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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
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 OSTI2021

Behavioral state resource selection in invasive wild pigs in the Southeastern United States

Elucidating correlations between wild pig ( Sus scrofa ) behavior and landscape attributes can aid in the advancement of management strategies for controlling populations. Using GPS data from 49 wild pigs in the southeastern U.S., we used hidden Markov models to define movement path characteristics and assign behaviors (e.g., resting, foraging, travelling). We then explored the connection between these behaviors and resource selection for both sexes between two distinct seasons based on forage availability (i.e., low forage, high forage). Females demonstrated a crepuscular activity pattern in the high-forage season and a variable pattern in the low-forage season, while males exhibited nocturnal activity patterns across both seasons. Wild pigs selected for bottomland hardwoods and dense canopy cover in all behavioral states in both seasons. Males selected for diversity in vegetation types while foraging in the low-forage season compared to the high-forage season and demonstrated an increased use of linear anthropogenic features across seasons while traveling. Wild pigs can establish populations and home ranges in an array of landscapes, but our results demonstrate male and female pigs exhibit clear differences in movement behavior and there are key resources associated with common behaviors that can be targeted to improve the efficiency of management programs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Interplay between Mixed and Pure Exciton States Controls Singlet Fission in Rubrene Single Crystals

Singlet fission (SF) is a multielectron process in which one singlet exciton S converts into a pair of separated triplet excitons T. SF is widely studied as it may help overcome the Shockley−Queisser efficiency limit for semiconductor photovoltaic cells. To elucidate and control the SF mechanism, great attention has been given to the identification of intermediate states in SF materials, which often appear elusive due to the complexity and fast time scales of the SF process. Here, we apply 14 fs-1 ms transient absorption techniques to high-purity rubrene single crystals to disentangle the intrinsic fission dynamics from the effects of defects and grain boundaries and to identify reliably the fission intermediates. Our data demonstrates that above-gap excitation directly generates a hybrid vibronically assisted mixture of singlet state and triplet-pair multiexciton [S/TT], which rapidly (<100 fs) and coherently branches into pure singlet or triplet excitations. The relaxation of [S/TT] to S is followed by a relatively slow and temperature-activated (48 meV activation energy) incoherent fission process. The SF competing pathways and intermediates revealed here unify the observations and models presented in previous studies of SF in rubrene and offer alternative strategies for the development of SF-enhanced photovoltaic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Adaptation of virtual synchronous generators to dynamic conditions in power grids

Virtual synchronous generators (VSGs) are widely adopted as grid-forming controls for inverter-based resources. However, when grid conditions vary significantly as characterized by changes in short-circuit ratio (SCR) and the reactance-to-resistance (X/R) ratio, fixed-gain designs and the commonly used P–Q decoupling assumption can become inaccurate. Such conditions can degrade transient power performance, leading to oscillations, prolonged settling, and overshoot, particularly in stiff-grid operating points. This paper quantifies how grid strength and impedance-dependent coupling affect the active–reactive power dynamics of a conventional VSG over a broad range of SCR and X/R values. An adaptive VSG tuning framework is then developed by combining (i) a coupling-explicit, impedance-parameterized state-space model to enable systematic controller synthesis, (ii) a full-state-feedback law designed via pole placement to meet prescribed damping and settling-time specifications, and (iii) a physics-informed neural network (PINN)–based online grid-impedance estimator that updates controller gains in real time as grid conditions vary. Offline simulations in MATLAB/Simulink and real-time validation on an OPAL-RT platform show that the proposed method preserves consistent damping and settling behavior with reduced overshoot across wide SCR and X/R ranges, compared with fixed-gain VSG baselines.

