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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 OSTI2026

Offshore Wind Farm Turbine and Energy Storage Optimization

Abstract This paper evaluates the technical and economic feasibility of repurposing decommissioned offshore oil and gas platforms as electrical substations for offshore wind projects in the U.S. Gulf of America, a region characterized by relatively low and highly variable wind speeds, extensive legacy offshore infrastructure, and exposure to merchant electricity markets. A unified techno-economic framework is developed using the Repurposing Offshore Infrastructure for Continued Energy (ROICE) Economic Model (REM) to integrate Gulfspecific wind resource assessment, commercial wind turbine performance, offshore infrastructure cost modeling, and wholesale electricity market exposure. Gulf wind speed data are vertically extrapolated to turbine hub height and combined with manufacturer power curves to compute annual energy production and capacity factors across a broad portfolio of commercial turbines, enabling identification of turbine designs best suited for low-wind offshore environments. Hourly electricity price data from the Midcontinent Independent System Operator (MISO) day-ahead market are incorporated to characterize revenue potential, price volatility, and the temporal alignment between wind generation and market conditions. In addition, a conceptual framework for offshore battery energy storage system (BESS) integration is developed to support future investigation of market-responsive energy shifting at repurposed platforms. Results from the turbine evaluation demonstrate that machines with lower cut-in wind speeds and earlier ‘rated-power’ characteristics significantly outperform larger, industry-standard offshore turbines for the same net power under Gulf wind conditions, underscoring the need for region-specific technology selection. Market analysis further reveals substantial price variability and limited intrinsic alignment between wind production and high-price periods, motivating consideration of operational flexibility mechanisms. While storage optimization is not implemented in this study, the REM framework establishes a transparent and replicable foundation for co-evaluating turbine selection, infrastructure constraints, and market exposure, providing a practical pathway for assessing the potential role of repurposed offshore platforms in enabling economically viable offshore wind development in the Gulf of America.

02 PETROLEUM↗
DOE OSTI2022

The economic value of photovoltaic performance loss mitigation in electricity spot markets

Photovoltaic (PV) performance is affected by reversible and irreversible losses. These can typically be mitigated through responsive and proactive operations and maintenance (O&M) activities. However, to generate profit, the cost of O&M must be lower than the value of the recovered electricity. This value depends both on the amount of recovered energy and on the electricity prices, which can vary significantly over time in spot markets. The present work investigates the impact of the electricity price variability on the PV profitability and on the related O&M activities in Italy, Portugal, and Spain. Here, it is found that the PV revenues varied by 1.6 × to 1.8 × within the investigated countries in the last 5 years. Moreover, forecasts predict higher average prices in the current decade compared to the previous one. These will increase the future PV revenues by up to 60% by 2030 compared to their 2015–2020 mean values. These higher revenues will make more funds available for better maintenance and for higher quality components, potentially leading to even higher energy yield and profits. Linearly growing or constant price assumptions cannot fully reproduce these expected price trends. Furthermore, significant price fluctuations can lead to unexpected scenarios and alter the predictions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Metadynamics investigation of lanthanide solvation free energy landscapes and insights into separations energetics

Lanthanide ion solvation chemistry in nonaqueous phases is key to understanding and developing effective separation processes for these critical materials. Due to the complexity and inherent disorder of the solution phase, a comprehensive picture of the solvated metal ion is often difficult to generate solely from conventional spectroscopic approaches and electronic structure calculations, particularly in the extractant phase. In this work, we use classical molecular dynamics (MD) simulation with an advanced sampling technique, metadynamics, supplemented by experimental spectroscopy and speciation analysis, to measure lanthanide solvation free energy landscapes. We define coordination-based collective variables to probe the entire range of solvation configurations in the organic phase of lanthanum (La), europium (Eu), and lutetium (Lu) nitrate salts bound with a commonly used extractant, N,N′-dimethyl, N,N′-dioctylhexylethoxymalonamide (DMDOHEMA). The known lanthanide extraction trend of La ≈ Eu > Lu is readily explained by the measured free energy surfaces, which show consistent DMDOHEMA coordination from La to Eu, followed by loss of DMDOHEMA coordination from Eu to Lu. These simulations suggest how ligand crowding at the metal center can control selectivity, in this case resulting in the opposite extraction trend as observed with other conventional extractants, where the enthalpic contribution from increasing lanthanide charge density across the series dominates the extraction energetics. We also find that the presence of inner-sphere water, verified by time-resolved fluorescence, diversifies the accessible solvation structures. As a result, understanding solvation requires consideration of an entire thermodynamic ensemble, rather than the single dominant lowest-energy structure, as is often considered out of necessity in interpretation of spectroscopic data or in electronic structure-based ligand design approaches. In general, we demonstrate how metadynamics uniquely enables investigation of complex, multidimensional solvation energetic landscapes, and how it can explain selectivity trends where extraction is controlled by more complex mechanisms than simple charge density-based selectivity.

