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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 OSTI2024

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

Abstract The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA’s versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

SCALE Lattice Physics Code Assessments of Accident Tolerant Fuel

This report highlights accident-tolerant fuel (ATF) code assessment activities performed under Project NRCHQ- 60-17-T-0017, Lattice Physics Enhancements and Assessment. ATF covers a broad range of advanced fuel and clad designs for light water reactors (LWRs) to enhance performance under several accident conditions. Several ATF concepts are anticipated to be deployed as lead test rods (LTRs) or lead test assemblies (LTAs) within the next five years. The purpose of this work is to assess the predictive capabilities of NRC neutronics codes that underpin various licensing calculations. ATF designs use different fuel and clad materials compared to standard UO 2 fuel and zirconium-alloy claddings (hereafter UO 2 -Zry). These new materials and geometrical designs need to be assessed to quantify the impact of nuclear data uncertainties on quantities of interest (QOIs) in licensing calculations and the impact of modeling approximations which may be valid for UO 2 -Zry but not for ATF. This report outlines a systematic approach for ATF neutronics code assessment which includes sensitivity and uncertainty (S/U) analysis of nuclear data, identification of experimental benchmark and gaps for code validation, investigation of modeling approximations, and code-to-code comparisons of calculated QOIs against high-fidelity reference continuous energy (CE) Monte Carlo (MC) calculations. This report focuses on the assessment of the SCALE/Polaris lattice physics code for reactor safety analysis. Polaris lattice physics calculations generate few group (FG) cross sections for PARCS full-core calculations. (Full-core analysis, spent fuel analysis, and severe accident analysis will be performed in future work.) The selected ATF concepts for this report include Cr 2 O 3 and Al 2 O 3 -Cr 2 O 3 -doped UO 2 fuel, U 3 Si 2 fuel, FeCrAl cladding, SiC cladding, and Cr-coated cladding.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A global urban heat island intensity dataset: Generation, comparison, and analysis

The urban heat island (UHI) effect, a phenomenon of local warming over urban areas, is the most well-known impact of urbanization on climate. Globally consistent estimates of the UHI intensity (UHII) are crucial for examining this phenomenon across time and space. However, publicly available UHII datasets are limited and have several constraints: (1) they are for clear-sky surface UHII, not all-sky surface UHII and canopy (air temperature) UHII; (2) the estimation methods often neglect anthropogenic disturbance, introducing uncertainties in the estimated UHII. To address these issues, this study proposes a new dynamic equal-area (DEA) method that can minimize the influence of various confounding factors on UHII estimates through a dynamic cyclic process. Utilizing the DEA method and leveraging various gridded temperature data, we develop a global-scale (>10,000 cities), long-term (over 20 years by month), and multi-faceted (clear-sky surface, all-sky surface, and canopy) UHII dataset. Further, based on these estimates, we provide a comprehensive analysis of the UHII and its trends in global cities. The UHII is found to be greater than zero in >80% of cities, with global annual average magnitudes around 1.0 °C (day) and 0.8 °C (night) for surface UHII, and close to 0.5 °C for canopy UHII. Furthermore, an interannual upward trend in UHII is observed in >60% of cities, with global annual average trends exceeding 0.1 °C/decade (day) and over 0.06 °C/decade (night) for surface UHII, and slightly surpassing 0.03 °C/decade for canopy UHII. Notably, there exists a positive correlation between the magnitude and trend of UHII, suggesting that cities with stronger UHII tend to experience faster growth in UHII. Additionally, discrepancies in UHII are found between different temperature data, stemming not only from distinctions in data types (surface or air temperature) but also from differences in data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies (with or without gap filling). Overall, our proposed method, dataset, and analysis results have the potential to provide valuable insights for future urban climate studies. The UHII dataset is publicly available at https://doi.org/10.6084/m9.figshare.24821538.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reinterpreting π-stacking