Adaptive control↗
DOE OSTI2023

Investigating the effect of aluminum oxide fixed charge on Schottky barrier height in molybdenum oxide-based selective contacts

A tunneling atomic layer deposited (ALD) AlO x layer was inserted in a Si|SiO x |AlO x |MoO x |metal tunnel diode structure to investigate the impact of fixed interface charge on Schottky barrier height. ALD AlO x provides a synthesis-tunable fixed charge and can provide excellent field effect passivation at the SiO x |AlO x interface. A large (>1 × 10 12 q.cm –2 ) negative fixed charge density was expected to decrease Schottky barrier height on p-type silicon and increase Schottky barrier height on n-type silicon by up to 0.4 eV based on an electrostatic model. Fixed charge density and interface trap state density were measured using a thick layer of AlO x in metal oxide semiconductor capacitor structures (MOSCAPs); Schottky barrier height and specific contact resistivity were measured using a tunneling-active layer of AlO x in contacted structures with MoO x . In some samples, HfO x interface layers were inserted between AlO x and silicon to quench the fixed negative charge. The Schottky barrier height was found to vary with processing conditions but did not have a strong correlation with expected fixed charge density. Here, while fixed charge may play a role in determining the Schottky barrier height, other parameters also have significant affect. Further work is needed to minimize contact resistivity and elucidate other factors impacting the Schottky barrier height.

14 SOLAR ENERGY↗
DOE OSTI2024

Microbial polyphenol metabolism is part of the thawing permafrost carbon cycle

Abstract With rising global temperatures, permafrost carbon stores are vulnerable to microbial degradation. The enzyme latch theory states that polyphenols should accumulate in saturated peatlands due to diminished phenol oxidase activity, inhibiting resident microbes and promoting carbon stabilization. Pairing microbiome and geochemical measurements along a permafrost thaw-induced saturation gradient in Stordalen Mire, a model Arctic peatland, we confirmed a negative relationship between phenol oxidase expression and saturation but failed to support other trends predicted by the enzyme latch. To inventory alternative polyphenol removal strategies, we built CAMPER, a gene annotation tool leveraging polyphenol enzyme knowledge gleaned across microbial ecosystems. Applying CAMPER to genome-resolved metatranscriptomes, we identified genes for diverse polyphenol-active enzymes expressed by various microbial lineages under a range of redox conditions. This shifts the paradigm that polyphenols stabilize carbon in saturated soils and highlights the need to consider both oxic and anoxic polyphenol metabolisms to understand carbon cycling in changing ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Stable heat and particle flux detachment with efficient particle exhaust in the island divertor of Wendelstein 7-X

The island divertor concept is an innovative and promising idea to handle heat and particle exhaust in stellarators. At the Wendelstein 7-X (W7-X) stellarator, this divertor concept plays a central role in the device mission to demonstrate reactor relevant plasma confinement for steady-state time scales of up to 30 minutes in the high-performance campaign (OP2) starting in 2022. During the recently concluded first campaign with the inertially cooled island divertor, a large step in the experimental qualification of this divertor concept has been made. In discharges heated with electron cylotron resonance heating of 5-6 MW, central densities in the range of 0.7-1.2 × 10 20 m -3 have been reached in combination with full divertor heat flux detachment. Also, significant neutral gas pressures and neutral compression ratios were shown for the first time in combination with reduced divertor particle flux. The divertor heat loads drop by an order of magnitude from >5 MW m -2 to below 0.5 MW m-2 with increasing density, and substantial compression of neutrals reaching neutral pressure in the sub-divertor volume of >6.0 × 10 -4 mbar was seen. These elevated neutral pressure levels can be obtained and maintained with an up to 80% reduction of the particle fluxes onto the divertor target tiles. This discharge scenario was held stably detached for up to 28 seconds, which is equivalent to several hundred energy confinement times τ E and longer than the time scales for current relaxation. No impurity accumulation was seen at constant Z eff ≈ 1.5 and the stored energy stayed constant at levels of W dia >600 kJ. The level of neutral pressure and compression reached in this scenario extrapolates well to the steady-state particle exhaust requirements for high-performance steady-state operation in OP2, in which the fully actively cooled high-heat-flux divertor will be available. Finally, an overview of this recently discovered divertor regime is given and the status of the physics understanding based on modeling of these regimes with the EMC3-EIRENE code is presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulated wildfire burned area over the CONUS during 2001-2020

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM). A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

