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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 OSTI2016

Benchmark of Spacer Grid Models used in Nuclear Power Plants

The fuel bundles of both PWR and BWR reactors require spacers to maintain the integrity and spacing of the slender fuel rods. These spacers can provide benefits such as increased mixing and turbulence, which increases heat distribution, leading to better Departure from Nucleate Boiling (DNB) conditions in PWRs. They also enhance the critical power ratios by increasing the thickness of the liquid layer in BWRs. However, the spacers also cause a pressure drop which must be engineered into the performance and operation. The flow within a rod bundle involves a number of complicated factors, such as cross flow, (de)entrainment of liquid layer, bubble or droplet break up/join. This requires specialized 3 dimensional computational fluid dynamics for the complete flow, however, most of the newly developed models have been accomplished by industry, so they are proprietary and in addition require extensive programming and cost to implement. In the public domain, a number of 1 dimensional equations have been developed that could benefit in initial design and decision making, if the equations are valid in the new and different applications the models are used. In 2002, an OECD/NRC BFBT benchmark workshop determined the variables used in the development of models, to predict flow within nuclear fuel bundles, had not been updated with technological advances to replace general theory and characteristics. If those properties could be identified and modeled, they could be applied to a wider range of conditions. This study utilizes NUPEC BFBT tests' measured data, on an average spacer over a range of pressure and flows, which was determined to be the most detailed measured data available. The focus is to describe the mathematical models of spacer grids and validate them with experimental results. This study consists of compiling and comparing available and established single phase equations and models computations. While these models may not have the desired degree of accuracy for final decisions, they may be accurate enough for the initial phases of design. No conclusion on a universal equation that fits all boundary conditions accurately or detailed effect of properties was determined. However, by using the BFBT experimental data, boundary conditions where the spacer grid models can be useful were identified. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics

Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hydrodynamic models (RHMs) or data-driven models with formidable training costs, whereas data-driven modeling of flow velocity has rarely been explored. Here, using the hybrid Low-fidelity, Spatial analysis, and Gaussian process learning (LSG) model, we developed a downscaling approach to construct high-resolution flow depth and velocity from a two-dimensional (2-D) RHM simulation at coarse resolution. The LSG models were trained and tested in an urban watershed in Houston using two different hurricane-driven flood events. The high-resolution (as fine as 30 m resolution) and low-resolution (mostly 1000 m resolution) meshes include 664 724 and 14 536 grid cells, respectively. The results showed that through downscaling, the simulation errors were reduced to less than one-fourth and one-third of the errors of the low-resolution 2-D RHM for flow depth and velocity, respectively. Our analysis further revealed that the dominant uncertainty sources of the downscaled hydrodynamics are different, with flow velocity dominated by the dimensionality reduction error, which we reduced by using a regionalized training procedure. The downscaling approach achieves an 84-fold acceleration in computational time compared to the high-resolution 2-D RHM, making high-fidelity ensemble flood modeling feasible. More importantly, the developed method provides an opportunity to couple large-scale hydrodynamical processes with local physical, chemical, and biological processes in river models.

