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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Local electronic structure of histidine in aqueous solution

Here, the local electronic structure of aqueous histidine, an amino acid important in nature and biology, is revealed by aerosol X-ray photoemission spectroscopy. A detailed picture of the photoionization dynamics emerges by tuning the pH of the aqueous solution from which the aerosols are generated, allowing us to report the X-ray photoelectron spectroscopy (XPS) of histidine. Assignment of the experimental photoelectron spectra of the C1s and N1s levels allows the determination of the protonation state of histidine in these aqueous aerosols and is confirmed by density functional calculations. XPS spectra show that at pH = 1, both imidazole and amine group nitrogens are protonated, at pH = 7, the amine group nitrogen is protonated and the carboxyl group carbon is deprotonated resulting in a zwitterionic structure, and at pH = 13, only the carboxyl group remains deprotonated. Comparison of these results with previous experimental and theoretical results suggests that X-ray spectroscopy on aqueous aerosols can provide a convenient and simple way of probing their electronic structure in aqueous solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Intrinsic orbital moment and prediction of a large orbital Hall effect in two-dimensional transition metal dichalcogenides

Carrying information using generation and detection of the orbital current, instead of the spin current, is an emerging field of research, where the orbital Hall effect (OHE) is an important ingredient. We show that the 2D transition metal dichalcogenides (TMDCs) such as MoX2, a currently popular class of materials of considerable interest, may in fact be excellent systems to observe the OHE. This is due to the broken inversion symmetry and the specific nature of the electron states at the K, K$^\prime$ valley points, that lead to robust momentum-dependent orbital moments, which flow in different directions due to the opposite Berry curvatures under an applied electric field. This is in complete contrast to the inversion-symmetric systems studied so far, where the (weak) orbital moment is induced only after the application of the external electric field. In addition, the spin-orbit coupling leads to the spin Hall effect, which we find to be quite small in the TMDCs, making these materials particularly suitable for direct observation of the OHE, with potential application in orbitronics.

2D materials↗
DOE OSTI2021

Biological mechanisms may contribute to soil carbon saturation patterns

Increasing soil organic carbon (SOC) storage is a key strategy to mitigate rising atmospheric CO 2 , yet SOC pools often appear to saturate, or increase at a declining rate, as carbon (C) inputs increase. Soil C saturation is commonly hypothesized to result from the finite amount of reactive mineral surface area available for retaining SOC, and is accordingly represented in SOC models as a physicochemically determined SOC upper limit. However, mineral-associated SOC is largely microbially generated. In this perspective, we present the hypothesis that apparent SOC saturation patterns could emerge as a result of ecological constraints on microbial biomass—for example, via competition or predation—leading to reduced C flow through microbes and a reduced rate of mineral-associated SOC formation as soil C inputs increase. Microbially explicit SOC models offer an opportunity to explore this hypothesis, yet most of these models predict linear microbial biomass increases with C inputs and insensitivity of SOC to input rates. Synthesis of 54 C addition studies revealed constraints on microbial biomass as C inputs increase. Different hypotheses limiting microbial density were embedded in a three-pool SOC model without explicit limits on mineral surface area. As inputs increased, the model demonstrated either no change, linear, or apparently saturating increases in mineral-associated and particulate SOC pools. Taken together, our results suggest that microbial constraints are common and could lead to reduced mineral-associated SOC formation as input rates increase. We conclude that SOC responses to altered C inputs—or any environmental change—are influenced by the ecological factors that limit microbial populations, allowing for a wider range of potential SOC responses to stimuli. Understanding how biotic versus abiotic factors contribute to these patterns will better enable us to predict and manage soil C dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Emergence of a nocturnal low-level jet from a broad baroclinic zone