Liu, Ye↗
DOE OSTI2025

Understanding EV Charging Pain Points Through Deep Learning Analysis

Current and potential electric vehicle (EV) owners express concerns about the charging infrastructure, mentioning non-functional chargers, prolonged charging times, inconvenient charger locations, long wait times, and high costs as major barriers. Addressing these issues often requires analyzing actual vehicle charging data, which is typically proprietary and inconsistent due to diverse standards and protocols. To understand and improve the EV charging experience, customer reviews are typically used to identify common customer pain points (CPPs). However, there is not a comprehensive method to map customer reviews to a standardized set of CPPs. In collaboration with the National Charging Experience (ChargeX) Consortium, this study bridges these gaps by proposing a Systematic Categorization and Analysis of Large-scale EV-charging Reviews (SCALER) framework. SCALER is an integrated, deep learning framework that segments, actively labels, analyzes, and classifies EV charging customer reviews into six CPP categories. To test its effectiveness, we used SCALER to analyze over 72,000 reviews from customers charging various EV models on different networks across the United States. SCALER achieves a classification accuracy of 92.5%, with an F1 score exceeding 85.7%. By demonstrating real-world applications of SCALER, we enhance the industry’s ability to understand and address CPPs to improve the EV charging experience.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2020

Determinants for proton location and electron coupled proton transfer in hydrogen bonded pentafluorophenol-anion clusters

This work aims to reveal the determinant factors for proton locations and electron coupled proton transfer (ECPT) in biologically relevant hydrogen bonded systems. Pentafluorophenol-anion clusters [C6F5O-•H+•A-]- are chosen to model active sites of biological functions, with the anion A- being systematically varied to ensure the proton affinities (PAs) of the anions well cover the referenced PA of C6F5O- from being appreciably smaller, similar, and to significantly larger. Negative ion photoelectron spectroscopy of these clusters provides spectroscopic evidence showing that proton location in the anionic state is largely following the PA prediction, while ECPT is observed only for the clusters with the anion possessing the electron binding energy (EBE) significantly larger than that of the referenced C6F5O-. Theoretical calculations suggest these clusters are stabilized by forming single strong hydrogen bond between donor and acceptor, and the associated charge and MO analyses fully support the experimental observations. The current holistic cluster model study indicates that PA is the right determinant that can be used to predict the proton location and describe hydrogen bonding structures, while both PA and EBE of anionic groups play important roles in facilitating ECPT process. This work was supported by U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Chemical Science, Geosciences, and Biosciences (X.-B.W.), and performed using EMSL, a national scientific user facility sponsored by DOE’s Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory, which is operated by Battelle Memorial Institute for the DOE. Part of this work was supported by the National Natural Science Foundation of China (No-11604046 for JZ; No. 11727810 for ZRS), as well as “Chenguang Program” by Shanghai Education Development Foundation, Shanghai Municipal Education Commission (No. 16CG38) and Natural Science Foundation of Shanghai (No. 16ZR1448100) (JZ).

Zhang, Jian↗
DOE OSTI2024

Multiwavelength study of OT 081: broadband modelling of a transitional blazar

ABSTRACT OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source, which includes very high energy (VHE, $E\gt $ 100 GeV) $\gamma$-ray data taken by the MAGIC (Major Atmospheric Gamma-ray Imaging Cherenkov telescopes) and H.E.S.S. (High Energy Stereoscopic System) imaging Cherenkov telescopes. The discovery of VHE $\gamma$-ray emission happened during a high state of $\gamma$-ray activity in July 2016, observed by many instruments from radio to VHE $\gamma$-rays. We identify four states of activity of the source, one of which includes VHE $\gamma$-ray emission. Variability in the VHE domain is found on daily time-scales. The intrinsic VHE spectrum can be described by a power law with index $3.27\pm 0.44_{\rm stat}\pm 0.15_{\rm sys}$ (MAGIC) and $3.39\pm 0.58_{\rm stat}\pm 0.64_{\rm sys}$ (H.E.S.S.) in the energy range of 55–300 and 120–500 GeV, respectively. The broadband emission cannot be successfully reproduced by a simple one-zone synchrotron self-Compton model. Instead, an additional external Compton component is required. We test a lepto-hadronic model that reproduces the data set well and a proton-synchrotron-dominated model that requires an extreme proton luminosity. Emission models that are able to successfully represent the data place the emitting region well outside of the broad-line region to a location at which the radiative environment is dominated by the infrared thermal radiation field of the dusty torus. In the scenario described by this flaring activity, the source appears to be a flat spectrum radio quasar (FSRQ), in contrast with past categorizations. This suggests that the source can be considered to be a transitional blazar, intermediate between BL Lac and FSRQ objects.