Tan, Zeli [Pacific Northwest National Laboratory (↗
DOE OSTI2025

Future grid mix impacts on whole-building life cycle assessment

Building construction and operation are a significant contribution to global greenhouse gas emissions, so understanding and mitigating emissions is crucial for reliable and realistic emissions accounting. Whole-building Life Cycle Assessment (WBLCA) is an emissions accounting method that considers lifetime environmental impacts of a building during its construction, operation, and eventual end-of-life. When performing WBLCAs, emission calculations from the building's operation over the entire building lifespan are typically based on today's energy grid mixes. This method does not consider changes or advancements in the clean energy proportion within the grid mix and can over or under-inflate results, skewing the ratio of embodied vs. operational environmental impacts. While a variety of prediction tools estimate what future grid emissions might be, predictions can vary widely. To predict the clean energy ratio within future grid mixes and the potential impact these changes might have on WBLCA, annual data from several existing U.S. grid models was averaged and probabilistic modeling was used to extend the usable projections of shorter forecasts. Results show that clean energy sources will likely continue to increase over time, although the rate of growth varies by model. On average, by 2085, the clean energy penetration of the grid is projected to reach ~81% and renewable energy is projected to reach ~71%, although no widespread consensus is reached. To understand how the future grid mix impacts lifetime building emissions within a WBLCA context, the team analyzed two 2021 IECC-compliant all-electric residential buildings: one built from traditional materials and construction processes and the other built with carbon sequestering materials and modular assembly, with a portion of energy generated on site. The results indicate that a moderate estimate of future electricity grid mixes shows a reduction of yearly operational emissions for traditional residential buildings of 55% between 2025 and 2085, and a corresponding reduction of 48% of total emissions over a 60 year building lifespan. This study offers a nuanced approach to account for the variability of future grid mix models and provides an average trend-line based on a robust collection of scenarios.

Life Cycle Assessment (LCA)↗
DOE OSTI2016

SU-F-T-236: Comparison of Two IMRT/VMAT QA Systems Using Gamma Index Analysis

Purpose: The goal of this study is to assess differences in the Gamma index pass rates when using two commercial QA systems and provide optimum Gamma index parameters for pre-treatment patient specific QA. Methods: Twenty-two VMAT cases that consisted of prostate, lung, head and neck, spine, brain and pancreas, were included in this study. The verification plans have been calculated using AcurosXB(V11) algorithm for different dose grids (1.5mm, 2.5mm, 3mm). The measurements were performed on TrueBeam(Varian) accelerator using both EPID(S1000) portal imager and ArcCheck(SunNuclearCorp) devices. Gamma index criteria variation of 3%/3mm, 2%/3mm, 2%/2mm and threshold (TH) doses of 5% to 50% were used in analysis. Results: The differences in Gamma pass rates between two devices are not statistically significant for 3%/3mm, yielding pass rate higher than 95%. Increase of lower dose TH showed reduced pass rates for both devices. ArcCheck’s more pronounced effect can be attributed to higher contribution of lower dose region spread. As expected, tightening criteria to 2%/2mm (TH: 10%) decreased Gamma pass rates below 95%. Higher EPID (92%) pass rates compared to ArcCheck (86%) probably due to better spatial resolution. Portal Dosimetry results showed lower Gamma pass rates for composite plans compared to individual field pass rates. This may be due to the expansion in the analyzed region which includes pixels not included in the separate field analysis. Decreasing dose grid size from 2.5mm to 1.5mm did not show statistically significant (p<0.05) differences in Gamma pass rates for both QA devices. Conclusion: Overall, both system measurements agree well with calculated dose when using gamma index criteria of 3%/3mm for a variety of VMAT cases. Variability between two systems increases using different dose GRID, TH and tighter gamma criteria and must be carefully assessed prior to clinical use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

An AI-driven framework for evaluating local and state authorities’ permitting processes

