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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Discovering the Most Severe K-Point Failure Based on Reinforcement Learning: Preprint

Smart devices are essential to ensure the stability of the power grid and resilience to intermittent energy production. However, smart devices can also be the target of cyber adversaries that may exploit false data injection attacks (FDIAs) to induce unstable grid conditions. A practical consideration of FDIA mitigation approaches is addressed here: given a finite available budget, for which smart device should cyber-threat mitigation be deployed first? In this work, this question is answered by identifying the so-called most-sensitive devices, i.e., the devices that, if compromised, can let an adversary induce the most serious grid instabilities. The method proposed utilizes an adversarial reinforcement learning (RL) framework to identify the k-mostsensitive smart devices (here, smart inverters). The adversarial agent can tamper with the compromised inverters' active and reactive operating power setup points, with the goal of maximizing voltage deviations. Numerical results show that the proposed RL method finds the optimal attack scenarios for 1-point failure and the near-optimal solution for the 2-point case. Additionally, the proposed RL method achieves an 8.8 speed-up ratio in running time compared to the brute force method for the 2-point case.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Optimal Power Sharing Speed Compensation in On-road Wireless EV Charging Systems

Dynamic wireless charging of electric vehicles (EV) is an emerging technology with the potential to address range anxiety and reduce the size of batteries or provide charge-sustaining operation. Charging demand for dynamic wireless charging systems (DWCS) varies greatly in response to location-specific traffic behaviors including the number and speed of vehicles. This paper highlights the potential reduction of load variation with speed compensation and simulates opportunities to maximize the number of cars charged concurrently through “power sharing” or altering power output to slower cars while maintaining a maximum delivered energy. An improved sub-minute model for synthetic traffic is proposed to effectively model DWCS load on high speed roadways and a power electronics model is created based on an existing prototype developed by ORNL to investigate the potential for power sharing. Reductions in the average speed of traffic can greatly increase instantaneous DWCS load by as much as 26%, complicating capacity sizing. Parametric studies with a LCC-S power electronics model shows feasible power reduction of more than 20%, while maintaining a maximum achievable efficiency greater than 90%. Speed compensation on a roadway with large speed variation can reduce average power expected by 21% and increase the maximum number of cars charged simultaneously by 30%. The application of power sharing may significantly reduce load variability due to speed, allow for increased car hosting capability, and guarantee a maximum energy delivered.

Lewis, Donovin D.↗
DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2023

Grid Resilient, Self-Powered, Fuel Flexible, High Efficiency Heating System (Final Report)

The Grid Independent High Efficiency System (GIHES) delivers a heating system that operates free from the power grid and provides ultra-low emissions and high efficiency heating for residential and commercial buildings. The project demonstrated two weeks of continuous grid independent operation of a storage water heater equipped with a powered damper. The Thermoelectric Generator (TEG) integrated GIHES technology revolutionizes heating systems to provide grid resilient hot water production, while delivering significant value to the end user by eliminating impacts of power interruption and enhancing comfort, convenience and productivity. The system maximizes the thermal-to-electric conversion efficiency by optimizing location and orientation of the TEGs and durability while lowering system costs. The TEGs generate enough power to charge a battery and run the storage water heater unit with a powered damper uninterrupted by providing the required parasitic electrical power for start-up, shutdown and during idling. Several TEGs were analytically and experimentally evaluated based on the size, surface area and the power being generated before designing and fabricating a complete multi-TEG assembly for integration with the water heater. The GIHES technology can be extended to boilers, furnaces and tankless water heaters and integrated with heat pump to further increase the overall equipment efficiency. The technology has the potential to reduce the amount of dispatch power and peak power plant operation. An advanced burner was designed, developed and 3D printed for fuel flexible operation. The burner was designed to enable tighter integration with TEGs for potentially higher TEG output, should this be necessary. A bench-scale test rig to perform testing of a 200,000 Btu/h burner and capable of handling up to 10% H2 (by volume) was setup at GTI’s laboratory with appropriate safety and controls to ensure smooth and safe operation. Testing of the 3D burner showed < 5 ppm NOx emissions can be achieved for the entire firing range and with different levels of hydrogen blends with natural gas. The burner will enable a more integrated TEG-burner module design for improved system performance, should this be required in the future. Techno-economic analysis (TEA) of the TEG integrated with the water heaters was performed. The testing data and the power output were used to develop the TEA. The analysis for the storage unit with damper showed that the TEG integrated system costs $95. This is much lower than a one-time charge needed to wire and install dedicated power. Discussions with TEG manufacturers and OEM’s will provide more detailed information and methods to further lower these costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Hybrid geothermal-wind integration in the US energy grid