Abe, H.↗
DOE OSTI2026

Data-driven Mori–Zwanzig modeling of Lagrangian particle dynamics in turbulent flows

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly nontrivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This approach is based on the Mori–Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of applications, for example, for the control of active Lagrangian agents in turbulence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Regional Oil and gas Aerial Methane Synthesis model (ROAMS) v2.0

The Regional Oil and gas Aerial Methane Synthesis model is a tool to convert the results of wide-area, source-resolved aerial methane remote sensing surveys of oil and natural gas infrastructure in a given region into methane emissions inventories (estimates of the magnitude and breakdown of methane emissions from the surveyed infrastructure). The tool leverages databases of source-resolved methane emissions detected in aerial surveys, aerial survey coverage information (which areas were measured and when), data summarizing surveyed oil and natural gas infrastructure and production (derived from third-party databases), as well as state-of-the-art mechanistic emissions simulation tools to characterize emissions too small for the aerial system to see. The regional methane emissions estimates produced by this tool are much more granular in both space and asset type than common satellite- or flux tower-based regional estimates. Unlike other tools for converting site-level measurements into regional emissions estimates, our unique geostatistical approach integrates aerially measured emissions with limited need for statistical extrapolation, which can be highly sensitive to modeler assumptions. As a result, ROAMS-based estimates of regional methane emissions from oil and gas activity are widely viewed as highly credible, as evidenced by the success of Dr. Sherwin's recent paper in Nature.

Sherwin, Evan [Lawrence Berkeley National Laborato↗
DOE OSTI2025

Regional Oil and gas Aerial Methane Synthesis model (Analytica) (ROAMS Analytica) v1.5.2

The Regional Oil and gas Aerial Methane Synthesis model (Analytica) is a tool to convert the results of wide-area, source-resolved aerial methane remote sensing surveys of oil and natural gas infrastructure in a given region into methane emissions inventories (estimates of the magnitude and breakdown of methane emissions from the surveyed infrastructure). This version is written in the Analytica programming language, and this version accompanies a correction in preparation for submission to Sherwin et al. 2024 (Nature). The tool leverages databases of source-resolved methane emissions detected in aerial surveys, aerial survey coverage information (which areas were measured and when), data summarizing surveyed oil and natural gas infrastructure and production (derived from third-party databases), as well as state-of-the-art mechanistic emissions simulation tools to characterize emissions too small for the aerial system to see. The regional methane emissions estimates produced by this tool are much more granular in both space and asset type than common satellite- or flux tower-based regional estimates. Unlike other tools for converting site-level measurements into regional emissions estimates, our unique geostatistical approach integrates aerially measured emissions with limited need for statistical extrapolation, which can be highly sensitive to modeler assumptions. As a result, ROAMS-based estimates of regional methane emissions from oil and gas activity are widely viewed as highly credible, as evidenced by the success of Dr. Sherwin's recent paper in Nature.

Sherwin, Evan [Lawrence Berkeley National Laborato↗
DOE OSTI2022

Status of Research Activities on LVDTs: Modeling and Simulation Development and Performance Characterization of Commercial LVDTs