The demand for new energy infrastructure is increasing across the United States, but heterogenous permitting processes and embedded requirements across different local jurisdictions can cause project delays, increase “soft costs,” and hinder developer expansion. This study analyzes the variability in local permitting requirements across the U.S. and develops a quantitative approach to describe their clarity and effectiveness in enabling infrastructure project development. By using an Energy Language Model (ELM), a large language model (LLM) for energy technologies, we systematically gathered permitting information from nearly 300 state-, county-, and city-level documents, creating a structured dataset of requirements and procedures on an unprecedented scale and speed. Our analysis revealed that local (city and county) permitting requirement documents are underrepresented compared to state-level guidance documents, which can impede timely and cost-effective installation of new electric infrastructure. Our validation process showed that the final database has an accuracy of approximately 95%. We, further, created a new quantitative method to score permitting requirements for clarity and efficiency, with electric vehicle supply equipment as an initial use case. The average local permitting document scored a 1.8 out of 5, which we interpret as meaning that half of the requirements developers face when installing electric infrastructure are ambiguous, increasing both cost and time. We also created a “Generalized Permit Process”, highlighting common procedural steps and identifying specific opportunities for municipalities to improve their documentation. This research establishes a systematic and scalable framework for evaluating the complexities of local infrastructure permitting processes by combining LLM-powered data collection and quantitative scoring. The framework enables policymakers and developers to identify and mitigate procedural bottlenecks, with the expectation that these improvements can accelerate application review and approval, reduce project costs, and expedite connection to utility distribution grids. As a foundational approach for streamlining local project development processes, this study’s methods are intended to be extended to a wide range of energy applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Geochemistry & Geophysics↗
DOE OSTI2022

Machine Learning Derived Dynamic Operating Reserve Requirements in High-Renewable Power Systems

Accurately forecasting wind and solar power output poses challenges for deeply decarbonized electricity systems. Grid operators must commit resources to provide reserves to ensure reliable operations in the face of forecast errors, a process which can increase fuel consumption and emissions. We apply neural network-based machine learning to expand the usefulness of median point forecast data by creating probabilistic distributions of short-term uncertainty in demand, wind, and solar forecasts that adapt to prevailing grid conditions. Machine learning derived estimates of forecast errors compare favorably to estimates based on incumbent methods. Reserves derived from machine learning are usually smaller than values derived using incumbent methods, which enables fuel savings during most hours. Machine learning reserves are generally larger than incumbent reserves during times of higher forecast error, potentially improving system reliability. Performance is tested using multi-stage production simulation modeling of the California Independent System Operator (CAISO) system. Machine learning reserves provide production cost and greenhouse gas (GHG) emission reductions of approximately 0.3% relative to historical 2019 requirements. Savings in the 2030 timeframe are highly dependent on battery storage capacity. At lower levels of battery capacity, savings of 0.4% from machine learning reserves are shown. Significant quantities of battery storage are expected to be added to meet California's resource adequacy needs and GHG reduction targets. Addition of these batteries saturate reserve needs and results in minimal within-hour balancing costs in 2030.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Asynchronous space–time domain decomposition method with localized uncertainty quantification

The computational cost associated with uncertainty quantification of engineering problems featuring localized phenomenon can be reduced by confining the random variability of the model parameters within a region of interest. Here in this case, a localized treatment of mesh and time resolutions is required to capture the effect of the confined material uncertainty on the global response. We present a computational approach for localized uncertainty quantification with the capability of asynchronous treatment of mesh and time resolutions. In particular, we allow each subdomain to have its local uncertainty representation and the corresponding mesh and time resolutions. As a result, computing resources can be directed toward a small region of interest where a model with high spatial and temporal resolutions is required. To verify the numerical implementation, we consider elastic wave propagation in an axially loaded beam. Moreover, we perform convergence studies with respect to the spatial and temporal discretizations as well as the size of an uncertain subdomain. A projectile impacting a composite sandwich plate is considered as an engineering application for the proposed method.

42 ENGINEERING↗
DOE OSTI2023

Fabrication, Modeling, and Testing of a Prototype for Particle Thermal Energy Storage Containment