Here, we combine US wind generation—a cheap yet intermittent source of electricity—with the latest geothermal resource estimates to understand the technoeconomic possibilities of pairing enhanced geothermal systems (EGSs) with existing wind farms to develop a hybrid energy system. Using observed generation data from 583 wind farms, generation gaps are quantified and geographically paired with the latest EGS estimates. Results demonstrate that EGS potential within a 1 km 2 footprint can supplement wind generation at 56% of onshore wind farms. Each wind farm can be supplemented by EGSs when 10% of its surface-occupying footprint is available. The cost of EGSs at wind farms is lowest in the western US and southern Texas border and highest in the central US. While further experiments are warranted, a wind + EGS hybrid system offers an opportunity to increase power output from the same land footprint while maximizing the use of existing electrical infrastructure.

EGS↗
DOE OSTI2020

Opportunities for and challenges to further reductions in the “specific power” rating of wind turbines installed in the United States

A wind turbine’s “specific power” rating relates its capacity to the swept area of its rotor in terms of Watt per square meter. For a given generator capacity, specific power declines as rotor size increases. In land-rich but capacity-constrained wind power markets, such as the United States, developers have an economic incentive to maximize megawatt-hours per constrained megawatt, and so have favored turbines with ever-lower specific power. To date, this trend toward lower specific power has pushed capacity factors higher while reducing the levelized cost of energy. We employ geospatial levelized cost of energy analysis across the United States to explore whether this trend is likely to continue. We find that under reasonable cost scenarios (i.e. presuming that logistical challenges from very large blades are surmountable), low-specific-power turbines could continue to be in demand going forward. Beyond levelized cost of energy, the boost in market value that low-specific-power turbines provide could become increasingly important as wind penetration grows.

geospatial modeling↗
DOE OSTI2021

Net-Zero Carbon Microgrids

The microgrid concept has been effective in creating aggregations of distributed energy resources—generation, storage and loads—for resiliency, in the form of energy security. The success of microgrids in bringing energy security to a wide range of customers—from individual residences to commercial and industrial installations to military bases—has been exemplified during power disruptions and extended outages due to extreme weather events, cybersecurity attacks, and equipment failures. Now microgrids have an opportunity to meet the challenges of climate change and contribute to a carbon-free power delivery system. The transition to net-zero starts within microgrids themselves. In fact, today’s microgrids are largely dominated by generators using fossil fuels, natural gas and diesel, with high greenhouse gas emissions. In short, the transition to net-zero means replacing fossil fueled generators with renewable generation in microgrids. This transition is extended by including new dispatchable generation technologies that are 100% carbon-free and that offer additional advantage of a more-dependable and sustainable source of energy and power. Basically, the decarbonization of microgrids requires three elements: 1) maximizing generation from renewable energy resources, 2) management of storage and flexible loads to balance the variability and intermittency of renewable energy resources, and 3) introducing new clean power sources, including hydrogen-based generation and small modular reactors. This report affirms a need for specific focus by governmental agencies at national, regional, and local levels to establish technology, policy, and investment in this area. The intention of the Net-Zero Microgrid (NZM) Program is to inform these constituencies with cross-cutting research and tools for the reduction of GHG in microgrids – to net-zero in the near term eventually to zero in the longer term. The NZM Program is committed to achieving decarbonization for resiliency and for providing clean energy at the local or distribution level, from remote communities to underserved communities, and large industrial and military facilities. The NZM Planning and Design Platform is a core tool to be developed as an early deliverable of the NZM Program because only a fully integrated microgrid-design approach will ensure maximum carbon reduction in energy production.

