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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Real Time Phasor Analytics (RTPA) and RTPA-SCR System Strength Online Tool

This presentation showcases the Real-Time Phasor Analytics (RTPA) framework for monitoring inertia and assessing system strength in power grids. RTPA is an open-source tool designed to standardize access to data from Power Management Units (PMUs) and Phasor Data Concentrators (PDCs). It facilitates real-time connectivity to multiple PDCs in accordance with the IEEE C37.118-2 standard and supports asynchronous data stream integration. Additionally, RTPA can simulate a PDC server streaming C37.118-2 data and provides Python bindings for seamless interaction with the framework, eliminating the need for direct Rust programming.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Forecasting distributed energy resources adoption for power systems

Failing to incorporate accurate distributed energy resource penetration forecasts into long-term resource and transmission planning can lead to cost inefficiencies at best and system failures at worst. We have developed an open-source tool that employs an advanced Bass specification to calibrate and forecast technology adoption. The advanced specification includes geographic clustering, exogenously estimated market size, and dynamic time steps. Training on historical adoption of rooftop photovoltaics at the U.S. county-level and using detailed techno-economic estimates, our model achieves a two-year average mean-absolute-percentage-error of 19% in predicting system counts at the county-level, weighted by population. Model error was negatively correlated with market maturity - the error was 12% for counties in states with at least 28 W-per-capita of installed capacity. The advanced specification significantly reduces unweighted forecasting percent error compared to a conventional Bass specification: from 196% to 25% for capacity and from 226% to 22% for system count.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Dark matter and galaxy cross-correlations with the Cherenkov Telescope Array Observatory

The Cherenkov Telescope Array Observatory (CTAO) will be a ground-based Cherenkov telescope performing wide-sky surveys, ideal for anisotropy studies such as cross-correlations with tracers of the cosmic large-scale structure. Cross-correlations can shed light on high-energy γ -ray sources and potentially reveal exotic signals from particle dark matter. In this work, we investigate CTAO sensitivity to cross-correlation signals between γ -ray emission and galaxy distributions. We find that by using dense, low-redshift catalogs like 2 MASS, and for integration times around 50 hours, this technique achieves sensitivities to both annihilating and decaying dark matter signals that are competitive with those from dwarf galaxy and cluster analyses.

Pinetti, Elena [CCA, New York; Perimeter Inst. The↗
DOE OSTI2025

Scatter and Blur Corrections for High-Energy X-Ray Radiography

High-energy X-ray radiography is useful as a highly penetrating method for imaging through dense materials. However, the primary modes of interaction of X-rays at these energies involve scattering or the production of secondary high-energy photons, which can interfere with the image. In addition, detector blurring, often resulting from scatter within the detector, can reduce image sharpness. Both of these processes can be mitigated with the use of convolution kernels, with the main challenge being that the proper kernel to use is not known, particularly for the scatter contribution. By radiographing solid slabs of uniform attenuation, we show that point spread functions and material-specific point scatter functions can be determined to significantly reduce the effect of detector blurring and object scatter. Constraining the fits to the slabs and uniform transmission within the slabs is sufficient to recover these functions. A functional form that reproduces the angular distribution of high-energy bremsstrahlung X-rays is presented for recovering point scatter functions. In conclusion, the method is applied to radiographs of objects from bremsstrahlung X-ray sources operating at 4- and 7.5-MV endpoint energies and a significant increase in sharpness is observed.

Blind deconvolution↗
DOE OSTI2023

Power System Recovery from Momentary Cessation with Transient Stability Improvement

Power system dynamics will be significantly changed by integrating wind farms and solar photovoltaic plants into power systems. This study investigates the effect of momentary cessation of inverter-based resources (IBRs) on transient stability and provides a recovery strategy for bulk IBRs in power systems. The theoretical analysis was initially carried out on a one-machine infinite-bus system, demonstrating the IBR impact in a critical group. The analysis was then expanded to a multimachine power system with IBRs using the single-machine equivalent method. The study found that IBRs in critical and noncritical groups exert contrasting effects on transient stability. Finally, a strategy for enhancing transient stability is proposed by controlling IBRs during power system recovery. The proposed strategy was verified by simulation on IEEE 9-bus and IEEE 39-bus power systems with the addition of IBRs. A 39-bus power system simulation demonstrates the scalability of the proposed method. Here, the proposed strategy provides effective and executable measures for improving system security in the presence of IBRs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Hybrid Tandem Photovoltaics