The nature of π–π interactions has long been debated. The term “π-stacking” is considered by some to be a misnomer, in part because overlapping π-electron densities are thought to incur steric repulsion, and the physical origins of the widely-encountered “slip-stacked” motif have variously been attributed to either sterics or electrostatics, in competition with dispersion. Here, we use quantum-mechanical energy decomposition analysis to investigate π–π interactions in supramolecular complexes of polycyclic aromatic hydrocarbons, ranging in size up to realistic models of graphene, and for comparison we perform the same analysis on stacked complexes of polycyclic saturated hydrocarbons, which are cyclohexane-based analogues of graphane. Our results help to explain the short-range structure of liquid hydrocarbons that is inferred from neutron scattering, trends in melting-point data, the interlayer separation of graphene sheets, and finally band gaps and observation of molecular plasmons in graphene nanoribbons. Analysis of intermolecular forces demonstrates that aromatic π–π interactions constitute a unique and fundamentally quantum-mechanical form of non-bonded interaction. Not only do stacked π–π architectures enhance dispersion, but quadrupolar electrostatic interactions that may be repulsive at long range are rendered attractive at the intermolecular distances that characterize π-stacking, as a result of charge penetration effects. The planar geometries of aromatic sp 2 carbon networks lead to attractive interactions that are “served up on a molecular pizza peel”, and adoption of slip-stacked geometries minimizes steric (rather than electrostatic) repulsion. The slip-stacked motif therefore emerges not as a defect induced by electrostatic repulsion but rather as a natural outcome of a conformational landscape that is dominated by van der Waals interactions (dispersion plus Pauli repulsion), and is therefore fundamentally quantum-mechanical in its origins. This reinterpretation of the forces responsible for π-stacking has important implications for the manner in which non-bonded interactions are modeled using classical force fields, and for rationalizing the prevalence of the slip-stacked π–π motif in protein crystal structures.

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

Antiferromagnetic Ordering in Quasi-One-Dimensional FeBi 4 S 7

Here, we report a detailed study of the synthesis, composition, magnetic structure, and transport properties of a quasi-one-dimensional antiferromagnet FeBi 4 S 7 that contains chains of edge-sharing FeS 6 octahedra. High-resolution powder X-ray diffraction (PXRD) analysis, aided by variation of synthetic conditions, suggests that the true formula of the material is Fe 1.2 Bi 3.8 S 7 , due to the minor substitution of Fe into Bi sites. This finding is in agreement with crystal structure refinement from neutron powder diffraction data as well as with the small band gap of 0.23 eV determined from electrical transport measurements. Analysis of the neutron diffraction pattern collected below the antiferromagnetic ordering temperature of 64 K revealed ferromagnetic coupling between the Fe moments in the chains of FeS 6 octahedra. The overall ordering, however, is antiferromagnetic due to the antiparallel arrangement of moments on neighboring chains. The collinear spin arrangement is described by a k-vector (1, 0, 1/2), which indicates doubling of the unit cell in the c direction and the loss of the C-centering translation as compared to the nuclear cell. The ferromagnetic nature of the sulfide-bridged chains of Fe 2+ ions in FeBi 4 S 7 , in contrast to the antiferromagnetic coupling between Fe moments in compounds with similar structural fragments, can be justified by the analysis of metric parameters that characterize the Fe–S bonding in these materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Renormalization-group approach to Kohn-Luttinger superconductivity: Amplification of the pairing gap from ℓ 4 to ℓ

Here, we revisit the renormalization group (RG) analysis of the Kohn-Luttinger (KL) mechanism for superconductivity. The KL mechanism leads to superconductivity in a system with a repulsive bare interaction. The key ingredient is the screening effect that renders the induced interaction attractive in channels with nonzero angular momentum ℓ ≠ 0, thereby triggering the Bardeen-Cooper-Schrieffer (BCS) instability. According to the original argument, the resulting gap is exponentially small, with its exponent scaling as −ℓ 4 . However, the KL mechanism was originally formulated within perturbation theory, where the series is known to converge poorly in certain cases—most notably, for the 𝑝-wave paring gap induced by a repulsive 𝑠-wave contact interaction. This poor convergence may be attributed to a divergent integrand in a specific class of diagrams containing both the BCS logarithm and the Kohn anomaly, suggesting that one must resum the Kohn anomaly contributions separately from the BCS logarithm. In this work, we incorporate the Kohn anomaly contribution into the 𝛽 function of the RG equation governing the BCS instability near the Fermi surface. Our solution shows that the KL gap exponent is then proportional to −ℓ, indicating a significant enhancement of the KL mechanism beyond the previously known result. To illustrate this, we study the spin-triplet 𝑝-wave pairing gap arising from a repulsive 𝑠-wave contact interaction and compare our RG-based results with those obtained from the Bethe-Salpeter equation in perturbation theory.

nuclear matter in neutron stars↗
DOE OSTI2021

Resilience in an Age of Increasing Electrification

The purpose of this paper is to outline considerations of global electrification trends that include the resultant system-wide and nation-wide impacts. How we incorporate resilience into the electrification plans that transition a nation from conventional energy sources to secure clean energy sources can impact the success of those transitions. Electrification of energy systems has the potential to impact power system planning, emissions, and security; however, little research has been done on comprehensive solutions for incorporating resilience into the electrification process. Research has been conducted on aspects of the energy system and electrification, but not holistically across disciplines and related to the interdependencies of different components of the grid system. This gap in research provides an opportunity for more analysis and a collection of best practices to incorporate resilience into electrification plans. Although clean energy is the goal of an increasing number of nations worldwide, there are many considerations in designing clean energy transitions and increased electrification. Resilience must be factored into clean energy transitions from planning, market design, and policies to modernized grid structures and new, location-specific dispersed energy and energy storage technologies.