FY-22 research focused on two major areas. The first area was modeling and simulation with linked testing that will advance the Linear Variable Differential Transformers (LVDT) technology for use by stakeholders requiring LVDTs in upcoming irradiation tests. The second area focused on testing LVDTs purchased from U.S. suppliers that were investigated in FY-21. These cross-cutting development activities will ensure stakeholders have the current state-of-the-art LVDT-based technologies for deployment in future irradiation tests. Two stand-alone reports are included in the appendixes. The purpose of this report is to provide a convenient body of work to access the reports and to evaluate the data and conclusions provided. Appendix A, “Testing of the RDP Translation Transducer,” contains a complete evaluation of an RDP Electrosense linear transducer that has the capability of replicating the form, fit, and function of LVDTs currently being used at Idaho National Laboratory (INL). Appendix B is an extract from the report, “Advanced Sensors and Instrumentation Program at Boise State University (BSU), August 2022, Linear Variable Differential Transformers.” This report contains modeling and testing results conducted at BSU and INL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Is Clean Hydrogen Production a Good Fit for Questa? (Final Economic Impact Results) [Slides]

The Village of Questa, New Mexico is aiming to become a regional clean energy hub with robust and diverse employment opportunities for the local community supported by the energy sector and by other businesses inspired or attracted by abundant clean energy, outdoor recreation, and cultural opportunities. A coalition of stakeholders in the Village of Questa, comprising the Village, Kit Carson Electric Cooperative (KCEC), Questa Economic Development Fund, and Chevron, is exploring options to develop hydrogen production facilities as an opportunity to create jobs, provide reliable clean energy, and utilize former mine resources. Questa is home to a molybdenum mine owned by Chevron that closed in 2014. Several residents in Questa and surrounding communities lost their jobs when the mine closed and transitioned from active operations into environmental remediation. Although remediation efforts have been ongoing since 2014 and are expected to continue for at least 16 more years, the number of jobs with Chevron is much smaller now than it was before the closure. Between available workforce, brownfield land, and water rights formerly supporting mine operations but now in a transition period, there are considerable local resources that could be directed toward clean energy generation. Questa's electricity supply is already 100% solar during daylight hours thanks to Kit Carson Electric Cooperative's (KCEC's) strategic decision-making and partnering over the last decade. Now, Questa, KCEC, and Chevron are exploring the potential costs and benefits of siting an electrolytic hydrogen production facility and additional solar photovoltaic (PV) capacity in Questa to further advance the region's clean energy economy. In this report, we estimated the potential economic impacts (i.e., jobs, value added, gross output, tax revenue) of constructing and operating a combined hydrogen (32 MW polymer electrolyte membrane electrolizer + 7.5 MW fuel cell) and solar facility (22.5 MW) in the Village of Questa, as well as the resulting economic spillovers to Taos County and the state of New Mexico. We employ an input-output model that leverages IMPLAN's economic data for the region complemented by construction and operating expenses estimated by NREL and feedback from the local coalition to evaluate the direct, indirect and induced effects of the project construction (transient impacts) and operation (more permanent impacts). Based on the area's average trade profile, feedback from the coalition and current market conditions, these projects are expected to support 487 full-time equivalent jobs during construction, generating $\$24$ million in income for those workers and $\$82$ million in local economic activity in the state. Of those jobs, 106 are expected to be construction sector jobs. These investments are also estimated to add $\$36.5$ million to New Mexico's gross state product (GSP). In the Village of Questa, we estimate 16 jobs will be supported in construction and transportation industries, generating $\$0.9$ million in earnings. In Taos County, the construction phase is expected to support 285 jobs primarily in construction and professional services, while manufacturing jobs dominate the results for the Rest of New Mexico. The Village is also estimated to receive $\$0.9$ million in tax revenue from the construction phase alone. Once in operation, the project continues to impact the state and Questa. Around 20 jobs (full-time equivalent for each year of operation) are supported across New Mexico, with approximately 11 directly employed in Questa by both facilities. The total annual local economic activity supported by ongoing operations is just over $\$1.3$ million/yr, generating $\$1.6$ million/yr in additional income in the state. Annual operations are estimated to add $\$2.1$ million to the state's GSP. The Village is expected to receive around $\$43,000$/yr in tax revenue. Impacts vary significantly depending on which businesses are supplying materials, equipment and services, and where construction workers reside. Choosing local suppliers will most benefit Questa and the New Mexico economy, adding up to 500 jobs during construction and 13 long-term jobs. Local and state governments may consider ways to incentivize local businesses in order to maximize economic benefits.

08 HYDROGEN↗