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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 73 records · Page 4

Stability and Fidelity of Power-Hardware-in-the-Loop Using NREL's CGI

The presentation focuses on dynamic analysis of power hardware-in-the-loop (PHIL) experiments using National Renewable Energy Laboratory Flatirons Campus' grid simulator - 7MVA Controllable Grid Interface (CGI). A control diagram of typical PHIL experiment is described, and transfer functions of critical components are explained. An impedance-based definition of PHIL experiment bandwidth is defined using open loop transfer functions of the system. Further an example resistive divider circuit is introduced that is used to explain implication of PHIL on accuracy of simulation by comparing measured results against ideal expected signals. At the end the real-life PHIL simulation using Maui power system model is described showing various oscillation modes observed and discussing the meaning of these in context of dynamic bandwidth of simulation.

grid simulator↗

Assessment of mesoscale solid stress in coarse‐grid TFM simulation of Geldart A particles in all fluidization regimes

This study focused on assessing the effect of mesoscale solid stress in the coarse grid two‐fluid model (TFM) simulation of gas–solid fluidized beds of Geldart Group A particles over a broad range of flow regimes, including bubbling, turbulent, fast, and pneumatic transport fluidization regimes. Particularly, the impact of mesoscale solid pressure, mesoscale solid viscosity, and mesoscale solid stress anisotropy were investigated by comparing six different coarse‐grid TFM settings. Compared with the available experimental data, it is found that both the kinetic theory‐based TFM with only drag correction and the filtered TFM can predict the flow behavior in all fluidization regimes. Mesoscale solid pressure and viscosity have the opposite impact on flow hydrodynamics; they compete and offset each other, which confirms the assumption employed in many previous studies that the mesoscale solid stress could be neglected in coarse‐grid TFM simulation. Published 2018. This article is a U.S. Government work and is in the public domain in the USA. AIChE J , 64: 3565–3581, 2018

Gao, Xi↗

NREL and Clemson University Drivetrain Test Facility Collaboration: Cooperative Research and Development Final Report, CRADA Number CRD-13-509

The National Renewable Energy Laboratory (NREL) and Clemson University have a mutual interest in collaborating in the development of wind turbine drivetrain testing facilities and grid simulators. NREL and Clemson University will work together to share resources and experiences in the development of future wind energy test facilities and capabilities. The CRADA includes sharing of facility topologies and capabilities, modeling efforts, commissioning plans, test protocols, infrastructure cost data, test plans, pro forma contracting instruments, and safe operating strategies. Further, NREL and Clemson University will exchange staff for training and development purposes, including collaborative participation in commissioning and customer testing. DOE has provided NREL with over 10 years of support in developing custom facilities and capabilities to enable testing of full-scale integrated wind turbine drivetrain systems in accordance with the needs of the US wind industry. NREL currently operates a 2.5MW dynamometer and is in the processes of building a 5MW dynamometer and a grid simulator (referred to as a 'Controllable Grid Interface' or CGI). Clemson University is currently building a drivetrain testing facility with two dynamometers, 7.5 MW and 15 MW, and 15 MW Hardware in the loop grid simulator.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Dynamic Temporal Graph Sequence Data for Resilience-Oriented Distribution Network Reconfiguration

This dataset comprises temporal dynamic graph sequences generated from power grid simulations focused on grid reconfiguration to enhance resilience. The simulations model failure propagation under varying conditions, with nodes assigned distinct failure probabilities. For each time step, the dataset captures the evolution of node states (functional or failed) and features critical to grid operations, such as pv_output, load_profile, load_dispatch, dg_output, loss, and voltage. Node types include sources, normal loads, and nodes with specific equipment like PVs, micro turbines, or shunt capacitors. The dataset is structured to support the training of dynamic graph neural networks, facilitating research on node feature prediction and edge dynamics under failure scenarios. Three distinct configurations are included, providing a robust foundation for modeling power grid resilience.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Synchronization Algorithms for Co-Simulation of Power Grid and Communication Networks

The ongoing modernization of power grids consists of integrating them with communication networks in order to achieve robust and resilient control of grid operations. To understand the operation of the new smart grid, one approach is to use simulation software. Unfortunately, current power grid simulators at best utilize inadequate approximations to simulate communication networks, if at all. Cooperative simulation of specialized power grid and communication network simulators promises to more accurately reproduce the interactions of real smart grid deployments. However, co-simulation is a challenging problem. A co-simulation must manage the exchange of informa- tion, including the synchronization of simulator clocks, between all simulators while maintaining adequate computational perfor- mance. This paper describes two new conservative algorithms for reducing the overhead of time synchronization, namely Active Set Conservative and Reactive Conservative. We provide a detailed analysis of their performance characteristics with respect to the current state of the art including both conservative and optimistic synchronization algorithms. In addition, we provide guidelines for selecting the appropriate synchronization algorithm based on the requirements of the co-simulation. The newly proposed algorithms are shown to achieve as much as 14% and 63% im- provement, respectively, over the existing conservative algorithm.