clean energy↗
DOE OSTI2021

Cubic AgMnSbTe 3 Semiconductor with a High Thermoelectric Performance

The reaction of MnTe with AgSbTe 2 in an equimolar ratio (ATMS) provides a new semiconductor, AgMnSbTe 3 . AgMnSbTe 3 crystallizes in an average rock-salt NaCl structure with Ag, Mn, and Sb cations statistically occupying the Na sites. AgMnSbTe 3 is a p-type semiconductor with a narrow band gap of ~0.33 eV. A pair distribution function analysis indicates that local distortions are associated with the location of the Ag atoms in the lattice. Density functional theory calculations suggest a specific electronic band structure with multi-peak valence band maxima prone to energy convergence. In addition, Ag 2 Te nanograins precipitate at grain boundaries of AgMnSbTe 3 . Here, the energy offset of the valance band edge between AgMnSbTe 3 and Ag 2 Te is ~0.05 eV, which implies that Ag 2 Te precipitates exhibit a negligible effect on the hole transmission. As a result, ATMS exhibits a high power factor of ~12.2 μWcm -1 K -2 at 823 K, ultralow lattice thermal conductivity of ~0.34 Wm -1 K -1 (823 K), high peak ZT of ~1.46 at 823 K, and high average ZT of ~0.87 in the temperature range of 400–823 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Best practices for in-situ and operando techniques within electrocatalytic systems

In-situ and operando techniques in heterogeneous electrocatalysis are a powerful tool used to elucidate reaction mechanisms. Ultimately, they are key in determining concrete links between a catalyst’s physical/electronic structure and its activity en route to designing next-generation systems. To this end, the exact execution and interpretation of these lines of experiments is critical as this determines the strength of conclusions that can be drawn and what uncertainties remain. Instead of focusing on how techniques were used to understand systems, as is the case with most reviews on the topic, this work instead initiates a nuanced discussion of 1) how to best carry out each technique and 2) initiate a nuanced analysis of which level of insights can be drawn from the set of in-situ or operando experiments/controls carried out. We focus on several commonly used techniques, including vibrational (IR, Raman) spectroscopy, X-ray absorption spectroscopy and electrochemical mass spectrometry. In addition to this, we include sections of reactor design and the link with theoretical modelling that are applicable across all techniques. While we focus on heterogeneous electrocatalysis, we make links when appropriate to the areas of photo- and thermo-catalytic systems. We highlight common pitfalls in the field, how to avoid them, and what sets of complementary experiments may be used to strengthen the analysis. We end with an overview of what gaps remain in in-situ and operando techniques and what innovations must be made to overcome them.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

MultiTaskDeltaNet: change detection-based image segmentation for operando ETEM with application to carbon gasification kinetics

Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.

08 HYDROGEN↗
DOE OSTI2026

Morphological descriptors of nanoparticles: The link between atomistic structures and x-ray absorption spectra

Understanding and quantifying the morphology of nanoparticles are essential for linking their atomic structure to diverse applications and verifying theoretical models. While experimental information on the structure of nanoparticles in the size range below ∼5 nm can be extracted from x-ray absorption spectroscopy using a small number of descriptors—most commonly coordination numbers—developing an understanding of morphology descriptors from experimental data remains a challenge. Here, in this study, we introduce NanoGene, a genetic algorithm-based method for generating structurally diverse nanoparticle models guided by user-defined descriptors. We establish correlations among structural, size-related, and morphological descriptors and demonstrate how experimentally accessible parameters, such as coordination numbers, can be leveraged to infer otherwise inaccessible ones, such as the generalized coordination number or particle oblateness. Principal component and clustering analyses reveal the relative importance of descriptors, with the number of atoms emerging as a key discriminant of the nanoparticle structure. By providing both the methodology and an extensive dataset of nanoparticle geometries, this work offers a practical foundation for descriptor-based analysis and interpretation of experimental observations, bridging the gap between local atomic coordinates and global morphological characterization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

A temporal hierarchy underpins the transcription factor–DNA interactome of the maize UPR

