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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Deep Learning-enhanced Block-Diagram Modeling of Solar Power Systems

Data-driven models of power system inverter-based resources are desired to run simulations faster than with detailed electromagnetic transient models, to hide proprietary design details, to support control system design applications, and to aggregate the effects of distributed energy resources. This paper applies a customized Hammerstein Wiener framework to train block diagram models from thousands of electromagnetic transient simulations or experimental test records. The block diagram models integrate with larger grid simulations as voltagecontrolled current sources or current-controlled voltage sources for several simulators. Guidelines for block architecture and training are presented. Three-phase balanced, three-phase unbalanced, and single-phase examples all achieve an acceptable root mean square error of no more than 0.05 per-unit.

Mcdermott, Thomas E. [Private consulting company]↗
NASA NTRS2017

STEREO Superior Solar Conjunction Mission Phase

With its long duration and high gain antenna (HGA) feed thermal constraint; the NASA Solar-TErestrial RElations Observatory (STEREO) solar conjunction mission phase is quite unique to deep space operations. Originally designed for a two year heliocentric orbit mission to primarily study coronal mass ejection propagation, after 8 years of continuous science data collection, the twin STEREO observatories entered the solar conjunction mission phase, for which they were not designed. Nine months before entering conjunction, an unforeseen thermal constraint threatened to stop daily communications and science data collection for 15months. With a 3.5 month long communication blackout from the superior solar conjunction, without ground commands, each observatory will reset every 3 days, resulting in 35 system resets at an Earth range of 2 AU. As the observatories will be conjoined for the first time in 8 years, a unique opportunity for calibrating the same instruments on identical spacecraft will occur. As each observatory has lost redundancy, and with only a limited fidelity hardware simulator, how can the new observatory configuration be adequately and safely tested on each spacecraft? Without ground commands, how would a 3-axis stabilized spacecraft safely manage the ever accumulating system momentum without using propellant for thrusters? Could science data still be collected for the duration of the solar conjunction mission phase? Would the observatories survive? In its second extended mission, operational resources were limited at best. This paper discusses the solutions to the STEREO superior solar conjunction operational challenges, science data impact, testing, mission operations, results, and lessons learned while implementing.

STEREO↗
DOE OSTI2021

SPECs Early-Stage Decision Model: User Manual

The SPECs Early-Stage Decision (ESD) model is central to the SPECs procurement solutions toolkit. The ESD model is an Excel-based spreadsheet model, which provides information about the economic and strategic value of a proposed battery-storage project or solar-plus-storage (solar-plus) project. The model can be used to explore combinations of storage-related project value streams in order to define a potential project, while educating co-op decision-makers about project benefits and costs. A sensitivity analysis function speeds the development of "what-if" scenarios. A gap analysis function solves for top-priority metrics and supports the inclusion of hard-to-monetize strategic values, such as the value of storage to defer costly system upgrades in light of increasing distributed solar and other distributed energy resources (DERs). Model outputs include the utility data, assumptions, and use-case scenarios that are recommended content for the requests for proposals (RFPs). The model may also provide an initial "sanity check" for RFP responses, supporting further discussions among utility staff, vendors and stakeholders. The ESD is not a "finance-grade" modeling tool, and users are cautioned to be mindful of its limitations, but the model has been reviewed by users, who recommend it as a way to drive faster and better project design and planning, as well as to facilitate better communications with vendors, grid partners, and stakeholders.

14 SOLAR ENERGY↗
NASA NTRS1978

The goldstone energy project

The Golstone Energy Project was established in 1974 to investigate ways in which the Goldstone Deep Space Complex in California could be made partly or completely energy-sufficient, especially through the use of solar- and wind-derived energy resources. Ways in which energy could be conserved at the Complex were also studied. Findings included data on both wind and solar energy. Obstacles to demonstrating energy self-sufficiency are: (1) operation and maintenance costs of solar energy systems are estimated to be much higher than conventional energy systems, (2) initial capital costs of present-day technology solar collectors are high and are compounded by low collector efficiency, and (3) no significant market force exists to create the necessary industry to reduce costs through mass production and broad open-market competition.