Increasing penetration of variable renewable energy resources requires the deployment of energy storage at a range of durations. Long-duration energy storage (LDES) technologies will fulfill the need to firm variable renewable energy resource output throughout the year. Conventional electrochemical batteries (e.g., lithium-ion) are uneconomical in this role due to high energy capacity costs. Thermal energy storage (TES) is one promising technology for LDES applications because of its siting flexibility and ease of scaling. Particle-based TES systems use low-cost solid particles that have higher temperature limits than the molten salts used in traditional concentrated solar power systems. A key component in particle-based TES systems is the containment silo for the high-temperature (> 1100 degrees C) particles. This study combined experimental testing and computational modeling methods to design and characterize the performance of a particle containment silo for LDES applications. A containment silo prototype was built at a laboratory scale and used to validate a congruent transient finite element analysis (FEA) model. The validation compared the actual and predicted temperature profile through the prototype over six days as the particles cooled from their initial temperature. The performance of a commercial scale (> 5 GWhth) was then characterized using the validated model. The transient FEA model was subject to several charge-discharge cycles to mimic a possible operating schedule. The commercial-scale model predicted a storage efficiency in excess of 95% after five days of storage with a design storage temperature of 1200 degrees C. Insulation material and concrete temperature limits were considered as well. The validation of the methodology means the FEA model can simulate a range of scenarios for future applications. This work supports the development of a promising LDES technology with implications for grid-scale electrical energy storage, but also for thermal energy storage for industrial process heating applications.

ENERGY STORAGE↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗
DOE OSTI2016

The Second Assessment of the Effects of Climate Change on Federal Hydropower

Hydropower is a key contributor to the US renewable energy portfolio due to its established development history and the diverse benefits it provides to the electric power system. Ensuring the sustainable operation of existing hydropower facilities is of great importance to the US renewable energy portfolio and the reliability of electricity grid. As directed by Congress in Section 9505 of the SECURE Water Act (SWA) of 2009 (Public Law 111-11), the US Department of Energy (DOE), in consultation with the federal Power Marketing Administrations (PMAs) and other federal agencies, has prepared a second quinquennial report on examining the potential effects of climate change on water available for hydropower at federal facilities and on the marketing of power from these federal facilities. This Oak Ridge National Laboratory (ORNL) Technical Memorandum, referred to as the 9505 assessment, describes the technical basis for the report to Congress that was called for in the SWA. To evaluate the potential climate change effects on 132 federal hydropower plants across the entire US, a spatially consistent assessment approach is designed to enable an interregional comparison. This assessment uses a series of models and methods with different spatial resolutions to gradually downscale the global climate change signals into watershed-scale hydrologic projections to support hydropower impact assessment. A variety of historic meteorological and hydrologic observations, hydropower facility characteristics, and geospatial datasets is collected to support model development, calibration, and verification. Among most of the federal hydropower plants throughout the US, the most important climate change effect on hydrology is likely to be the trend toward earlier snowmelt and change of runoff seasonality. Under the projections of increasing winter/spring runoff and decreasing summer/fall runoff, water resource managers may need to consider different water use allocations. With the relatively large storage capacity in the most of the US federal hydropower reservoirs, the system is likely to be able to absorb part of the runoff variability and hence may continue to provide stable annual hydropower generation in the projected near-term and midterm future periods. Nevertheless, the findings are based on the assumption that there is no significant change in the future installed capacity and operation. The issues of aging infrastructures, competing water demand, and environmental requirements may reduce the system s ability to mitigate runoff variability and increase the difficulty of future operation. These issues are not quantitatively analyzed in this study. This study presents a regional assessment at each of the eighteen PMA study areas. This generalized approach allows for spatial consistency throughout all study areas, enabling policymakers to evaluate potential climate change impacts across the entire federal hydropower fleet. This effort is expected to promote better understanding of the sensitivity of federal power plants to water availability and provides a basis for planning future actions that will enable adaptation to climate variability and change.