Adverse environmental conditions reduce crop productivity and often increase the load of unfolded or misfolded proteins in the endoplasmic reticulum (ER). This potentially lethal condition, known as ER stress, is buffered by the unfolded protein response (UPR), a set of signaling pathways designed to either recover ER functionality or ignite programmed cell death. Despite the biological significance of the UPR to the life of the organism, the regulatory transcriptional landscape underpinning ER stress management is largely unmapped, especially in crops. To fill this significant knowledge gap, we performed a large–scale systems–level analysis of the protein–DNA interaction (PDI) network in maize (Zea mays). Using 23 promoter fragments of six UPR marker genes in a high–throughput enhanced yeast one–hybrid assay, we identified a highly interconnected network of 262 transcription factors (TFs) associated with significant biological traits and 831 PDIs underlying the UPR. We established a temporal hierarchy of TF binding to gene promoters within the same family as well as across different families of TFs. Cistrome analysis revealed the dynamic activities of a variety of cis–regulatory elements (CREs) in ER stress–responsive gene promoters. By integrating the cistrome results into a TF network analysis, we mapped a subnetwork of TFs associated with a CRE that may contribute to UPR management. Lastly, we validated the role of a predicted network hub gene using the Arabidopsis system. The PDIs, TF networks, and CREs identified in our work are foundational resources for understanding transcription–regulatory mechanisms in the stress responses and crop improvement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data

Today's scientific simulations and instruments are producing a large amount of data, leading to difficulties in storing, transmitting, and analyzing these data. While error-controlled lossy compressors are effective in significantly reducing data volumes and efficiently developing databases for multiple scientific applications, they mainly support error controls on raw data, which leaves a significant gap between the data and user's downstream analysis. This may cause unqualified uncertainties in the outcomes of the analysis, a.k.a quantities of interest (QoIs), which are the major concerns of users in adopting lossy compression in practice. In this paper, we propose rigorous mathematical theories to preserve four families of QoIs that are widely used in scientific analysis during lossy compression along with practical implementations. Specifically, we first develop the error control theory for univariate QoIs which are essential for computing physical properties such as kinetic energy, followed by multivariate QoIs that are more commonly used in real-world applications. The proposed method is integrated into a state-of-the-art compression framework in a modular fashion, which could easily adapt to new QoIs and new compression algorithms. Experiments on real-world datasets demonstrate that the proposed method provides faithful error control on important QoIs including kinetic energy, regional average, and isosurface without trials and errors, while offering compression ratios that are up to 4x of the compression ratios provided by state-of-the-art compressors.

Jiao, Pu↗
DOE OSTI2021

Acquire and Test IDAES Framework Components

The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

System Integration of Rationally Designed Dilute Alloy Catalysts for Energy-Efficient Electrochemical CO 2 -To-Fuel Conversion

Energy effiecient electrochemical conversion of CO 2 to high-demand chemicals and transportation fuels using renewable solar and wind energy is a key technology needed for a high-productivity, low-carbon future. However, the development of scalable, low-cost, active, selective, and stable electrocatalysts remains a key challenge that needs to be overcome to enable high-volume conversion of CO 2 to feedstock chemicals for the chemical industry. In previous work, Lawrence Livermore National Laboratory (LLNL) has developed a rational design platform for dilute alloy transition metal electrocatalysts that promise to make electrochemical CO 2 conversion more energy efficient and selective. In this project, we worked with our industrial partners, Twelve and TotalEnergies, to improve scale up, integration, and stability of LLNL’s dilute alloy catalyst technology into an industry-relevant zero gap electrolyzer platform. Through virtual experiments and data analysis, we designed efficient and cost-effective copper-based catalysts. The catalyst was specifically designed to streamline the slowest and most energy-intensive step of the electrochemical chemical transformation of CO 2 to multi-carbon products – that is making the carbon-carbon bond by dimerization of the reaction intermediate carbon monoxide - resulting in up to 10% improvement in energy efficiency for C 2 products while simultaneously increasing the selectivity towards C 2 products. We tested two different scalable catalyst coating technologies and down-selected magnetron sputtering as the technique that provided the best control over catalyst loading, composition, and morphology. Using this technology, we successfully demonstrated integration of our dilute alloy catalysts into a 100 cm 2 electrolyzer platform with Faradaic efficiencies for ethylene production reaching 40% at a current density of 200 mA/cm 2 . We also developed the technology to integrate a well-defined nanoscale porosity by depositing alloy compositions that were compatible with dealloying, that is, selective leaching of an alloy component to generate nanoscale porosity. We observed that integration of the dealloying-derived nanoporosity improved catalyst performance and stability by leading to a more hydrophobic catalyst/anion exchange membrane interface. Unsolved problems that still need to be addressed are corrosion of the Cu catalyst -specifically if the used catalyst is exposed to air - as well as long term stability do to salt formation/deposition and flooding of the catalyst/electrolyzer flow channels, especially at higher current densities. As we only worked on optimization of catalyst composition, coating thickness, and morphology, further performance optimization will require a system level approach that includes optimization of electrolyzer design and membrane technology.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Queued Up: 2025 Edition – Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2024 [Slides]