Wang, Xiaoyu↗
DOE OSTI2026

Modeling and Power-Hardware-in-the-Loop Validation of Synchronous Machine Governor

This paper introduces the development of a high-fidelity gas turbine governor model using a programmable logic controller for power-hardware-in-the-loop (PHIL) validation. The governor model is integrated with the National Renewable Energy Laboratory's (NREL) PHIL test bed, featuring a 2-MVA synchronous machine and a 2.5-MW variable-speed drive, to emulate NG-driven HRSGs and CTs under various operational scenarios. The primary objective of this research is to study the grid-connected and islanding operations of conventional generation sources, with representative startup sequences including turbine purge, ignition, speed ramp-up, synchronization, and breaker closure. Preliminary results of the generator governor model on NREL PHIL platform, particularly using the 2.5-MW dynamometer system, offered significant insights into the modeling techniques, hardware integration, scaling, and real-world simulation dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Conceptual Design Study of a Thermal Energy Storage System Using Granular Solids for Coal-fired Boilers

<span style="font-size: 12pt;">Presentation for the 2020 International Pittsburgh Coal Conference held virtually from September 8-11, 2020.</span><span style="font-size: 12pt;">Abstract: As electricity from various renewable energy sources is increasingly supplied to the power grid, the grid’s stability will be challenged. Proposed grid-stability solutions will need to address variability of sources, such as solar and wind energy, in order to alleviate problems such as overgeneration and ramping. In regions where increases in solar energy are prominent, such as California, the net power demand curve often referred to as “the Duck Curve,” has caused problems for energy dispatchers, due to over-generation and fast ramping demands. In this project, we investigated a conceptual design for a Thermal Energy Storage System (TESS) integrated into a coal-fired power plant to support grid-scale storage. Implementing a TESS into power plants can allow for an increase in renewable sources, increase grid resilience, and allow conventional coal-fired plants to operate more efficiently. For this project, a TESS using granular solids as the energy-storage media is integrated into a conventional coal-fired power plant. The purpose of the TESS is to store energy from the flue gas during low demand periods and transfer the energy to steam during peak demand periods. The storage allows the stabilization of combustion and related boiler components at low loads and provides the opportunity to avoid some shutdowns. This study uses thermodynamic properties of several candidate thermal storage materials to estimate the size of storage system required for several operating scenarios. </span>

Huckaby, E. David↗
DOE OSTI2023

Resilient Distribution System Restoration with Equitable Load Shedding

A methodology is proposed for the improvement of electric distribution system resilience to high-impact, low-probability catastrophic events. An approach for dynamic network reconfiguration and coordination of distributed energy resources is introduced to assist in restoration efforts. The problem is formulated as a mixed-integer linear program that minimizes generation costs, the cost of lost load, and costs associated with equitable load shedding, while respecting operational limits of generation, loads, and the network. Constraints are imposed on binary switching variables to ensure equitable load shedding in emergency situations. Numerical validation of the proposed approach is conducted on an example distribution feeder, and case studies are performed to analyze the impact of various parameters in the optimization problem formulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Dynamic P-Q Capability and Abnormal Operation Analysis of a Wind Turbine with Doubly-Fed Induction Generator