Bartos, K. P.↗
DOE OSTI2025

Solar Photovoltaic (PV) Module Facts and Trends

Unprecedented growth of solar PV has led to growing concerns about PV module toxicity and potential environmental and human health impacts. This factsheet provides objective, science-based data to help address these concerns and empower communities with the resources they need to make solar PV decisions.

14 SOLAR ENERGY↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

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.

climate change↗
NASA NTRS2019

Differences Between OCO‐2 and GOME‐2 SIF Products From a Model‐Data Fusion Perspective

Space-borne retrievals of solar-induced chlorophyll fluorescence (SIF) over land surfaces have recently become a resource for studying and quantifying the broad scale dynamics of gross carbon uptake (gross primary productivity—GPP) across ecosystems. To prepare for the assimilation of SIF data in terrestrial biosphere models, we examine how differences between SIF products (due to differences in acquisition characteristics and processing chain) may affect the optimization of model parameters and the resultant GPP estimate. We compare recent daily mean SIF products (one from the Orbiting Carbon Observatory-2 [OCO-2] and two from the Global Ozone Monitoring Experiment–2 [GOME-2], GlobFluo [GF] and NASA-v28 [N28], missions), averaged at 0.5° × 0.5° spatial resolution and 16-day temporal resolution, at the biome level. Phase differences between these products are relatively small. A first-order correction of the difference in spectral sampling between the two instruments shows that OCO-2 and N28 are consistent in terms of magnitude and amplitude, while GF is twice as large as the others. Using a bias-blind toy data assimilation framework, we analyze how biases between SIF products, and between model and products, can be partially alleviated by optimizing the slope and intercept parameters of a linear GPP-SIF operator. As observation biases can transfer to biases in other optimized process-based parameters and to modeled carbon fluxes— thereby resulting in unidentified inaccurate parameter values—we argue that potential SIF biases should be treated cautiously in real-world experiments in order to achieve realistic and reliable future simulations.

Gross primary production↗
DOE OSTI2022

Wind and Solar Hybrid Power Plants for Energy Resilience

Wind-solar-storage hybrid power plants represent a significant and growing share of new proposed projects in the United States (U.S.). Their uptake is supported by increasing renewable energy market share, technical abilities for dispatch and control, and decreasing wind, solar, and battery storage costs. Simultaneously, generation and storage resources are increasingly used in distributed power systems. While concerns around the reliability of the aging, transforming U.S. electric grid are growing, diversifying energy resources through hybridization or spatial distribution provides an opportunity to enhance power system resilience compared to single-source generation. Understanding where to build hybrids for resilience value, rather than bulk power supply, has not been fully explored in previous studies. Therefore, in this study, we complete a national complementarity analysis to identify areas in the U.S. that are particularly suited for wind-solar hybrid power plant development. We show the importance of seasonal and diurnal patterns in assessing complementarity, and identify that regions in the Great Plains, Midwest, and Southeast are particularly suited for hybrid power plants. We demonstrate the resilience value of hybridization for a reference system based near Memphis, Tennessee, and show optimal sizing of wind, solar, and storage assets given 1.0 and 0.9 critical load factors. Results indicate that pairing wind and solar assets better meet constant load demand and reduce storage requirements compared to solar alone. These results enable future work integrating complementarity metrics in resilience frameworks and indicate a need for more finer resolution of local resource, demand, and hazard data.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Providing Long-term Solar and Meteorological Resource Information from NASA Research through the GIS-Enabled POWER Web Services Portal