co-simulation↗

Simulating dispatchable grid services provided by flexible building loads: State of the art and needed building energy modeling improvements

End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability, reduce costs, and support increased hosting of distributed renewable generation. This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling. We consider services delivered to (1) the high-voltage bulk power system through the coordinated action of many, distributed building loads working together, and (2) targeted support provided to the operation of low-voltage electric distribution grids. Although an exhaustive exploration is not possible, we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling (BEM) tools, suggesting pathways for future research and development.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Inferring Neutron Star Merger Ejecta Morphology with Kilonovae

In this study we incorporate a new grid of kilonova simulations produced by the Monte Carlo radiative transfer code SuperNu in an inference pipeline for astrophysical transients and evaluate their performance. These simulations contain four different two-component ejecta morphology classes. We analyze follow-up observational strategies by Vera Rubin Observatory in optical and James Webb Space Telescope (JWST) in mid-infrared (MIR). Our analysis suggests that, within these strategies, it is possible to discriminate between the four different morphologies only when late-time JWST observations in MIR are available. We conclude that follow-ups by the new Vera Rubin Observatory alone are not sufficient to determine ejecta morphology. Additionally, we make comparisons between surrogate models based on radiative transfer simulation grids by SuperNu and POSSIS, by analyzing the historic kilonova AT2017gfo that accompanied the gravitational wave event GW170817. We show that both SuperNu and POSSIS models provide similar fits to photometric observations but their qualitative interpretations differ.

79 ASTRONOMY AND ASTROPHYSICS↗

2024.03.13 Bushing Subcommittee presentation on Transformer Bushing Seismic Resilience: Design, Hazard, & Prevention Analysis [Slides]

Presents results from power grid simulations on synthetic grids that estimate the impact from power transformer failures due to earthquakes. In the best-case scenario, there is an 82.9% likelihood of load loss greater or equal to 51 MW. Methodologies are described as well as path forward to improving transformer resilience and, consequently, the resilience of the bulk power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Wildfire-Power Grid Interactions: Feedback, Impacts, Monitoring, Modeling, and Mitigation Strategies

Wildfires are increasingly interacting with electric power systems through a two-way hazard chain: fires damage grid assets and trigger cascading outages, while grid faults can ignite new fires under hot, dry, and windy conditions. This review synthesizes the state of knowledge across five domains: (i) physical impacts of flames, heat, and smoke on lines, towers, insulators, and substations; (ii) power-infrastructure-initiated ignitions via conductor clash, high-impedance faults, and corona discharge; (iii) widespread blackouts and disproportionate societal impacts; (iv) multi-scale monitoring spanning laboratory tests, in-situ and grid-integrated sensors, and Earth observation; (v) coupled modeling that links fire behavior with grid operations; and (vi) technological and strategic mitigation pathways spanning prevention, response, and recovery. We integrate these domains into a novel 'feedback-aware' socio-technical framework. Through a longitudinal analysis (2005-2025) of global incidents, we identify that while vegetation contact remains the most frequent ignition source, aging infrastructure failure has emerged as a critical driver of catastrophic 'mega-fires'. We further identify persistent gaps, including limited interoperability of high-frequency grid and environmental data, scarce real-time data assimilation, and under-developed equity metrics for outage management. We conclude by outlining a research agenda to (1) deploy interoperable sensing architectures, (2) advance feedback-coupled fire-grid simulations, and (3) evaluate mitigation portfolios through techno-economic and fairness lenses. Recognizing wildfire-grid interactions as coupled socio-technical systems is essential for protecting infrastructure and communities and for ensuring reliable, sustainable electricity in a changing world.

24 POWER TRANSMISSION AND DISTRIBUTION↗

City-Wide Distributed Roof-Top Photovoltaic System Adoption Forecast, Grid Impact Simulation, & Neighborhood Microgrid Contribution Assessment

The adoption of distributed photovoltaic (PV) systems grew significantly in recent years. Market projections anticipate future growth for both residential and commercial installations. To understand grid impacts associated with distributed PV, useful hosting capacity studies require accurate representations of the spatial distribution of PV adoptions. Prediction of PV locations and numbers depends on median income data, building use zoning maps, and permit records to understand existing trends and predict future adoption rates and locations throughout an entire city. Using the PV adoption data, advanced and realistic simulations were performed to capture the distributed PV impacts on the grid. Also, using graph theory community detection hundreds of neighborhood microgrids can be discovered for the entire city by identifying densely connected loads that are sparsely connected to other communities. Then, based on the PV adoption predictions, this work identified the contribution of PV within each of the newly discovered graph theory defined microgrid communities.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Clemson University Wind Turbine Drivetrain Test Facility