Tandem solar cell structures are the only strategy demonstrated to surpass the detailed balance efficiency limit of high-quality single-junction solar cells. To continue to improve the efficiencies of cost-effective terrestrial solar power, hybrid tandems of dissimilar subcells are being considered by many around the world, especially designs that incorporate silicon solar cells as a bottom subcell. In this project, we studied a wide variety of tandem design possibilities including those with three-terminal (3T) and four-terminal (4T) configurations. The use of 3T and 4T designs could be useful for efficient and economical hybrid tandem designs that utilize the best available subcell materials such as emerging perovskite materials. Three-terminal configurations, in particular, have not been sufficiently studied previously. We have laid the foundational groundwork in this project for understanding the operation of 3T tandems: developing a taxonomy for naming, a methodology for measuring and interconnecting, and models for simply characterizing 3T tandems. Electrical and optical subcell coupling between the subcells was also measured and modeled. An important part of this work was the fabrication of novel example tandem structures, including 4T GaAs/Si, 3T GaInP/Si, 3T GaAs/Si, and 3T GaInP/GaAs devices. Using these high-quality tandem cells, we have been able to clearly demonstrate the achievability of high-efficiencies, and subtle physical effects such as photon recycling and luminescent coupling. We have developed and demonstrated essential building-block tools such as transparent conductive adhesives (TCA) and 3T silicon bottom cells with interdigitated back contacts (IBC) that can also be used in many other tandem designs. We have tested the reliability of these tools and devices under standardized testing and outdoor measurements. We have found 4T GaAs/Si tandems to be relatively straightforward to fabricate and robust in real-world outdoor conditions. While we have demonstrated working hybrid 3T III-V/TCA/Si IBC tandems, we experienced low yields even with our best process flows yet. Further work is still needed to improve the processing yield of these devices. We therefore also created tandem cells using an all-III-V 3T tandem process which was very robust with high yields, allowing for the creation of voltage-matched strings in many different configurations using 8 nearly identical 3T tandems. Using these robust 3T tandem examples, we were able measure and precisely characterize 3T tandem behaviors to predict their operation under changing spectrum and temperature. The optoelectronic equivalent-circuit model was shown to be very general and applicable to hybrid tandems, and encompassed the operation 3T Si IBC cells. This general model has been distributed to the public in as open-source Python-based software called PVcircuit. We have calculated the implications of these new tandem device designs on the real-world energy production and shown how the relative performance of different tandem configurations is situational and can be engineered using the tools developed here.

14 SOLAR ENERGY↗
DOE OSTI2024

REopt Model Overview and Example Use Cases [Slides]

REopt(R) is a mixed-integer optimization model that minimizes the lifecycle cost of serving energy loads at a site. This work provides and introduction to the model along with its key workflow, techno-economic inputs, key outputs, and key caveats for readers to understand REopt the when, why, how of using this model. This resource also includes helpful links related to REopt model and the data sources it uses during the optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

REopt Lite Web Tool: Capabilities and Features

REopt Lite transforms complex energy project decisions into actionable results for building owners, utilities, and industry. Based on decades of NREL decision-support expertise, the free, publicly available REopt Lite web tool guides investment in economic, resilient energy technologies. And open source and API access to the tool enables energy analysis at scale. This presentation provides an overview of how REopt Lite can help users optimize the economic and resilience benefits of distributed energy resources.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2022

Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model

The residential buildings sector is one of the largest electricity consumers worldwide and contributes disproportionally to peak electricity demand in many regions. Strongly driven by occupant activities, household energy consumption is stochastic and heterogeneous in nature. However, most residential energy models applied by industry use homogeneous, deterministic activity schedules, which work well for predictions of annual energy consumption, but can result in unrealistic hourly or sub-hourly electric load profiles, with exaggerated or muted peaks. The increasing proportion of variable renewable energy generators means that representing the heterogeneity and stochasticity of occupant behavior is now crucial for reliable planning at both bulk-power and distribution-system scales. This work presents a novel and open-source occupancy simulation approach that can simulate a diverse set of individual occupant and household event schedules for all major electricity, fuel, and hot water end uses. To accomplish this, we evaluated three alternative occupant activity simulation approaches before selecting a hybrid combining time-inhomogeneous Markov chains and probability-sampling of event durations and magnitudes. Further, we integrated the stochastic occupancy simulation with an open-source bottom-up physics-simulation building stock model and published a set of 550,000 diverse household end-use activity schedules representing a national housing stock. The simulator was verified against time-use survey data, and simulation results were validated against measured end-use electricity data for accuracy and reliability. While we use data for the United States, our application demonstrates how similar approaches could be applied using the time-use survey data collected in many countries around the world.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Detection of voids in hydrogen embrittled iron using transmission X-ray microscopy