13 HYDRO ENERGY↗
DOE OSTI2026

High-Resolution South American Wind Resource Data Downscaled with Generative Machine Learning Conditioned on Near-Surface Observations

High-resolution historical wind data was developed for the entirety of South America using the innovative Super-Resolution for Renewable Resource Data (sup3r) machine learning framework. The publicly available Sup3rWind South America dataset represents a significant advancement in wind resource data generation, leveraging generative machine learning conditioned on near-surface observations from the Meteorological Assimilation Data Ingest System (MADIS) to efficiently and accurately downscale coarse reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5). This approach produces fine-scale, spatially and temporally coherent wind and meteorological fields hundreds of times more computationally efficient than traditional numerical weather modeling methods, enabling access to high-fidelity wind information across both continental and offshore regions. Sup3rWind South America builds on the earlier Sup3rWind Ukraine dataset through improvements in model architecture and outputs conditioned on near-surface observation inputs. As with the Ukraine data release, this dataset includes wind speed, wind direction, temperature, relative humidity, and pressure at a horizontal resolution of ~2 km, representing a 15x spatial enhancement relative to the 31 km ERA5 grid. Wind speed and direction are provided at 5-minute resolution, a 12x temporal refinement compared to the hourly ERA5 data, while temperature, relative humidity, and pressure remain at hourly resolution. The data covers all years from 2005 to 2024. Before downscaling, ERA5 inputs were bias-corrected using long-term monthly means and a limited number of quality-controlled observations to align large-scale statistics with regional conditions. The resulting dataset is the first publicly available high-resolution timeseries wind record that provides full spatial coverage of South America. Model validation demonstrates strong agreement with observations across several statistical metrics, consistent with other state-of-the-art high-resolution wind resource datasets. The potential applications of Sup3rWind South America span renewable energy resource assessment, energy system modeling, and grid resilience analysis. The 20-year record and high spatial and temporal resolution support accurate estimation of long-term energy yield and the economic feasibility of potential wind development sites. Continuous coverage across both continental and offshore regions enables comprehensive site prospecting within exclusive economic zones. The 2 km, 5-minute resolution data provide the spatial and temporal variability required for power system simulation, operational planning, and regional risk assessments.

17 WIND ENERGY↗
DOE OSTI2016

Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part I: Wave Energy

Marine hydrokinetic energy is a promising and growing piece of the renewable energy sector that offers high predictability and additional energy sources for a diversified energy economy. This report investigates the market opportunities for wave energy along the U.S. coastlines. It is part one of a two-part investigation into the United State's two largest marine hydrokinetic resources (wave and tidal). Wave energy technology is still an emerging form of renewable energy for which large-scale, grid-connected project costs are currently poorly defined. Ideally, device designers would like to know the resource conditions at economical project sites so they can optimize device designs. On the other hand, project developers need detailed device cost data to identify sites where projects are economical. That is, device design and siting are, to some extent, a coupled problem. This work describes a methodology for identifying likely deployment locations based on a set of criteria that wave energy experts in industry, academia, and national laboratories agree are likely to be important factors for all technology types. This work groups the data for the six criteria into 'locales' that are defined as the smaller of either the local transmission grid or a state boundary. The former applies to U.S. islands (e.g., Hawaii, American Samoa) and rural villages (e.g., in Alaska); the latter applies to states in the contiguous United States. These data are then scored from 0 to 10 according to scoring functions that were developed with input from wave energy industry and academic experts. The scores are aggregated using a simple product method that includes a weighting factor for each criterion. This work presents two weighting scenarios: a long-term scenario that does not include energy price (weighted zero) and a near term scenario that includes energy price. The aggregated scores are then used to produce ranked lists of likely deployment locales. In both scenarios, Hawaii and the Pacific Northwest (northern California, Oregon, and Washington) rank at the top of the lists. Hawaii ranks highest in the near-term scenario because it has high energy costs. In the long-term scenario, Oregon ranks highest because it has a large market and an energetic resource. Several East Coast states and Puerto Rico are also identified as potential wave energy deployment sites if technological innovations make it possible to efficiently generate electricity from the modest resource there. There are also several small-market sites in Alaska and U.S. Pacific Islands that rank particularly well in the near-term analysis due to their high energy prices. These locations may represent opportunities to demonstrate economical wave energy conversion as a stepping-stone to larger markets. Several factors that will affect wave project costs and siting have not been considered here -- including permitting constraints, conflicting use, seasonal resource variability, extreme event likelihood, and distance to ports -- because consistent data are unavailable or technology-independent scoring could not be identified. As the industry continues to mature and converge around a subset of device archetypes with well-defined costs, more precise investigations of project siting that include these factors will be possible. For now, these results provide a high-level guide pointing to the regions where markets and resource will one day support commercial wave energy projects.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Probabilistic Day-Ahead Forecasting Using an Analog Ensemble Approach for Wind Farm Grid Services