After several accidents occurred in wind farms in the United States and around the world, the subsynchronous oscillation (SSO) issues in grid-connected wind farms have gained serious attention. Particularly, these issues have caused significant challenges to wind turbines with a doubly fed induction generator (DFIG) because it is connected to the grid via both its stator and rotor paths. Traditionally, a P-Q capability chart is utilized to assure the safe operation boundary for a synchronous generator. But the energy conversion characteristics of a DFIG wind turbine are completely different. A critical factor to affect the reliable operation of a DFIG wind turbine is the rated current and pulse-width modulation (PWM) saturation constraints of its power converters. These constraint conditions can be affected by the wind turbine rotating speed and grid conditions. However, a detailed study of DFIG P-Q capability from these perspectives has not been conducted, which has hindered adequate understanding of many abnormal wind turbine operations reported in the literature and the development of advanced control technologies to overcome the challenges. The proposed study in this paper considers vector control implementation to DFIG power electronic converters in the dq reference frame, and the models and algorithms developed for the P-Q capability study have addressed specific DFIG power converter constraints that are different from those of a traditional synchronous generator. The paper especially focuses on exploring the dynamic natures of DFIG P-Q capability under uncertain and variable conditions to explore the root causes of many abnormal operations of DFIG wind turbines reported in the literature. The proposed study is validated through an electromagnetic transient simulation model of a grid-connected DFIG wind turbine. The proposed study has the potential to lead to the development of new DFIG control technologies that can help overcome the challenges for many abnormal operations of DFIG wind turbines.

17 WIND ENERGY↗
DOE OSTI2022

Stacking Fault Induced Symmetry Breaking in van der Waals Nanowires

While traditional ferroelectrics are based on polar crystals in bulk or thin film form, two-dimensional and layered materials can support mechanisms for symmetry breaking between centrosymmetric building blocks, e.g., by creating low-symmetry interfaces in van der Waals stacks. Here, we introduce an approach toward symmetry breaking in van der Waals crystals that relies on the spontaneous incorporation of stacking faults in a nonpolar bulk layer sequence. The concept is realized in nanowires consisting of Se-rich group IV monochalcogenide (GeSe 1–x S x ) alloys, obtained by vapor–liquid–solid growth. The single crystalline wires adopt a layered structure in which the nonpolar A-B bulk stacking along the nanowire axis is interrupted by single-layer stacking faults with local A-A' stacking. Density functional theory explains this behavior by a reduced stacking fault formation energy in GeSe (or Se-rich GeSe 1–x S x alloys). Computations demonstrate that, similar to monochalcogenide monolayers, the inserted A-layers should show a spontaneous electric polarization with a switching barrier consistent with a Curie temperature above room temperature. Second-harmonic generation signals are consistent with a variable density of stacking faults along the wires. Here, our results point to possible routes for designing ferroelectrics via the layer stacking in van der Waals crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Supply Chain Cybersecurity Recommendations for Solar Photovoltaics

Solar photovoltaic (PV) cybersecurity is a growing field of research. As deployments of solar PV has increased, cyber risk has also increased. However, utility solar PV installations are not required to comply with North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) unless they meet a minimum generation threshold of 75 Megawatts (MW). Individual residential scale solar PV deployments will not meet that generation threshold and are therefore excluded from NERC CIP requirements. With most solar installations below 75MW, solar PV has been deployed with minimal oversight and highly variable cybersecurity maturity. The resources that make up the digital supply chain can include software, code, data, as well as other digital components. However as clean energy technology advances, cybersecurity threats and vulnerabilities continue to evolve and grow in sophistication. Solar PV faces a unique challenge in which it can be deployed in residential buildings and purchased by a consumer directly. This makes the supply chain of PV a unique challenge, where responsible parties for cybersecurity vary widely depending on the type of solar PV being deployed. Supply chain cybersecurity for solar PV represents a critical area for ensuring safe operations as the U.S. moves towards a clean energy future.