Hydrogen embrittlement remains a barrier to widespread adoption of hydrogen as a carbon-neutral energy source. Here, hydrogen embrittlement mechanisms are investigated across length scales in iron using transmission X-ray microscopy (TXM), digital image correlation (DIC), and notched tensile testing during in-situ electrochemical hydrogen charging. TXM reveals void size and spatial distribution ahead of a propagating crack. We find hydrogen charging leads to voids within ~10 μm of the crack tip and suppression of voids beyond this distance. Near the crack tip, voids are elongated in the direction of the crack and are smaller than voids in an uncharged sample. In the presence of hydrogen, these voids lead to quasi-cleavage fracture and a sharper crack tip. DIC shows localization and reduction of plastic strain with hydrogen charging, and tensile testing reveals a reduction in fracture energy and elongation at failure. Finally, these results are discussed in the context of hydrogen embrittlement mechanisms.

08 HYDROGEN↗
DOE OSTI2022

Corrosion proxy material integrated sensor devices for distributed sensing of early corrosion onset and corrosion quantification

Embodiments relate to methods, systems and apparatus for detecting corrosion using a detector apparatus with a host component. The method includes a detector apparatus. The detector apparatus includes an energy source; a corrosion proxy thin film that corrodes at a rate where the normalized change of mass of the film over time is greater than the normalized change of mass of the bulk material of the host component in a harsh environment; and the detector, wherein the detector is capable of detecting a change in energy due to corrosion of the corrosion proxy thin film. The method includes exposing the host component and the corrosion proxy thin film to the harsh environment; and detecting a change in energy using the detector due to corrosion of the corrosion proxy thin film.

Ohodnicki, Jr., Paul R.↗
DOE OSTI2022

Commercial, industrial, and institutional discount rate estimation for efficiency standards analysis: Sector-level data 1998–2021

Underlying each of the Department of Energy’s (DOE’s) federal appliance and equipment energy conservation standards are a set of complex analyses of the projected costs and benefits of regulation. Any new or amended standard must be designed to achieve significant additional energy conservation, provided that it is technologically feasible and economically justified (42 U.S.C. 6295(o)(2)(A)). DOE determines economic justification based on whether the benefits exceed the burdens, considering a variety of factors, including the economic impact of the standard on consumers of the product and the savings in lifetime operating cost compared to any increase in price or maintenance expenses (42 U.S.C. 6295(o)(2)(B)). As part of this determination, DOE conducts a Life-Cycle Cost (LCC) analysis, which models the combined impact of appliance first cost and operating cost changes on a representative commercial building sample in order to identify the fraction of customers achieving LCC savings or incurring net cost at the considered efficiency levels. Thus, the commercial discount rate value(s) used to calculate the present value of energy cost savings within the LCC model implicitly plays a role in estimating the economic impact of potential standard levels. This report provides an in-depth discussion of the commercial discount rate estimation process. It is an update to previous reports on estimating commercial discount rates from firm-level financial data (Fujita, 2016). Major topics covered in this report include: Discount rate estimation methods and rationale; -Data sources used and data limitations; -Discount rate distributions for use in standards analysis; -Discount rate estimation methods and distributions specific to the small business subgroup analysis. Going forward, this report will be updated as data allow and analyses necessitate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

HETDEX Public Source Catalog 1: 220 K Sources Including Over 50 K Ly$α$ Emitters from an Untargeted Wide-area Spectroscopic Survey*