Solar and other renewable energy systems are optimized using geophysical parameters describing information about the solar resource and meteorological environments and how those environments may be changing in time. The Prediction of Worldwide Renewable Energy Resource (POWER) team at the NASA LaRC Research Center deployed its first version of the revitalized POWER geophysical parameter website that employs Esri Geographic Information System (GIS) tools. This web application provides access to both time series and climatological data sets spanning from a few days behind real-time back to the early 1980’s with a spatial resolution of 0.5 x 0.5 degree, thus extending over 30 years. The GIS tools enable, generate and store climatological averages using spatial queries and calculations in a spatial database resulting in greater accessibility to government agencies, industry and individuals. There is an API that provides data through a URL coding and can also invoked inside user software packages. Output data formats now include ASCII, CSV, geoTiff, JSON and .netCDF. Since May 2018, over 10.4 TB of data have been delivered to fulfill over 110 million data requests from 240,000 unique user IPs (https://power.larc.nasa.gov). This presentation provides an overview of this project and the current version of the new POWER web capabilities through to the end usage. Surface solar parameters are adapted from both the NASA/GEWEX (Global and Energy Water Cycle Exchange) SRB (Surface Radiation Budget) project and the CERES (Clouds and Earth’s Radiant Energy System) data products. The meteorological parameters are adapted from NASA’s MERRA-2 (Modern Era Retrospective-analysis for Research and Applications). We review uncertainty of various basic parameters using surface measurements. After the introduction, we specifically discuss various clusters of parameters for equator pointing tilted surfaces that provide for the estimation of optimal tilt angle by month and year. The discussion will emphasize a few examples of how solar and building engineers are using the data products. We will then preview the capabilities of Version 2 featuring examples of hourly solar and meteorological data products, customized user reports and expanded web mapping services from CERES data products. The web services provide a unique, expandable resource for renewable energy systems engineers to design, evaluate and optimize to environments worldwide.

Solar Resource↗
DOE OSTI2021

A Nonstationary and Non-Gaussian Moving Average Model for Solar Irradiance

Historically, power has flowed from large power plants to customers. Increasing penetration of distributed energy resources such as solar power from rooftop photovoltaic has made the distribution network a two-way-street with power being generated at the customer level. The incorporation of renewables introduces additional uncertainty and variability into the power grid. Distribution network operation studies are being adapted to include renewables; however, such studies require high quality solar irradiance data that adequately reflect realistic meteorological variability. Data from satellite-based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high frequency variation at very fine timescales. We propose a new stochastic method for temporally downscaling global horizontal irradiance (GHI) to 1 min resolution, but we do not consider the spatial aspect due to limited availability of the in situ irradiance measurements. Solar irradiance's first and second-order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Empirical irradiance data exhibits highly non-Gaussian behavior; we develop a nonstationary and non-Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood-based approaches. The model is demonstrated for a case study of three locations that are located in diverse climates through the United States. The model is compared against competitors from the literature and is shown to provide better uncertainty and variability quantification on testing data.

Cholesky factor↗
DOE OSTI2023

Data-Driven Approach to Transactive Energy Systems with Commercial Buildings

A microgrid with solar, storage, and responsive load resources has been implemented and tested on an urban academic campus. Through modeling and simulation, a consensus transactive energy mechanism has been implemented, with each resource participating as a virtual battery. Most owners of large buildings don't have the information and expertise to develop and validate suitable models of their buildings using available tools. To mitigate this adoption barrier, a data-driven building model has been implemented and validated. It uses 5-minute weather data, 3-second revenue meter data, energy audit information, and a load reduction test conducted by the building owner.

Buildings, data-driven modeling, deep learning, en↗
DOE OSTI2025

U.S. Agrivoltaics Irradiance Database

This is a foundational data set for research and deployment of agrivoltaics, which is the co-location of agriculture and solar power plants on the same land. This irradiance and shading dataset can be utilized to determine the suitability of agrivoltaics configurations for a given region and crop-type. The data is hourly, 4x4 km resolution across the contiguous United States and Hawaii. It is calculated from the National Solar Radiation Database sites, using the System Advisor Model (SAM) to simulate the shading patterns for 10 common agrivoltaics configurations. Sunlight availability data is reported for 10 locations on the ground between adjacent rows of solar panels, as well as averaged across areas of interest such as the average irradiance in the edge-to-edge open area or across 3-6 planting beds. Other available metrics include the input meteorological data from the NSRDB (e.g. global horizontal irradiance, wind speed, etc.) and estimates for comparing energy and agricultural characteristic across the 10 configurations, including power output per acre or per kW installed capacity and farmable land area per acre.