13 HYDRO ENERGY↗
DOE OSTI2020

Austin Sustainable and Holistic Integration of Energy Storage and Solar PV [Austin SHINES]. Final Report, Version 2

The Austin SHINES project and solution is a software management platform, for an electric grid with a high penetration of dispersed photovoltaic (PV) solar generation sites, which maintains the traditional power quality and reliability associated with grid service. This project developed and deployed the platform as a Distributed Energy Resource Management System (DERMS), engaging multiple advanced controls, to evaluate operation and optimization of a fleet of diverse DER assets, installed at several locations among Austin Energy’s customers and distribution system. The project also produced a methodology to create a replicable DERMS template, adaptable to other regions and market structures. Last, Austin SHINES aimed to demonstrate the solution’s methodology would enable the DER grid ecosystem to serve load at a technical cost (System Levelized Cost of Electricity, or System LCOE) of less than the U.S. Department of Energy SHINES program metric of $0.14/kWh, in a defined boundary, while enabling a high penetration of distributed PV. Research was categorized in 6 reports (Final Deliverables = FD) listed below, with titles and descriptions indicating which area of understanding was investigated: FD-1: System Levelized Cost of Electricity (System LCOE) Methodology The creation and use of the System LCOE to Serve Load metric that encompasses the holistic, system-level costs and benefits of all resources, and enables them to be evaluated based on their ability to support an efficient and low-cost integrated grid ecosystem. FD-2: Software Platform Product Description The creation of new DER control methodologies deployable within a utility-grade software platform that enable DER's to maximize their benefit within a grid, that is capable of serving load enabling a high penetration of distributed PV generation. FD-3: Optimal Design Methodology Optimal design methodologies for individual DER installations that enable utilities to determine the optimal combinations and sizing for individual DER sites. FD-4: Austin SHINES Ownership and Operation Models for DER System Performance A comparison of multiple DER aggregation and ownership methodologies including direct utility control, third-party aggregator, and autonomous. FD-5: Economic Modeling & Optimization A comparison of multiple DER technology mixes and configurations within the distribution system, providing insight into an optimal blend of technologies that best enable the distribution system to serve load at the lowest cost at high penetrations of solar. FD-6: Fielded Assets Deployed DER assets within the Austin Energy SHINES circuits. Austin SHINES provided an opening for state-of-the-art technology products to be deployed, providing a rich opportunity for improving how each of the products perform as stand-alone products, and in concert with other complementary products. The Austin SHINES project comprised of two key metrics for System LCOE: SystemLCOE_SHINES<$0.14/kWh Modeled ΔSystemLCOE_SHINES/ΔSystemLCOE_Base≥20% at same solar penetration The System LCOE calculation uses the costs of the utility-owned infrastructure as it exists today, the cost of the DERs that exist in the system today, and the cost of the purchase of energy from ERCOT wholesale markets over the course of the calendar year. All costs are on an annualized basis. The capital and operating costs are derived from the rate case, which produces a yearly cost. The net cost of energy and services imported to the system is integrated over the test year, as is the load served and solar penetration. The first metric was easily achieved by every scenario considered. The goal was set when the Department of Energy’s SHINES Funding Opportunity Announcement was written in 2015 and was a more difficult target at the time. Due mostly to rapidly declining costs for DERs and the significant decrease in the Electric Reliability Council of Texas (ERCOT) energy market prices, which results in lower net cost of energy purchases, the System LCOE is well below this target for all scenarios considered. A fleet of DERs can assume different mixtures, each of which serves the load at a different LCOE. The optimal mixture of DERs serves load at the smallest System LCOE. The second metric (hereinafter %delta metric) asks that the holistic DERMS controls reduce the incremental cost above the baseline of going to a high solar penetration future by at least 20% as compared to the case of a DER deployment with no sophisticated controls (autonomous). Many comparison sets were created throughout this project. Physical technology was installed for informing utility engineering and testing several types of operational control schemes, through the DERMS. The types of operational control which were compared for valuation of the System LCOE Metric were: Holistic control = using the full suite of the DERMS platform to decide and optimize how/why the systems operate depending on weather, market, and reliability signal input. Autonomous control = a local mode at the asset site, wherein a schedule operates the asset, with visibility into performance only No control = the baseline for comparing value against the other two types of control The types of ownership control included: Direct Utility control = the utility dispatches a signal to each asset Third-Party Aggregator = a third party aggregates a fleet of assets and the utility dispatches one signal for all Autonomous = a local mode is set for operation at the asset site, wherein a schedule operates the asset, with visibility into performance only The types of control methodologies deployable within a utility-grade software platform included: Utility Peak Load Reduction = Lower transmission cost obligation Day-Ahead Energy Arbitrage = Realize economic value through price differential Real-Time Price Dispatch = Realize economic value from real-time price spikes Voltage support = Reduce losses and increase solar generation Distribution Congestion Management = Increase local grid reliability Demand Charge Reduction = Lower customer bills and realize system benefit The fielded assets deployed for the project were: Utility Scale Kingsbery Energy Storage System: 1.5 MW / 3 MWh Li-Ion battery storage Mueller Energy Storage System: 1.75 MW / 3.2 MWh Li-Ion battery storage, 7 Energy Storage Units (250 kW each) La Loma Community Solar: 2.6 MW Commercial Scale Aggregated storage installations at 3 sites, with existing solar (300+ kW): One 18 kW / 36 kWh Li-Ion battery storage Two 72 kW / 144 kWh Li-Ion battery storage Residential Scale Aggregated storage installations: -Six stationary battery storage systems (10 kWh each) at homes with existing solar -One Electric Vehicle installed as Vehicle-to-Grid (V2G) Utility-Controlled Solar via Smart Inverters at 12 homes Autonomously-Controlled Smart Inverters at 6 homes Over the course of the project, Austin SHINES undertook installing more than 3 MW of distributed battery energy storage, smart PV inverters, a DER control platform, and other enabling technologies utilizing customer and utility locations and aggregation models. All of these resources were to be integrated and optimized at the utility level. DER assets and control methodologies were designed to achieve a credible pathway to a System LCOE for energy delivered to load of $0.14//kWh or less by 2020, while maximizing distributed solar generation and maintaining acceptable standards of power quality. The project also established a template for other regions to follow, to maximize the adoption of distributed solar PV in support of an economic and efficient grid. In total, the Austin SHINES project added value to the DER subject area in each layer of integration. From utility, to commercial to residential scales, the sheer hierarchy of communication and coordination was a significant accomplishment in addition to learnings from what these communications revealed was unique to each. Economically, the most effective method demonstrated was the criticality of planning phases. Contingencies and multiple projection scenarios helped guide the project to deploy optimal design as close as feasible, in real world conditions. The project and reports will serve public benefit by outlining specific areas of DER strategy and installation where many stakeholders and needs can be addressed with improved efficiency. Overall, communities and utilities should use the results to guide the increasing options available for powering the grid with DER, renewables, and carbon considerate energy.