An analytical model is presented for the generation of a Blackadar-like nocturnal low-level jet in a broad baroclinic zone. The flow is forced from below (flat ground) by a surface buoyancy gradient and from above (free atmosphere) by a constant pressure gradient force. Diurnally-varying mixing coefficients are specified to increase abruptly at sunrise and decrease abruptly at sunset. With attention restricted to a surface buoyancy that varies linearly with a horizontal coordinate, the Boussinesq-approximated equations of motion, thermal energy, and mass conservation reduce to a system of one-dimensional equations that can be solved analytically. Sensitivity tests with southerly jets suggest that (i) stronger jets are associated with larger decreases of the eddy viscosity at sunset (as in Blackadar theory), (ii) the nighttime surface buoyancy gradient has little impact on jet strength, and (iii) for pure baroclinic forcing (no free-atmosphere geostrophic wind), the nighttime eddy diffusivity has little impact on jet strength, but the daytime eddy diffusivity is very important and has a larger impact than the daytime eddy viscosity. The model was applied to a jet that developed in fair weather conditions over the Great Plains from southern Texas to northern South Dakota on 1 May 2020. The ECMWF Reanalysis v5 (ERA5) for the afternoon prior to jet formation showed that a broad north-south-oriented baroclinic zone covered much of the region. The peak model-predicted winds were in good agreement with ERA5 winds and lidar data from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) central facility in north-central Oklahoma.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integrating static PRA information with risk informed safety margin characterization (RISMC) simulation methods

The overall objective of the project was to develop a computationally feasible and user-friendly mechanized process to integrate traditional probabilistic risk assessment (PRA) and dynamic PRA (DPRA) results. Starting with the systematic identification of items in an existing PRA that need dynamic augmentation, the project used a generic 4-loop pressurized reactor (PWR) and 3-loop PWR as example plants. Station blackout (SBO) and large break loss of coolant accident SBLOCA) were selected as the example initiating events. Using the traditional event-tree (ET)/fault-tree (FT) methodology augmented by dynamic evet tree approach, the potential consequences of the initiating events were simulated with RELAP-3D and MELCOR/RASCAL codes to cover Level 1 through Level 3 of PRA. RAVEN and ADAPT software were used to generate Level 1 simulations with RELAP-3D and Level 2/3 simulations with MELCOR (Level 2)/RASCAL (Level 3), respectively. Example branching conditions (BCs) for SBO included AC power recovery time, valve repair failure time, reactor coolant pump leak time/break size and emergency power supply duration to a total of 9. Example BCs for LOCA included off-site power recovery time, diesel generator power recovery time, auxiliary feed water system operation time, safety relief valve failure to open upon demand, reactor coolant pump seal break time and size to a total of 21. Each RELAP-3D simulation (9,587 scenarios) was labelled OK or Core Damage based on the maximum allowed peak clad temperature (2,100oF). Each MELCOR simulation (4610 scenarios) was labeled as Bin over 10rem or Bin 0-10rem based on the dose at the site boundary. The scenarios were clustered based on the criteria above using the mean shift methodology. Classical PRA (CPRA) and DPRA results were compared to identify the ET sequences that need DPRA augmentation. Several approaches were proposed for the incorporation of these sequences into CPRA using clustering with the mean shift methodology, restructuring the CPRA ETs by adding new BCs/sequences, and using the concept of a limit surface. Procedures for decision making regarding the possible consequences of an initiating event (e.g. core damage or not, site evacuation or not) were developed using a convolutional neural network (CNN), a recurrent neural network (RNN) and a transformer neural network (TNN). The project has led to two PhD degrees, three archival journal papers and five refereed conference proceedings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Carbon-Based Quantum Information Science with Symmetry Protected Topological States (Final Report, DOE-BES award DE-SC0023105)