14 SOLAR ENERGY↗
NASA NTRS1992

Atlas of albedo and absorbed solar radiation derived from Nimbus 7 earth radiation budget data set, November 1985 to October 1987

An atlas of monthly mean global contour maps of albedo and absorbed solar radiation is presented for 21 months from Nov. 1985 to Oct. 1987. These data were retrieved from measurements made by the shortwave wide-field-of-view radiometer of the Earth Radiation Budget (ERB) instrument aboard the Nimbus 7 spacecraft. Profiles of zonal mean albedos and absorbed solar radiation were tabulated. These geographical distributions are provided as a resource for researchers studying the radiation budget of the Earth. The El Nino/Southern Oscillation event of 1986-1987 is included in this data set. This atlas of albedo and absorbed solar radiation extends to 12 years the period covered by two similar atlases: NASA RP-1230 (Jul. 1975 - Oct. 1978) and NASA RP-1231 (Nov. 1978 - Oct. 1985). These three compilations complement the atlases of outgoing longwave radiation by Bess and Smith in NASA RP-1185, RP-1186, and RP-1261, which were also based on the Nimbus 6 and 7 ERB data.

Smith, G. Louis↗
NASA NTRS1981

Application of HCMM data to regional geologic analysis for mineral and energy resource evaluation

The effects of topographic elevation and slope on thermal images were examined using representative field obstructions of the solar and sky radiation and a simple linearized thermal model. The forms are easily adapted for analysis of HCMM images using the DMA digital terrain data. From a representative set of field observations, it was found that flux variations with elevation can cause changes in the mean diurnal temperature gradient from -4 deg to -14 deg per km evaluated at 2000 m. Changes in the temperature difference gradient of 1 to 2 C per km are also produced which is equivalent to an effective thermal inertia gradient of 100 W s to the 1/2 power/sq m K/1 per km. Exposed bedrock on topographic ridges will appear to have a lower thermal inertia due to the additional effect.

Watson, K.↗
DOE OSTI2022

Enhanced Control, Optimization, and Integration of Distributed Energy Applications (ECO-IDEA)

With support from the U.S. Department of Energy Solar Energy Technologies Office, the National Renewable Energy Laboratory (NREL) partnered with Xcel Energy, Schneider Electric, Varentec, and Electric Power Research Institute (EPRI) to meet the goals of the Enabling Extreme Real-Time Grid Integration of Solar Energy (ENERGISE) program. This project developed and validated an innovative data-enhanced hierarchical control architecture that enables the efficient, reliable, resilient, and secure operation of future distribution systems with a high penetration of distributed energy resources like solar energy. The architecture enables a hybrid control approach where a centralized control layer is complemented by distributed control algorithms for solar inverters and autonomous control of grid edge devices. It is fully interoperable and includes all the cybersecurity aspects necessary for reliable and secure system operation. The hybrid approach can seamlessly integrate multiple voltage-regulation technologies, both at central and grid-edge levels, which enables reliable and efficient system operation in the face of unpredictable conditions. The overarching goal of the Eco-Idea project is to develop, validate, and deploy a unique and innovative Data-Enhanced Hierarchical Control (DEHC) architecture that comprehensively addresses the formidable challenges associated with proliferation of high penetration of distributed PV such as reverse power flows, transients from variability of PV systems, feeder load balancing, and voltage stability. These issues are exposing the weaknesses of existing grid operations and controls - including, but not limited to, lack of grid situational awareness, heuristic and slow-acting control actions, latency of control for emergency situations, and points of failure in communications. The proposed architecture will comprehensively resolve the deficiencies of current operational settings - where monitoring and control solutions proposed across industry and academia may not be interoperable and may not coexist in the same system - and will enable an efficient, reliable, resilient, and secure operation of future distribution systems with penetration of solar energy well beyond current limits. The DEHC architecture was developed and validated rigorously through hardware-in-loop simulations in the laboratory environment and deployed on the field.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2012

Space Weathering Investigations Enabled by NASA's Virtual Heliophysical Observatories