14 SOLAR ENERGY↗
DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗
DOE OSTI2024

TEAMER: MADWEC Techno-Economic Analysis

The objective of this project was for the facility to conduct a techno-economic assessment (TEA) of the Maximal Asymmetric Drag Wave Energy Converter (MADWEC), developed by the University of Massachusetts Dartmouth (UMass Dartmouth). MADWEC is used for powering remote monitoring and Autonomous Underwater Vehicle (AUV) charging systems compared to other existing power supply options. The assessment estimates capital expenditures (CapEx), operational expenditures (OpEx), and power performance for 18 scenarios with the purpose of identifying key cost drivers, comparing total system cost, and comparing the power performance of the power supply options in terms of required installed capacity and estimated theoretical annual energy performance. The 18 assessed scenarios include two end-uses: 1) AUV charging and 2) offshore remote monitoring); three power sources: 1) MADWEC), 2) photovoltaic (PV) solar buoy, 3) and traditional battery swapping); and three locations; 1) nearshore, 2) far-offshore, and 3) high-latitude). In addition, other project goals included developing high level installation, operation, and maintenance plans for each scenario. The techno-economic model, created in Microsoft Excel, estimates CapEx, OpEx, and the power performance of each power supply source. The model has a dynamic format that allows custom inputs to accommodate future changes to the systems being assessed. This is a TEA for the MADWEC project, TEAMER RFTS 7 (request for technical support) program.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

A Privacy-Preserving Distributed Control of Optimal Power Flow

Here, we consider a distributed optimal power flow formulated as an optimization problem that maximizes a nondifferentiable concave function. Solving such a problem by the existing distributed algorithms can lead to data privacy issues because the solution information exchanged within the algorithms can be utilized by an adversary to infer the data. To preserve data privacy, in this paper we propose a differentially private projected subgradient (DP-PS) algorithm that includes a solution encryption step. We show that a sequence generated by DP-PS converges in expectation, in probability, and with probability 1. Moreover, we show that the rate of convergence in expectation is affected by a target privacy level of DP-PS chosen by the user. We conduct numerical experiments that demonstrate the convergence and data privacy preservation of DP-PS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Development of Genetic Algorithm Based Multi-Objective Plant Reload Optimization Platform