This research program established the scientific foundation for the rational, bottom-up design, synthesis, isolation, and investigation of symmetry-protected topological (SPT) electron spin qubits embedded in graphene nanoribbons (GNRs). The work focused on integrating atomically precise low-dimensional carbon nanostructures with emerging quantum logic architectures, providing a pathway toward scalable quantum materials for next-generation computing and sensing technologies. A central component of the program was the elucidation of fundamental relationships between real-space molecular architecture, local spin density distributions, electronic band dispersion, and energy level alignment in atomically precise GNR systems. These correlations define key operational parameters of SPT qubits and were systematically investigated to establish quantitative benchmarks against established molecular and solid-state spin qubit platforms. Attention was given to properties critical for quantum device performance, e.g. decoherence times, spectral sharpness of energy transitions, and tunable exchange interactions between spin states. The research demonstrated that these parameters can be engineered with atomic precision through scalable bottom-up synthetic strategies. Theory-guided design played a central role in identifying candidate structures hosting topologically protected spin states. Experimental validation was performed using both ensemble measurements and single-molecule characterization. In addition to advances in quantum materials synthesis, the program developed and applied spin-sensitive scanning probe microscopy techniques capable of directly probing quantum states and dynamic processes with atomic-scale spatial resolution. These capabilities enabled direct observation and characterization of quantum structures at the single-atom level. While the research activities were primarily hypothesis-driven fundamental investigations, the program adopted a comprehensive materials-by-design framework aimed at translating scientific discoveries into technological concepts compatible with scalable and intelligent manufacturing approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2024

InP- and GaAs-Based 0.6 eV GaInAs Devices for Thermophotovoltaics and Laser Power Conversion

Emerging applications such as thermal energy grid storage, waste heat recovery and portable power generation require efficient thermophotovoltaic (TPV) converters tuned to temperatures near 1000 Degrees Celsius or below. Metamorphic GaInAs with larger lattice constants than InP present a promising option for these needs. The ability to grow these devices on GaAs substrates instead of more expensive InP would enhance the scalability of these devices. In this talk, we present inverted metamorphic Ga0.3In0.7As photovoltaic converters with sub-0.60 eV bandgaps grown on InP and GaAs substrates. These devices are realized using InAsP or GaInP/InAsP compositionally graded buffers which exhibit threading dislocation densities of 1.3 +/- 0.6 x 106 cm-2 and 8.9 +/- 1.7 x 106 cm-2 on InP and GaAs, respectively. Despite this difference in defect density, the devices generate similar open-circuit voltages of 0.386 V and 0.383 V, respectively, under irradiance producing a short-circuit current density of -10 A/cm2, with bandgap-voltage offsets of 0.20 and 0.21 V. We estimate their thermophotovoltaic efficiency using these measurements coupled with broadband reflectance measurments. The InP-based cell is estimated to yield 1.09 W/cm2 at 1100 Degrees Celsius vs. 0.92 W/cm2 for the GaAs-based cell, with TPV efficiencies of 16.8 vs. 9.2%. Both devices are limited by sub-bandgap absorption, which we assess largely occurs in the graded buffers. We estimate that the 1100 Degrees Celsius thermophotovoltaic efficiencies would increase to 24.0% and 20.7% in structures with the graded buffer removed, if previously demonstrated reflectance is achieved. These devices also have application as laser power converters in the 2.0-2.3 um atmospheric window. We estimate efficiencies of 36.8% and 32.5% under 2.0 um monochromatic irradiance of 1.86 W/cm2 and 2.81 W/cm2, respectively.

ENGINEERING,MATERIALS SCIENCE,SOLAR ENERGY↗
DOE OSTI2021

Continuous Fly-Through High-Temperature Synthesis of Nanocatalysts

Conventional thermal treatment system, such as muffle and tube furnaces, typically feature low ramping and cooling rates, which lead to steep thermal gradients during bulk material synthesis that generate inefficient, non-uniform reaction conditions and result in nanoparticle aggregation. Herein, we demonstrate a continuous fly-through material synthesis approach using a novel high-temperature reactor design based on the emerging thermal-shock technology. By facing two sheets of carbon paper with a small distance apart (1–3 mm), we are able to generate uniform, ultra-high temperatures that can reach up to 3200 K within 50 ms by simply applying a voltage of 15 V. We can control this high-temperature to enable ultrafast chemical reactions by continuously feeding raw materials through the device from one end to the other, allowing the final products to be rapidly collected. As a proof-of-concept demonstration, we synthesized Pt nanocatalysts ~4 nm in size anchored to carbon black via this fly-through high-temperature reactor at ~1400 K. Furthermore, we find these supported Pt nanoparticles feature excellent electrocatalytic activities toward methanol oxidation reaction. Compared to existing heating methods, this continuous fly-through high temperature reactor offers a new and highly efficient platform for the synthesis of nanomaterials at high temperatures.