Wind resource assessment and wind power forecasting are used in research and industry to anticipate future power output at scales ranging from individual wind turbines to entire wind farms. Probabilistic day-ahead wind forecasting is useful for anticipating how a wind farm could potentially participate in the day-ahead market by providing upper and lower bounds for expected power generation, thus informing grid operators of its uncertainty. Understanding this uncertainty is part of a larger project focused on building a platform that combines efforts in weather forecasting, aerodynamic and economic modeling to create maximum value of a wind plant to better provide services to the grid. This effort is also known as the Atmosphere to Electrons to Grid (A2E2G) project. One method for producing a probabilistic forecast is through the analog ensemble approach (Delle Monache et al., 2011). This method leverages historical forecasts and their corresponding observations as a training data set from which future forecasts can be made. For some future forecast, the most similar historical forecasts (analogs) are identified on a regular time basis such as once per a 3-hour window. The most similar analogs, based on a metric such as root mean square error (RMSE), are recorded and their corresponding verifying observations are used as an ensemble member for this future forecast. Prior work in this area demonstrates improvements over raw Numerical Weather Prediction (NWP) forecasts and shows skill similar to techniques such as logistic regression and machine learning (Delle Monache et al., 2013; Alessandrini et al., 2015). Here, we take the High-Resolution Rapid Refresh model (HRRR) day-ahead forecast (0-36 hours) to create a probabilistic day-ahead forecast using an analog ensemble approach. The HRRR has an hourly temporal resolution, with a spatial resolution of 3 km. The 12 UTC HRRR model run is downloaded every day for one year from August 2019 - July 2020, with the first 11 months serving as a bank of analogs from which the forecasting algorithm can create a probabilistic forecast. Once downloaded, the original HRRR forecast is temporally interpolated to 5-minutes, aligning with both the temporal resolution of the observations as well as the timescale relevant for day-ahead power forecasts. The forecast is validated at the M2 tower at the Flatirons Campus of the National Renewable Energy Laboratory (NREL) at a typical wind turbine height of 80 m. Variables such as wind speed, wind direction, and turbulence intensity are incorporated into the probabilistic forecast model and weighted according to their relative importance to the forecast. Based on metrics such as mean bias error (MBE), mean absolute error (MAE), and root mean square error, the analog ensemble forecast outperforms the raw HRRR forecast during the testing period of July 2020. Figure 1 illustrates an example day-ahead forecast compared against the verifying observations. The general variability and ramps are captured throughout the day, with potential to further improve the analog ensemble model through machine learning techniques.

numerical weather prediction↗
DOE OSTI2018

Relativistic Modeling Capabilities in Perseus Extended MHD Simulation Code for HED Plasmas