The U.S. nuclear industry is facing a challenge in maintaining required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects of light-water reactor nuclear power plant operations. Safety can become more economical by using a risk-informed ecosystem, such as the one being developed in the Risk-Informed Systems Analysis Pathway under the U.S. Department of Energy Light Water Reactor Sustainability Program. The Light Water Reactor Sustainability Program promotes a wide range of research and development activities to maximize both the safety and economic efficiency of nuclear power plants through improved scientific understanding, especially given that many plants are now considering second license renewals. The Risk-Informed Systems Analysis Pathway has two main goals: Deploy methodologies and technologies that better represent safety margins and cost and safety factors; Develop advanced applications that enable cost-effective plant operations. The Plant Reload Optimization Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. This report summarizes genetic-algorithm-based multi-objective fuel reload optimization activities, specifically: Developing the non-dominated sorting genetic algorithm II optimizer in the Risk Analysis and Virtual ENviroment (RAVEN); Demonstrating and validating the developed non-dominated sorting genetic algorithm II optimizer using benchmark optimization problems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Energy Arbitrage: Comparison of Options for use with LWR Nuclear Power Plants

Arbitrage is the opportunistic buying and selling of a commodity during local pricing valleys and peaks respectively to maximize economic value. This report evaluates options for energy arbitrage integrated with existing light water reactor (LWR) nuclear power plants (NPPs) where nuclear energy could be stored in a variety of forms and later recovered to generate electrical power during periods when grid electricity demand and pricing are high. The forms of energy storage examined in this report include the potential value of batteries, hydrogen, and thermal energy storage for coupling with nuclear power. Various large demand response options are also analyzed, including the production of liquid nitrogen via air separation and liquefaction, liquefaction of hydrogen, compressed hydrogen, and the cryogenic capture of CO2. Demand response refers to dispatchable loads that can cycle up or down depending on-grid electricity demand to aid in balancing the grid. Large demand response options could dispatch to aid nuclear power stations in avoiding power turndowns by providing an alternate disposition for electrical energy by producing marketable products (e.g., liquid nitrogen, hydrogen, or captured CO2). Static conditions were chosen and analyzed in this report for each option. Dynamic operation or optimization of energy arbitrage or demand response are out of scope for this report. The analysis is based on storage systems with discharge capacities of 500 MW for which various durations of storage and costs of charging (electricity cost) are examined. While the value of thermal energy to an industrial user for flexible plant operations has been previously proven as a business case, this report evaluates costs of hydrogen energy storage and leading thermal energy storage options, and large demand response loads that could be integrated with LWRs in comparison to utility-scale battery storage for use of off-peak nuclear energy. Compilation of this information will be used by the Idaho National Laboratory (INL) RAVEN/HERON systems integration and economics tool to evaluate thermal energy dispatch to industrial users. Relative ranking of energy storage options was done using a levelized cost of storage (LCOS) metric which calculates a rough breakeven cost for the system, taking into account the capital and operating costs as well as the revenue from arbitrage. Table ES1 below shows the LCOS for each of the energy storage options considered. First, in the table, lithium iron (Fe) phosphate batteries are listed as the base case for comparison against the other options. Next is hydrogen storage where most of the hydrogen analyses assumed the hydrogen to be produced using solid oxide electrolytic cell (SOEC) high temperature steam electrolysis (HTSE). The others used existing models of polymer electrolyte membrane (PEM) low temperature electrolysis to produce hydrogen. HTSE performance parameters and costs were taken from existing INL models. Various means were assumed to convert the hydrogen to electricity, including PEM fuel cells (FCs) and a gas turbine mixed in a 30 vol% mixture with natural gas. Physical storage (pressure vessels) and geological storage (natural underground features) were used to store the hydrogen as noted. Geological storage is more economical, but the locations are limited because of the requirement for pre-existing geological formations that will support storage. Thermal energy storage (TES) options were also analyzed including electro-thermal energy storage (ETES) and four different liquid sensible heat TES storage media as noted (Hitec, Hitec XL, Therminol-66, and Dowtherm A). The ETES process considered was modified using existing public documentation on an Echogen process and uses a separate supercritical CO2 charge and discharge cycle with sand as the heat storage media.