Qiao, Yun↗
DOE OSTI2025

AI-Assisted Conceptual Development of a Pre-Geometric Cosmological Model - An Exercise in AI-Assisted Conceptual Framework Generation, Paper III: Cosmological Structure and Predictions

This paper develops the cosmological consequences of the replication-driven cosmogenesis framework introduced in Paper I and the emergent geometric structure established in Paper II. After the replication epoch freezes out, the coherent sector occupies a finite spectral band and contains a population of excited states. The relaxation of these excited coherent configurations does not produce coherent radiation; instead, all released energy flows into the incoherent substrate, where the randomizer acts as a rapid phase-scrambling mechanism. This process generates an effectively thermal radiation bath, providing a natural reheating mechanism that requires neither inflaton oscillations nor scalar-field potentials, and can be contrasted with standard scenarios of nonperturbative reheating dynamics. Subsequent symmetry-breaking transitions in the coherent vacuum inject additional radiation, yielding a multi-stage thermal history with well-defined energy transfers. We derive the effective equations of state for each component—the cosmological vacuum, the coherent vacuum, and the radiation bath—and show how their interplay produces an FRW-like expansion. The discrete sequence of coherent-state relaxations imprints a distinctive multi-peaked stochastic gravitational-wave background, whose spectral structure reflects the underlying hierarchy of coherent frequencies. Potential observational signatures in the LISA and mid-band frequency ranges are highlighted, providing concrete avenues to test this replication-based cosmological framework in the context of standard cosmological gravitational-wave backgrounds and LISA-oriented forecasts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Evaluation and optimization of flow boiling frictional pressure drop correlations using the data from traditional and next-generation refrigerants in a micro-fin tube

This study presents an experimental evaluation and optimization of flow boiling frictional pressure drop correlations for conventional and next-generation refrigerants in a horizontal micro-fin tube, with particular emphasis on the newly emerging refrigerant blends R-454C and R-455A, for which pressure-drop data in enhanced tubes remain limited. Experiments were conducted with R-410A, R-454C, R-455A, R-134a, R-1234yf, and R-1234ze(E) in a copper micro-fin tube with an inner diameter of 8.468 mm, over mass fluxes ranging from 100 to 300 kg/(m²·s) depending on the refrigerant, and evaporation temperatures of 7, 12, and 14 °C. Frictional pressure gradients were determined from measured total pressure drops after subtracting acceleration pressure drop, and the resulting database was used to assess four existing models: Kuo and Wang (1996), Cavallini et al. (1997), Goto et al. (2001), and Diani et al. (2014). The measured frictional pressure gradient increased with vapor quality and mass flux for all refrigerants and increased further at lower evaporation temperatures, with the overall trend strongly related to liquid viscosity. Among the four correlations, the Goto et al. (2001) model provided the best overall agreement with the measured data before optimization. To further improve prediction accuracy, the Kuo and Wang (1996) and Goto et al. (2001) models were optimized using the complete experimental database. After optimization, both models reduced the overall mean absolute deviation to below 15%, while the optimized Goto et al. (2001) model maintained the best and most consistent overall performance. The results provide new pressure-drop data for next-generation refrigerants and demonstrate that parameter optimization can significantly enhance the applicability of existing micro-fin-tube correlations.

