs and ICMEs.

scintillation↗
NASA NTRS2013

Do Solar Coronal Holes Affect the Properties of Solar Energetic Particle Events?

The intensities and timescales of gradual solar energetic particle (SEP) events at 1 AU may depend not only on the characteristics of shocks driven by coronal mass ejections (CMEs), but also on large-scale coronal and interplanetary structures. It has long been suspected that the presence of coronal holes (CHs) near the CMEs or near the 1-AU magnetic footpoints may be an important factor in SEP events. We used a group of 41 E (is) approx. 20 MeV SEP events with origins near the solar central meridian to search for such effects. First we investigated whether the presence of a CH directly between the sources of the CME and of the magnetic connection at 1 AU is an important factor. Then we searched for variations of the SEP events among different solar wind (SW) stream types: slow, fast, and transient. Finally, we considered the separations between CME sources and CH footpoint connections from 1 AU determined from four-day forecast maps based on Mount Wilson Observatory and the National Solar Observatory synoptic magnetic-field maps and the Wang-Sheeley-Arge model of SW propagation. The observed in-situ magnetic-field polarities and SW speeds at SEP event onsets tested the forecast accuracies employed to select the best SEP/CH connection events for that analysis. Within our limited sample and the three analytical treatments, we found no statistical evidence for an effect of CHs on SEP event peak intensities, onset times, or rise times. The only exception is a possible enhancement of SEP peak intensities in magnetic clouds.

Coronal mass ejections - low coronal signatures↗
DOE OSTI2020

What Is the Value of Alternative Methods for Estimating Ramping Needs?

Power system operators procure and deploy flexibility reserves or ramping products to address balancing needs caused by uncertainty and variability of load and generation. Existing methods estimate ramping needs using calendar information and historical forecast errors. Novel methods investigate if real-time weather information could inform ramping and other balancing requirements. This paper compares estimation methods for ramping requirements in theory and practice. The theoretical framework indicates when an alternative method could yield improved economic or reliability performance than existing methods by requiring lower or higher levels of ramping products. Preliminary simulations on a 118-bus test system for 4 days in May 2019 illustrate how system performance improves or deteriorates when ramping requirements are weather-informed (alternative) instead of calendar-based (baseline). Preliminary results suggest high variability in change of performance and underline the impact of additional factors, such as system conditions, on the realized performance change.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRS2000

Theoretical Technology Research for ISTP/SOLARMAX

During the last decade, we have been developing theoretical tools to support the scientific objectives of the International Solar Terrestrial Physics (ISTP) program. Results from our mission-oriented theory program have contributed significantly to the development of predictive capabilities by using real upstream solar wind conditions as input to our models and forecasting events observed downstream near Earth. We also developed the capability to unravel the complex information contained in ion velocity distribution functions measured near the Earth to determine their origin and energization process. During solar maximum, solar flares and coronal mass ejections (CMEs) dominate the sun's activity. It is now widely accepted that the impact of CMEs (or magnetic clouds) with the Earth's magnetosphere is the cause of most magnetic storms during solar maximum. One important aspect of a CME is the occurrence of solar energetic particle (SEP) events. During these events, protons, electrons, and heavy ions of solar origin are accelerated to very high energies by shock waves driven out from the sun. We carried out a series of large-scale kinetic (LSK) simulations to model the effect of SEPs on the near-Earth environment and the accessibility of these high-energy particles to the inner magnetosphere. We present the results of these studies.

Ashour-Abdalla, Maha↗