Electric transmission system operators (ISOs, RTOs, or utilities) require proposed power plants seeking to connect to the transmission grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. The lists of projects in this process are known as “interconnection queues”. In collaboration with interconnection.fyi, Berkeley Lab compiled, aggregated, and cleaned interconnection queue data from >50 transmission grid operators (7 ISO/RTOs and 49 non-ISO balancing areas), which collectively represent ~97% of currently installed U.S. electric generating capacity. The dataset includes requests submitted to queues through the end of 2024, and only includes requests seeking to connect to the transmission grid (not distribution-connected or behind-the-meter projects). The files below include both a PDF report and an Excel data file. The PDF report analyzes interconnection data and metrics through the end of 2024. The Excel data file includes (a) the full project-level interconnection queue dataset through 2024, (b) a codebook (data dictionary) describing each data field, and (c) 35 additional tabs featuring tables summarizing a range of interconnection metrics. Key highlights from the Queued Up: 2025 Edition (featuring data through 2024) include: • As of the end of 2024, there were ~10,300 projects actively seeking grid interconnection in the U.S., representing 1,400 GW of generation and approximately 890 GW of storage. • Historic withdrawal rates alongside relatively fewer new requests resulted in a 12% decrease in total active queue volume compared to the prior year. • Active natural gas capacity (136 GW, +72% year-over-year) increased in 2024, while solar (956 GW, -12%), storage (890 GW, -13%), and wind (271 GW, -26%) capacity decreased. • 408 GW of capacity already has a draft or executed interconnection agreement (IA) but has not yet reached commercial operations. • The time projects spend in queues before reaching COD is increasing. For the regions with available data, the median duration from IR to COD has doubled from <2 years for projects built in 2000-2007 to over 4 years for those built in 2018-2024. • Ultimately, most of this proposed capacity will not be built. Only 13% of capacity that submitted interconnection requests from 2000-2019 had reached commercial operations by the end of 2024; 77% of that capacity had been withdrawn and 10% was still active. • FERC Order 2023 and various other reforms are being implemented. These are important measures to reduce interconnection bottlenecks and enhance grid system reliability, but it is too early to measure and assess their full impact. • New additions for the 2025 edition include: (a) additional detail on data processing and gaps; (b) updates on interconnection reforms; (c) new analysis on interconnection agreements, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Commercial Building Sensors and Controls Systems: Barriers and Drivers: Preprint

Building sensors and controls systems, including building automation systems, comprise the sensor-based devices installed in buildings as well as the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 13% of small commercial buildings, however, have installed sensors and controls systems, largely because of cost barriers. To accelerate adoption, this work seeks to increase the transparency of system costs and identify specific barriers and drivers. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 21 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. We collected the cost data in the form of invoices and used it to develop a percentage-based cost category breakdown. The interview data were analyzed using grounded theory to identify overarching concepts such as barriers, drivers, and future directions. From this analysis, we found the primary barriers to be complex and confusing systems, lack of user skills, and financial concerns, and the primary drivers to be operational benefits, insight into operations, and remote access to data. The future directions analysis highlighted the potential technological solutions to address gaps and barriers, as well as predicted drivers to increase adoption. This greater understanding of the costs, barriers, and drivers associated with commercial building sensors and controls systems lays the groundwork for increasing system adoption, reducing energy consumption, and transforming the market.

building automation system↗
DOE OSTI2023

Enhancing Resilience of our Nation’s Critical Infrastructure

Due to the ever-changing risk environment that faces the Nation’s critical infrastructure, it is essential that a comprehensive, collaborative approach is taken to enhance the resilience of the infrastructure assets and systems that are relied upon by the American people. To address the gaps that exist in infrastructure resilience research and development, Idaho National Laboratory (INL) created the Resilience Optimization Center (IROC) to bring together multi-disciplinary subject matter experts internally across the laboratory, as well as from public and private entities, other national laboratories, and academia to address some of the Nation’s more challenging infrastructure problems. These experts are working to provide easier access to subject matter experts; providing feasible, optimized solutions that yield observable results; and creating collaborative teams that apply a cyber-physical-dependencies approach. Though a variety of research initiatives, IROC is striving to provide end-to-end solutions, bridging the gap between cyber and physical infrastructure through research, analysis, testing, and validation.

99 GENERAL AND MISCELLANEOUS↗