Hu, Yifeng [ORNL] (ORCID:0000000242875185)↗
DOE OSTI2020

Second Harmonic Generation from a Single Plasmonic Nanorod Strongly Coupled to a WSe 2 Monolayer

Monolayer transition metal dichalcogenides, coupled to metal plasmonic nanocavities, have recently emerged as new platforms for strong light–matter interactions. These systems are expected to have nonlinear-optical properties that will enable them to be used as entangled photon sources, compact wave-mixing devices, and other elements for classical and quantum photonic technologies. Here, we report the first experimental investigation of the nonlinear properties of these strongly coupled systems, by observing second harmonic generation from a WSe 2 monolayer strongly coupled to a single gold nanorod. In this work, the pump-frequency dependence of the second-harmonic signal displays a pronounced splitting that can be explained by a coupled-oscillator model with second-order nonlinearities. Rigorous numerical simulations utilizing a nonperturbative nonlinear hydrodynamic model of conduction electrons support this interpretation and reproduce experimental results. Our study thus lays the groundwork for understanding the nonlinear properties of strongly coupled nanoscale systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Energy Efficient, Cost-Effective Power and Co-Generation Technologies: Techno-Environmental Analysis

Development of energy solutions for addressing grid resiliency and energy efficiency while lowering greenhouse gas emissions is critical in today’s energy scenario. Chemical energy provides on demand power. Cogeneration technologies offer numerous benefits in meeting the growing energy demand while lowering the impact on environment. Utilization of waste heat from prime movers in conjunction with energy efficient heat pumps and renewable photovoltaics is an attractive approach. Efficient utilization of available resources to support current and future building energy needs targeting grid resiliency, energy and environmental security via co-generation approaches is the focus of this study. A detailed techno-environmental analysis of hybrid system configurations consisting of conventional and emerging technologies utilizing natural gas, electric grid, and renewable power resources along with heat recovery systems and heat pump technologies are analyzed and presented. The key objective is to present integrated system configurations and thermodynamic analysis of various co-generation systems suitable for providing building energy. Design solutions targeting low carbon footprint and high energy efficiency are presented.