14 SOLAR ENERGY↗
DOE OSTI2026

High-Resolution WRF-Based Downscaling of Earth System Model Projections for Energy Applications across CONUS

Evaluating energy resources under future scenarios requires meteorological information that adequately resolves regional-scale variability and is suitable for regional energy system studies. Although Earth system model (ESM) outputs provide essential large-scale context, their coarse resolution and inherent biases limit direct use in energy system applications. This work presents a high-resolution dynamical downscaling framework using the Weather Research and Forecasting (WRF) model to generate energy-relevant regional fields for future scenarios over the contiguous United States (CONUS). The framework first identifies an optimal WRF configuration through sensitivity experiments, then evaluates raw and bias-corrected ESM initial and boundary conditions, with soil moisture and soil temperature bias correction implemented as an integral component of the bias-corrected ESM atmospheric forcing prior to WRF dynamical downscaling to improve land-atmosphere interactions. Simulations performed at 4-km resolution show that uncorrected ESM forcing leads to systematically dry and cold soil states, which propagate into elevated near-surface air temperature and solar irradiance biases, particularly during summer for the period 2000-2014. Incorporating bias-corrected atmospheric forcing together with soil state bias correction substantially reduces these errors and improves the representation of surface energy processes in WRF simulations. The results highlight the importance of bias-aware initialization strategies in high-resolution dynamical downscaling for future energy system analysis and planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

DER Digital Supply Chain Gap Analysis

Solar photovoltaic (PV) cybersecurity is a growing field of research. As deployments of solar PV have increased, cyber risk has also increased. Utility solar PV installations, however, are not required to comply with the North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) plan unless they meet a minimum generation threshold of 75 MW. Individual residential-scale solar PV deployments will not meet that generation threshold and are therefore excluded from the NERC CIP requirements. With most solar installations less than 75 MW, solar PV has been deployed with minimal oversight and highly variable cybersecurity maturity. The resources that comprise the digital supply chain can include software, code, data, and other digital components. But as clean energy technologies advance, cybersecurity threats and vulnerabilities continue to evolve and grow in sophistication. Supply chain cybersecurity represents a critical area for ensuring safe operations as the U.S. moves toward a clean energy future.

cybersecurity↗
DOE OSTI2023

Striped Jets in Post–Neutron Star Merger Systems

Abstract Models invoking magnetic reconnection as the particle acceleration mechanism within relativistic jets often adopt a gradual energy dissipation profile within the jet. However, such a profile has yet to be reproduced in first-principles simulations. Here we perform a suite of 3D general relativistic magnetohydrodynamic simulations of post–neutron star merger disks with an initially purely toroidal magnetic field. We explore the variations in both the microphysics (e.g., nuclear recombination, neutrino emission) and system parameters (e.g, disk mass). In all of our simulations, we find the formation of magnetically striped jets. The stripes result from the reversals in the poloidal magnetic flux polarity generated in the accretion disk. The simulations display large variations in the distributions of stripe duration, τ , and power, 〈 P Φ 〉. We find that more massive disks produce more powerful stripes, the most powerful of which reaches 〈 P Φ 〉 ∼ 10 49 erg s −1 at τ ∼ 20 ms. The power and variability that result from the magnetic reconnection of the stripes agree with those inferred in short-duration gamma-ray bursts. We find that the dissipation profile of the cumulative energy is roughly a power law in both radial distance, z , and τ , with a slope in the range of ∼1.7–3; more massive disks display larger slopes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Regional Real-Time PV Spinning Reserve Estimator