Structural and chemical impact of the heliospheric space environment on exposed planetary surfaces and interplanetary dust grains may be generally defined as space weathering . In the inner solar system, from the asteroid belt inwards towards the Sun, the surface regolith structures of airless bodies are primarily determined by cumulative meteoritic impacts over billions of years, but the molecular composition to meters in depth can be substantially modified by irradiation effects. Plasma ions at eV to keV energies may both erode uppermost surfaces by sputtering, and implant or locally produce exogenic material, e.g. He-3 and H2O, while more energetic ions drive molecular change through electronic ionization. Galactic cosmic ray ions and more energetic solar ions can impact chemistry to meters in depth. High energy cosmic ray interactions produce showers of secondary particles and energetic photons that present hazards for robotic and human exploration missions but also enable detection of potentially useable resources such as water ice, oxygen, and many other elements. Surface sputtering also makes ejected elemental and molecular species accessible for in-situ compositional analysis by spacecraft with ion and neutral mass spectrometers. Modeling of relative impacts for these various space weathering processes requires knowledge of the incident species-resolved ion flux spectra at plasma to cosmic ray energies and as integrated over varying time scales. Although the main drivers for investigations of these processes come from NASA's planetary science and human exploration programs, the NASA heliophysics program provides the requisite data measurement and modeling resources to enable specification of the field & plasma and energetic particle irradiation environments for application to space weather and surface weathering investigations. The Virtual Heliospheric Observatory (VHO), Virtual Energetic Particle Observatory (VEPO), Lunar Solar Origins Exploration (LunaSOX), and Space Physics Data Facility (SPDF) services now provide a wide range of inner heliospheric spacecraft data that can be applied to space weathering of potential exploration destinations including the Moon, asteroids, and the moons of Mars, as well to radiation hazard assessment for the spacecraft and human explorers. For example, the new VEPO service for time-averaging of multi-source ion flux spectra enables the specification of composite flux spectra from a variety of ongoing and legacy missions for applications to surface interaction modeling. Apollo to Artemis data resources of LunaSOX enable specific space weathering investigations for the Moon, while VHO more generally covers the space field and plasma environments of the inner and outer solar system from the sunward-most perihelia of the twin Helios spacecraft to the ongoing heliosheath passages of the twin Voyagers. Composite multi-source spectra from VEPO can also be applied to the continuing compilation of accumulated 1-AU fluence spectra, mostly contributed by solar wind plasma and energetic particle events, for determination of time-averaged particle compositional and kinetic energy output from the Sun and for modeling of long-term irradiation impacts on planetary surfaces.

Cooper, John F.↗
DOE OSTI2021

Modular horizontal pendulum wave energy converter: Exploring feasibility to power ocean observation applications in the U.S. pacific northwest

Abstract Marine renewable energy as a power source for ocean observation applications has the potential to allow longer deployment operations due to the consistent, higher, and denser energy available from this resource. This additionally could encourage deployments in remote locations where maintenance is costly or resource availability is low if dependent on solar power. More importantly, gaps in spatial data could be filled. This paper examines the feasibility of a modular horizontal pendulum wave energy converter to power National Data Buoy Center's Self‐Contained Ocean Observations Payload (SCOOP) off the coast of Washington State, U.S. The effect on power output was studied when the pendulum's radius arm, mass, and power take‐off damping were varied. Results using Matlab toolbox WEC‐Sim revealed positive correlation between radius arm length and mass to power output, where power maximised for optimal damping values. Seasonal trends in power were not significant, where a 20 kg pendulum mass was needed to meet the SCOOP base power requirement of 5 W throughout the year.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

AI-based Detection and Defense Against Cyberattacks in Distributed Energy Resources

This study will provide comprehensive artificial intelligence (AI)-based solution tools for network security, malware prevention, and sensor data anomaly detection for distributed energy resource (DER) research, development, and demonstration. DER technologies are energy systems (e.g., solar panels, wind turbines, and energy storage systems) that are often connected to the internet and thus vulnerable to cyberattacks. Cybersecurity should be of primary concern for DERs, which is why we propose an integrated multi-layer cyber-defense system for DERs. This system encompasses risk assessments, network security, malware prevention, and detection of anomalies in the sensor data. Implementation of a comprehensive risk assessment with an overview of the model architecture should be the primary step, and should include the potential impact of experiencing, at a given time, one or more cyberattacks on the system. The second step is to ensure that the network security includes firewalls, intrusion detection, and malware prevention. The third step is to provide solution tools that enable sensor data anomaly detection for DERs. By incorporating these considerations into DER research, development, and demonstration, organizations can help ensure the safety and security of their systems and protect against potential cyberattacks.

20 FOSSIL-FUELED POWER PLANTS↗