Cheekatamarla, Praveen↗
DOE OSTI2024

Hydrogen Detection Strategies to Support H2@SCALE - The NREL Sensor Laboratory

Hydrogen represents a major pathway to decarbonize and stabilize the national and international energy industry and select manufacturing markets. To facilitate the development of hydrogen markets, the US Department of Energy initiated H2@Scale to bring together stakeholders to advance affordable hydrogen production, transport, storage, and utilization to increase revenue opportunities in multiple energy sectors. One major impediment to hydrogen implementation is cost. To expedite the use of hydrogen in energy and other markets, the United States announced in 2021 the Hydrogen Shot, which seeks to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). As the cost of hydrogen drops, new applications will emerge that will require unique configurations of existing equipment and infrastructure, and eventually lead to advances in the generation and utilization of hydrogen. As the hydrogen economy expands, sensors and detection methods will need to adapt to changing infrastructure demands to address the primary targets of health & safety, emissions monitoring, and process control. The NREL Sensor Laboratory is playing a pivotal role in advancing the use of hydrogen sensors and detection methodologies in each of these categories to support DOE's mission for safe and efficient utilization in emerging markets. Health & safety monitors are required to ensure that operators and facilities can react to unintended hydrogen releases, either as GH2, LH2, or as a constituent of blends (e.g., natural gas or ammonia). Current detection methodologies focus on safety applications to detect near its lower flammable limit (4 vol %), and typically include point sensors in applications such as fixed or mobile detectors (e.g., personal gas monitors). Methodologies amenable for area detection include acoustic, emerging optical imaging methods, and flame detectors. Comparable detection strategies can be utilized for emissions monitoring and quantization, however few methods can simultaneously cover both low (emissions) and high (health & safety) levels. Deployment of emission level detectors will be required to 1) reduce product loss through small but potentially significant leaks from an environmental or cost perspective, 2) reduce downtime of high demand systems by early identification of eminent system failures (leaks through pump or compressor seals indicative of impending failure), and 3) address potential emission monitoring requirements that may be set by regulating bodies. The first two points should be adopted by industry to reduce the cost-of-goods-sold. The third main category for hydrogen detection relates to process control and may be advantageous for many existing applications. Two main applications are emerging. For example, the purity requirements for hydrogen that is dispensed from refueling systems for hydrogen fuel cell electric vehicles (FCEV) is rigorously regulated by the Standard SAE J2719, which prescribes maximum allowable levels of multiple impurities in the hydrogen fuel and must be verified by a regulatory body. Hydrogen contaminant detectors (HCD) integrated to the fueling station can assure this compliance. HCDs must be able operate in 100% H2 backgrounds and be able to distinguish between multiple contaminants at low ppm to low ppb levels. Secondly, as a strategy to decarbonize the natural gas grid, there are proposals to blend hydrogen with natural gas. This blending will affect transport applications (pipeline infrastructure), stationary combustion systems (turbines), and consumer and commercial appliances. In the short-term, hydrogen levels up to 20% are proposed. Variations in the hydrogen level can have dramatic impact on the combustion process and on the potential response of safety sensors. These mixtures may be regulated so that the concentration at a delivery point must be monitored with high precision. However, routine maintenance may introduce background gases such as ambient air (with water) or maintenance gases (introduced with welding processes or adhesive outgassing.) Therefore, the detection methodology must be robust enough to recover or respond to various contaminants. Several reviews can be found in literature addressing sensing and detection technologies, including their limitations and applications. However, for most applications, limitations can be alleviated by combining various detection techniques either through system integration or implementation of machine learning methods (artificial intelligence). In this presentation, we will discuss several applications, highlight their current approach for hydrogen detection, and suggest detection strategies to supplement their limitations.

ENERGY STORAGE,HYDROGEN↗
DOE OSTI2021

Additive manufacturing and applications of nanomaterial-based sensors

Nanoscale materials possess distinct physical and chemical attributes including size-dependent properties, quantum confinement, high surface-to-volume ratio, and superior catalytic activity. These unique qualities enable sensors with high sensitivity, robustness, and fast time response. As the emergence of the Internet of Things (IoT) demands increased production of sensors, it also provides an impetus for concentrated nanomaterial-based sensor research. Meanwhile, additive manufacturing (AM) of nanomaterial-based sensors is critical to bridge the gap between one-off, lab-scale fabrication and cost-effective, industrial-scale production with high reproducibility. By applying the design flexibility and cost savings of AM techniques, a new generation of nanomaterial-based sensing platforms can be integrated with IoT devices in the consumer space. Furthermore, emergent research in human-machine interfaces, food safety, and point-of-care diagnostics will be expedited by the development of sensors that can be printed with irregular form factors. In this Review, the relative strengths and weaknesses of printed sensor systems based on zero-, one-, and two-dimensional nanomaterials are discussed. In addition, sensors enabled by printable soft nanomaterials, heterostructures, and nanocomposites are surveyed due to their synergistic advantages for wearable healthcare monitoring and soft robotics. Lastly, a roadmap for the next decade of research on this topic is provided.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Emergent flat band and topological Kondo semimetal driven by orbital-selective correlations

Flat electronic bands are expected to show proportionally enhanced electron correlations, which may generate a plethora of novel quantum phases and unusual low-energy excitations. They are increasingly being pursued in d-electron-based systems with crystalline lattices that feature destructive electronic interference, where they are often topological. Such flat bands, though, are generically located far away from the Fermi energy, which limits their capacity to partake in the low-energy physics. Here we show that electron correlations produce emergent flat bands that are pinned to the Fermi energy. We demonstrate this effect within a Hubbard model, in the regime described by Wannier orbitals where an effective Kondo description arises through orbital-selective Mott correlations. Moreover, the correlation effect cooperates with symmetry constraints to produce a topological Kondo semimetal. Our results motivate a novel design principle for Weyl Kondo semimetals in a new setting, viz.d-electron-based materials on suitable crystal lattices, and uncover interconnections among seemingly disparate systems that may inspire fresh understandings and realizations of correlated topological effects in quantum materials and beyond.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