We present the first publicly released catalog of sources obtained from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). HETDEX is an integral field spectroscopic survey designed to measure the Hubble expansion parameter and angular diameter distance at 1.88 < z < 3.52 by using the spatial distribution of more than a million Lyα-emitting galaxies over a total target area of 540 deg 2 . The catalog comes from contiguous fiber spectra coverage of 25 deg 2 of sky from 2017 January through 2020 June, where object detection is performed through two complementary detection methods: one designed to search for line emission and the other a search for continuum emission. The HETDEX public release catalog is dominated by emission-line galaxies and includes 51,863 Ly$α$-emitting galaxy (LAE) identifications and 123,891 [O ii]-emitting galaxies at z < 0.5. Also included in the catalog are 37,916 stars, 5274 low-redshift (z < 0.5) galaxies without emission lines, and 4976 active galactic nuclei. The catalog provides sky coordinates, redshifts, line identifications, classification information, line fluxes, [O ii] and Ly$α$ line luminosities where applicable, and spectra for all identified sources processed by the HETDEX detection pipeline. Extensive testing demonstrates that HETDEX redshifts agree to within Δz < 0.02, 96.1% of the time to those in external spectroscopic catalogs. We measure the photometric counterpart fraction in deep ancillary Hyper Suprime-Cam imaging and find that only 55.5% of the LAE sample has an r-band continuum counterpart down to a limiting magnitude of r ~ 26.2 mag (AB) indicating that an LAE search of similar sensitivity to HETDEX with photometric preselection would miss nearly half of the HETDEX LAE catalog sample. Data access and details about the catalog can be found online at http://hetdex.org/. A copy of the catalogs presented in this work (Version 3.2) is available to download at Zenodo doi:10.5281/zenodo.7448504.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Control of a Three-Phase Grid-Connected Voltage-Sourced Converter Using Long Short-Term Memory Networks

With the rise of inverter-based resources (IBRs) within the power system, the control of grid-connected converters (GCCs) has become pertinent due to the fact they interface IBRs to the grid. The conventional method of control for a GCC such as the voltage-sourced converter (VSC) is through a decoupled control loop in the synchronous reference frame. However, this model-based control method is sensitive to parameter changes causing deterioration in controller performance. Data-driven approaches such as machine learning can be utilized to design controllers that are capable of operating GCCs in various system conditions. This work explores a deep learning-based control method for a three-phase grid-connected VSC, specifically utilizing a long short-term memory (LSTM) network for robust control. Simulations of a conventional controlled VSC are conducted using Simulink to collect data for training the LSTM-based controller. The LSTM model is built and trained using the Keras and TensorFlow libraries in Python and tested in Simulink. The performance of the LSTM-based controller is evaluated under different case studies and compared to the conventional method of control. Simulation results demonstrate the effectiveness of this approach by outperforming the conventional controller and maintaining stability under different system parameter changes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Quantifying Quantum Chaos through Microcanonical Distributions of Entanglement

A characteristic feature of “quantum chaotic” systems is that their eigenspectra and eigenstates display universal statistical properties described by random matrix theory (RMT). However, eigenstates of local systems also encode structure beyond RMT. To capture this feature, we introduce a framework that allows us to compare the properties of eigenstates in local systems with those of pure random states. In particular, our framework defines a notion of distance between quantum state ensembles that utilizes the Kullback-Leibler divergence to compare the microcanonical distribution of entanglement entropy (EE) of eigenstates with a reference RMT distribution generated by pure random states (with appropriate constraints). This notion gives rise to a quantitative metric for quantum chaos that not only accounts for averages of the distributions but also higher moments. The differences in moments are compared on a highly resolved scale set by the standard deviation of the RMT distribution, which is exponentially small in system size. As a result, the metric can distinguish between chaotic and integrable behaviors and, in addition, quantify and compare the of chaos (in terms of proximity to RMT behavior) between two systems that are assumed to be chaotic. We implement our framework in local, minimally structured, Floquet random circuits, as well as a canonical family of many-body Hamiltonians, the mixed-field Ising model (MFIM). Importantly, for Hamiltonian systems, we find that the reference random distribution must be appropriately constrained to incorporate the effect of energy conservation in order to describe the ensemble properties of midspectrum eigenstates. The metric captures deviations from RMT across all models and parameters, including those that have been previously identified as strongly chaotic, and for which other diagnostics of chaos such as level spacing statistics look strongly thermal. In Floquet circuits, the dominant source of deviations is the second moment of the distribution, and this persists for all system sizes. For the MFIM, we find significant variation of the KL divergence in parameter space. Notably, we find a small region where deviations from RMT are minimized, suggesting that “maximally chaotic” Hamiltonians may exist in fine-tuned pockets of parameter space. Published by the American Physical Society 2024

Physics↗