Curtailed photovoltaic (PV) generation is a zero-marginal-cost spinning reserve that can be used for a number of active power control services. Unlike traditional spinning reserve providers, however, i.e., fossil-fueled generators, which have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate knowledge of the PV PHL is essential. It ensures that enough headroom is reserved by the PV plants to deliver the award services in real time and informs feasible dispatch decisions made by the market operator. To tackle this challenge, a novel reference-control grouping-based PV plant reserve estimation method has been proposed by the National Renewable Energy Laboratory under past projects funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The estimation method separates inverters within a plant into two groups: a control group and a reference group. While the reference group is reserved to operate at its PHL, the control group can be curtailed to provide the grid services. Real-time outputs from the reference inverters are used to estimate the PHL for the whole plant based on the ratio between capacities of the reference group and of the plant. This work further enhances the methodology by (1) improving the model accuracy through machine learning; (2) automating the reference inverter selection through correlation analysis; (3) considering estimation look-ahead windows; and (4) applying to regional spinning reserve estimation. Significant performance improvement has been observed based on real-world data collected by CAISO, Southern Company, and Terabase Energy. Compared with the original scaling method, the newly proposed machine learning-based approach reduces the estimation errors by 30% and 13% at the plant level and region level, respectively. Results obtained from this project are intended to be used by grid operators, market operators, balancing authorities, and PV plant owners and operators to facilitate PV participation in ancillary service markets. Regulators, policymakers, and system planners can also consider the results of this work in their decision-making processes. In addition to the performance improvement on the existing reference-control based grouping method, we also investigated how the variability of PV generation from a single PV inverter can be used to represent the variability of PV generation at the plant level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A transported Livengood-Wu integral model for knock prediction in CFD simulation

This work describes the development of a transported Livengood-Wu (L-W) integral model for computational fluid dynamics (CFD) simulation to predict auto-ignition and engine knock tendency. The currently employed L-W integral model considers both single-stage and two-stage ignition processes, thus can be generally applied to different fuels such as paraffin, olefin, aromatics and alcohol. The model implementation is first validated in simulations of homogeneous charge compression ignition combustion for three different fuels, showing good accuracy in prediction of auto-ignition timing for fuels with either single-stage or two-stage ignition characteristics. Then, the L-W integral model is coupled with G-equation model to indicate end-gas auto-ignition and knock tendency in CFD simulations of a direct injection spark ignition engine. This modeling approach is about 10 times more efficient than the ones that based on detailed chemistry calculation and pressure oscillation analysis. Two fuels with same Research Octane Number (RON) but different octane sensitivity are studied, namely Co-Optima Alkylate and Co-Optima E30. Feed-forward neural network model in conjunction with multi-variable minimization technique is used to generate fuel surrogates with targets of matched RON, octane sensitivity and ethanol content. The CFD model is validated against experimental data in terms of pressure traces and heat release rate for both fuels under a wide range of operating conditions. The knock tendency indicated by the fuel energy contained in the auto-ignited region of the two fuels at different load conditions correlates well with the experimental results and the fuel octane sensitivity, implying the current knock modeling approach can capture the octane sensitivity effect and can be applied to further investigation on composition of octane sensitivity.

Yue, Zongyu↗
DOE OSTI2026

GeneratorSE.jl

SAND2026-22941O GeneratorSE.jl is a Julia software package for analytical sizing of variable-speed wind turbine generators. It translates and maintains generator sizing methods from the NREL WISDEM GeneratorSE framework in a Julia package form. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Michelen Strofer, Carlos [Sandia National Lab. (SN↗
DOE OSTI2024

iButton and Tinytag snow/ground interface temperature measurements at Teller 27 and Kougarok 64 from 2022-2023, Seward Peninsula, Alaska

Snow/ground interface temperature measurements were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) and at the Kougarok Road Site at mile marker 64 (KG_MM64) on the Seward Peninsula, Alaska. Data were collected between October 1, 2022 to September 18, 2023 using iButton Link DS1921G-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g) and Tinytag TGP-4017 internal sensors (https://www.micronmeters.com/product/tgp-4017-internal-sensor-40-to-85-c-40-f-to-185-f) deployed across the Kougarok and Teller sites. These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 4 hours, while Tinytag data were collected every 30 minutes. In total, data were collected from 196 iButtons and 26 Tinytags. This dataset contains four *.csv files of near-ground surface temperatures at various locations throughout each study site and two *.kml files of sensor locations. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Sensors were placed both inside and outside of vegetation to better capture the spatial variability of snow properties across each domain.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluation of Hybrid FPOG Applications in Regulated and Deregulated Markets Using HERON