A Study of Emerging Ancillary Service Markets in Non-Restricted Regions of the Western Power Grid (CRADA Final Report)

Rising penetrations of renewable energy generation in the Western United States pose new requirements for ancillary services, which are the services required, in addition to energy service, in order to maintain the system reliability. This project combines the research capabilities of the National Renewable Energy Laboratory (NREL) and the University of Colorado at Boulder (CU-Boulder) to provide more accurate information to market participants and regulators about the present and future size and composition of ancillary service markets in the non-restructured regions of the West. This information would provide market participants and state and federal regulators with a more transparent view of future ancillary service markets, and allow for more efficient system planning in both the private and public sectors of the electricity market.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Integrating AI Data Centers with the Power Grid

The rapid expansion of artificial intelligence (AI) has triggered an unprecedented surge in electricity demand, with US data center energy use projected to double or triple 2023 levels by 2028. This exponential growth places strain on grid infrastructure, which can hinder timely construction of desired computing capacity. To bridge this supply-demand gap, utilities and AI developers are increasingly turning to demand flexibility, a strategy that incentivizes shifting or reducing power use during peak periods of grid stress. Data centers are uniquely equipped for flexible operations due to their digital workloads, built-in redundancy, and onsite energy assets. This article outlines four primary mechanisms to enable data center flexibility: computational load flexibility (shifting tasks temporally or geographically), flexible use of core facility infrastructure adjustments, energy storage utilization, and onsite electricity generation. To encourage adoption, utilities are deploying new tariff designs, including voluntary interruptible service riders, mandated flexibility requirements, and streamlined interconnection processes for flexible loads. For the highly capitalized and rapidly growing AI industry, the primary motivators for embracing these strategies are expediting facility interconnection, satisfying emerging regulatory mandates, and mitigating community resistance. While demand flexibility cannot substitute the long-term need for new bulk power generation, it serves as an essential, immediate solution for enabling near-term deployment. By transforming data centers from grid stressors into stabilizing assets, flexible operations can ensure reliable grid integration, ease market pressures, and support a resilient power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Climate and air pollution impacts of generating biopower from forest management residues in California

California faces crisis conditions on its forested landscapes. A century of aggressive logging and fire suppression in combination with conditions exacerbated by climate change have created an ongoing ecological, economic, and public health emergency. Between commercial harvests on California’s working forestlands and the increasing number of acres the state treats each year for fire risk reduction and carbon sequestration, California forests generate millions of tons of woody residues annually—residues that are typically left or burned in the field. State policymakers have turned to biomass electricity generation as a key market for woody biomass in the hope that it can support sustainable forest management activities while also providing low-carbon renewable electricity. However, open questions surrounding the climate and air pollution performance of electricity generation from woody biomass have made it difficult to determine how best to manage the risks and opportunities posed by forest residues. The California Biomass Residue Emissions Characterization (C-BREC) model offers a spatially-explicit life cycle assessment framework to rigorously and transparently establish the climate and air pollution impacts of biopower from forest residues in California under current conditions. The C-BREC model characterizes the variable emissions from different biomass supply chains as well as the counterfactual emissions from prescribed burn, wildfire, and decay avoided by residue mobilization. We find that the life cycle ‘carbon footprint’ of biopower from woody residues generated by recent forest treatments in California ranges widely—from comparable with solar photovoltaic on the low end to comparable with natural gas on the high end. This variation stems largely from the heterogeneity in the fire and decay conditions these residues would encounter if left in the field, with utilization of residue that would otherwise have been burned in place offering the best climate and air quality performance. California’s energy and forest management policies should account for this variation to ensure desired climate benefits are achieved.

54 ENVIRONMENTAL SCIENCES↗