25 ENERGY STORAGE↗
DOE OSTI2021

Hall–Petch strengthening limit through partially active segregation in nanocrystalline Ag-Cu alloys

The breakdown from grain-size strengthening to softening mechanisms is generally well understood for high-purity nanocrystalline materials when the mean grain size decreases to the nanometer range. In nanocrystalline alloys, however, the stabilization of nanosized grains by grain-boundary solute segregation complicates the above mechanisms. Moreover, current segregation models have little predictive power for determining the optimal solute content that maximizes Hall-Petch strengthening effects. In this article, using large-scale hybrid Monte-Carlo/molecular dynamic simulations, we present a systematic study of the Hall–Petch breakdown in Cu-segregated Ag alloys with grain sizes ranging from 8 nm to 59 nm, where three concentration-dependent regimes of plasticity are described: (1) Classical segregation strengthening behavior at low solute contents, (2) shear band-induced softening at high solute contents, and (3) a previously unknown, but extended plateau of maximum strengths for intermediate solute contents from 4 to 15 at.%, which we term as nanocrystalline Sterling silver. We find that flow strengths in nanocrystalline Sterling alloys naturally exhibit a zero-slope limit at the smallest grain sizes that is well below the ideal Hall–Petch strengthening trend. This phenomenon results from partially active grain-boundary segregation that acts to influence interfacial plasticity in some, but not all, grain boundary regions. Our findings amplify the atomic nature of solute segregation and interaction at grain boundaries and its complex roles on grain boundary-mediated plasticity mechanisms in nanocrystalline alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Synthetic antiferromagnet-based spin Josephson oscillator

Two easy-plane ferromagnetic layers, antiferromagnetically coupled through a thin nonmagnetic metal layer, form the magnetic analog of a Josephson junction. Additionally, a current driven spin chemical potential drives a 2π precession of the in-plane magnetization of each ferromagnet. The participation of the full magnetic moment in the 2π precession maximizes the giant magnetoresistance and the ac output power. The frequency can be continuously tuned by a dc bias. An applied ac bias results in a time-averaged magnetoresistance with Shapiro-like steps. The multistate mode-locking behavior exhibited by the Shapiro steps may be exploited for applications such as microwave detectors and neuromorphic computing. They may also serve as an experimental signature of spin superfluidity.

42 ENGINEERING↗
DOE OSTI2024

Vertical instability forecasting and controllability assessment of multi-device tokamak plasmas in DECAF with data-driven optimization

Abstract Reliable vertical position control will be an essential element of any future tokamak-based fusion power plant in order to reduce disruptions and maximize performance. We investigate methods to improve vertical controllability boundary determination in plasma operational space and demonstrate a data-driven approach based on direct pseudoinversion of operational space data that is rigorously quantitative, applicable in real-time plasma control systems, and physically intuitive to interpret. Applied to historical shot data from entire run campaigns on the MAST-U, KSTAR, and NSTX tokamaks, this approach, implemented in DECAF, improves vertical displacement event identification accuracy to 98.9%–100%. Further, we explore the application of a physics-based vertical stability metric as an early warning forecaster for vertical displacement events. The development of a linear surrogate model for the plasma current density profile, with a coefficient of determination of 0.992 on the training dataset, enables potential employment of this forecaster in real-time. The application of this approach on historical data from the MAST-U MU02 campaign yields a forecaster with 62.6% accuracy, indicating promise for this method when further refined and potentially coupled with other stability metrics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

5G Enabled Transformative Co-design and Co-simulation Framework for Grid Decarbonization and Modernization

5G is a breakthrough technology to enable a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for clean energy economy and grid modernization. The clean energy transformation not only requires innovative manufacturing, algorithm, application, and cross-domain co-simulation, but also a systematic evaluation and comprehensive support on the microelectronic and architectural computing customization to enable and maximize the high renewable penetration in the future power grid. The co-design of decarbonized grid, reliable yet efficient communication, and affordable yet ubiquitous computing will unlock the potential of almost any available tools for the undergoing clean energy transformation. 5G is also an opportunity to rethink the paradigm of infrastructure planning, as computing will be one of the core services for society alongside electricity and communication services.

5G↗