This project is submitted in response to DE-FOA-0001153, and will be carried out by Dr. Charles Seyler and Dr. Nathaniel Hamlin as post doc under the direction of Professor Seyler. The objectives are to enable PERSEUS to self-consistently model relativistic high-energy-density (HED) laboratory and astrophysical phenomena. Electrons and ions in a plasma are generally modeled either as fluids using a two-fluid code, like PERSEUS, or as particles using a Particle-In- Cell (PIC) code. Nonrelativistic PERSEUS solves the two-fluid equations, formulated in terms of a generalized Ohm’s law (GOL), so as to model about nine orders of magnitude in density variation using a local semi-implicit method. Relativistic PERSEUS preserves this structure and therefore retains these advantageous properties, enabling it to model a broader range of relativistic HED phenomena than previous codes. We have also overcome a major technical challenge associated with solving a relativistic system of equations relating conserved quantities (momentum, energy, etc.) to primitive variables (density, velocity, and pressure). We have thus far developed a relativistic version of PERSEUS and demonstrated that it recovers expected nonrelativistic results. We are currently using relativistic PERSEUS to model two HED phenomena, namely laser-plasma interactions and X-pinches, both of which show spectroscopic evidence of relativistic electrons. In an X-pinch, a section of wire carrying sufficiently large current forms a plasma column that pinches down to a point-like source, generating X-rays for use in radiographic imaging. Laser-plasma interactions have been simulated using PIC codes, and we use their results as a benchmark for comparison. However, neither PIC codes nor existing two-fluid schemes can simulate X-pinches, due to the broad range of densities which have thus far made such a simulation computationally prohibitive. In both laser-plasma interaction and Xpinch simulations, we have observed relativistic phenomena that PERSEUS could not previously model. This includes relativistic channeling of a laser into an over-dense deuterium gas (i.e. induced transparency), consistent with PIC simulation results, along with energetic electrons and ions following an X-pinch and likely accompanied by strong X-ray emission, consistent with experimental observations of energetic X-rays. This proposal includes our latest simulation results. We will improve computational efficiency with techniques that allow the use of a less refined numerical grid. This will enable extensions to three-dimensional geometries. We will improve modeling of laser-plasma interactions and X-pinches, including radiative transport processes, in order to improve comparisons with PIC simulations and experimental diagnostics. We will also model power loss through electrode surface plasmas in magnetically insulated transmission lines (MITLs). We will model relativistic astrophysical phenomena in both astrophysical and HED regimes, which could shed light on the validity of laboratory astrophysics for certain problems. These phenomena are currently modeled using PIC codes and existing relativistic two-fluid codes, neither of which can model the range of length scales of which relativistic PERSEUS is capable. There is strong potential for this project to have a broad impact on both the astrophysical and laboratory plasma communities. In the laboratory community, our work is applicable to those concerned with laser-plasma interactions such as laser-driven fusion, and those in the dense Z pinch community concerned with X-pinches and electrode surface plasmas. An improved understanding of the mechanisms of hard (i.e. high-energy) X-ray generation will likely make important contributions toward the use of X-pinches for radiography through the harnessing or mitigation of hard X-rays, depending on the application. The modeling of electrode surface plasmas, including vacuum electron flow, will improve our understanding of power loss to various loads.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Real-Time Distributed Control of Smart Inverters for Network-level Optimization

The limitations of centralized optimization methods in managing electric power distribution systems operations have led to the distributed paradigm of computing and decision-making. Unfortunately, the existing distributed optimization algorithms are limited in their applicability to managing fast varying phenomena such as those resulting from highly variable Distributed Energy Resource (DER) generation patterns. They require a large number of communication rounds (in the order of 10 2 to 10 3 ) among the computing agents to solve one instance of the optimization problem. Related real-time distributed control methods are equally limited in their applications to power distribution systems with fast-changing DER generation; they require hundreds of rounds of communication and thus are slow in tracking the network-level optimal solutions. In this paper, we propose a novel distributed voltage controller that provides a fast-tracking of rapidly varying DER generation profiles while simultaneously converging to network-level optimal solutions within a few communication rounds. The proposed control algorithm leverages the radial topology of the system, which reduces the required communication rounds to reach the network-level optimum solution by order of magnitude. The novelty lies in carefully reducing the electrical network model from the perspective of each distributed controller and enabling appropriate data sharing among upstream and downstream nodes to achieve fast convergence. The simulation results demonstrate the effectiveness of the proposed approach in minimizing the feeder losses while maintaining the node voltage within the pre-specified limits.

voltage control, optimization, reactive power, inv↗