Recent changes in the U.S. energy market, such as low natural gas prices and increased electricity production for variable renewable energy (VRE) sources, have led to an economic crisis for existing light-water reactor (LWR) nuclear power plants (NPP). Many owners and operators of LWRs have elected to decommission these plants rather than continue using them as consistent sources of clean baseload power. This has led to exploration of various possibilities to increase the economic viability of these units, including market restructuring to monetize benefits LWRs already provide to the grid through ancillary markets, load following and economic dispatch, and possible integration of secondary systems directly to the NPP for production of additional products through technologies such as hydrogen electrolysis or water desalination. Previous studies have considered the technologies associated with these Integrated Energy Systems (IES) activities, and the analysis of markets for these secondary products. To analyze the economic viability of various system configurations including IES, especially given the uncertainty surrounding load demand, electricity prices, and the availability of VRE resources, the stochastic technoeconomic analysis package HERON (Heuristic Energy Resource Optimization Network) was released earlier this year as an extension of the risk analysis framework RAVEN (Risk Analysis Virtual Environment). HERON focuses foremost on making the complex uncertainty quantification analysis tools approachable for energy systems analysts, also providing general dispatch optimization algorithms for those workflows. HERON continues to be improved and tested as a significant part of the IES viability analyses performed in this work. HERON is not a capacity expansion model. To consider market and grid energy system development in a variety of scenarios, HERON is best used in coupling with modelling tools such as US-REGEN, which sacrifice some of the uncertainty analysis and resolution of HERON's modelling for the ability to efficiently predict the change in the grid energy system's profile due to economic drivers over decades. HERON can then use this information to explore the economic viability of introducing changes to the predicted outcomes, such as the introduction of an IES. In this work, experts at EPRI using US-REGEN provide six projection scenarios for use in HERON stochastic technoeconomic analysis (STEA) in considering the options available for increasing LWR economic viability through introduction of a hydrogen-centric IES using a high-temperature steam electrolysis plant (HTSE), hydrogen storage, and a constant-rate contracted hydrogen consumer. The results obtained are differential in nature; they do not report expected profits for any configuration, but rather report on the possible increase in the NPV of a configuration with respect to a baseline no-IES configuration. Due to the uncertainty captured in the variable net load of the systems, there is likewise uncertainty in the mean values reported. We consider this viability both in terms of a regulated market, where the energy producers and IES are owned and operated by single entity, as well as a deregulated market, where the IES chooses its bid for electricity generation and is then dispatched by the grid system operator. Results indicate that for deregulated markets, the inclusion of the IES is often statistically beneficial. This is especially true in policies that are not favorable towards nuclear, as nuclear is less often dispatched and is forced to deal with frequent idle capacity. In the nominal case as well as the case of carbon tax policies, inclusion of the IES clearly benefited the economic performance of the NPP. In the regulated case, however, there was a trend towards minimizing the IES, likely due to the optimal sizing performed by US-REGEN of the NPP within the system as well as the lack of penalty for idle capacity at the NPP in the regulated market analyses.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

iButton snow temperature measurements at Teller 27 and Kougarok 64, Seward Peninsula, Alaska, 2019-2020

Snow temperature measurements were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) and at the Kougarok Road Site at mile marker 64 (KG_MM64) on the Seward Peninsula. Data were collected from October 1, 2021 to August 16, 2022 using iButton Link DS1926-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g) and Tinytag TGP-4017 internal sensors (https://www.micronmeters.com/product/tgp-4017-internal-sensor-40-to-85-c-40-f-to-185-f) deployed across the Kougarok and Teller sites. These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 4 hours beginning on October 1, 2021. Tinytag data collection began between October 9, 2021 and October 12, 2021 depending on the specific Tinytag and data were collected every 30 minutes. In total, data were collected from 236 iButtons and 30 Tinytags. This dataset contains four *.csv files of near-ground surface temperatures at various locations throughout each study site and four *.shp files of sensor locations. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Sensors were placed both inside and outside of vegetation to better capture the spatial variability of snow properties across each domain.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