In November of 2009, Clemson University was awarded a competitive grant from the U.S. Department of Energy to design, build and operate a facility for full-scale, highly accelerated mechanical testing of next-generation wind turbine drivetrain technologies. The primary goal of the project was to design, construct, commission, and operate a state-of-the-art sustainable facility that permits full-scale highly accelerated testing of advanced drivetrain systems for large wind turbines. The secondary goal was to meet the objectives of the American Recovery and Reinvestment Act of 2009, especially in job creation, and provide a positive impact on economically distressed areas in the United States, and preservation and economic recovery in an expeditious manner. The project was executed according to a managed cooperative agreement with the Department of Energy and was an extraordinary success. The resultant new facility is located in North Charleston, SC, providing easy transportation access by rail, road or ship and operates on an open access model such that it is available to the U.S. Wind Industry for research, analysis, and evaluation activities. The 72 m by 97 m facility features two mechanical dynamometer test bays for evaluating the torque and blade dynamic forces experienced by the rotors of wind turbine drivetrains. The dynamometers are rated at 7.5 MW and 15 MW of low speed shaft power and are configured as independent test areas capable of simultaneous operation. All six degrees of freedom, three linear and three rotational, for blade and rotor dynamics are replicated through the combination of a drive motor, speed reduction gearbox and a controllable hydraulic load application unit (LAU). This new LAU setup readily supports accelerated lifetime mechanical testing and load analysis for the entire drivetrain system of the nacelle and easily simulates a wide variety of realistic operating scenarios in a controlled laboratory environment. The development of these two dynamometer test rigs is the first significant achievement for the project. These test rigs embody a new manner of test due to the system configuration and completely new design with a free floating loading hub in the LAU. This project provided the catalyst for the advancement to this new test rig configuration that has been adopted by every significant wind turbine test rig constructed since the inception of this project. There are currently two different vendors supplying these new systems. Catalyzing this new design is the second major success of the project. With the increased market penetration of wind energy over the past decade, many regions and countries have developed specific electrical grid specifications and performance codes for large wind farms to ensure operational reliability and stability. These grid codes provide requirements for interconnection with the grid during low or high voltage phenomena, typically encountered during and after system fault events. Given the installed infrastructure of the Wind Turbine Drivetrain Testing Facility (WTDTF), a natural expansion of facility capability was to include the necessary equipment for performing fault ride-through evaluations of wind turbines to the Low Voltage Ride Through (LVRT) codes. Once the decision was made to expand the scope of the original grant into fault ride-through testing, it was clear that there are several markets, not just wind, which could benefit from this type of test and that simple fault ride-through testing could be extended into a broader scope of electrical testing capabilities. It was at this point that Clemson University was awarded a second grant to build a 15 MW Hardware-In-the-Loop (HIL) Grid Simulator in order to establish world class electrical testing capabilities to compliment the mechanical testing at the WTDTF. This third significant achievement resulted in the 15 MW HIL Grid Simulator as the corner stone of the Duke Energy eGRID Center and is collocated with the WTDTF in the SCE&G Energy Innovation Center at the new Facility in North Charleston, SC, USA. Before the eGRID was completed, it was recognized that the ability to test solar farm equipment was but a small step away thru the addition of enhanced equipment to provide for DC testing. In yet another expansion/success, a 2.5 MW rectifier system was designed and implemented by Clemson staff to enhance the Center’s capabilities. The program required over 250,000 man-hours of on-site construction labor reworking the brownfield facility on the former Navy Base, clearly satisfying one of the major goals of the Reinvestment Act. This was done while winning numerous awards for design and construction of the facility, including the Top US Project for 2014 from the Trade Journal Engineering News Record. The project was a major collaborative developmental activity managed by Clemson University staff that involved the DOE and many partners and organizations.

17 WIND ENERGY↗

A Nonlinear Algebraic Multigrid Framework for the Power Flow Equations

Multigrid is a highly scalable class of methods most often used for solving large linear systems. In this paper we develop a nonlinear algebraic multigrid framework for the power flow equations, a complex quadratic system of the form ${diag}({v})\overline{Y{v}}={s}$, where $Y$ is approximately a complex scalar rotation of a real graph Laplacian. This is a standard problem that needs to be solved repeatedly during power grid simulations. A key difference between our multigrid framework and typical multigrid approaches is the use of a novel multiplicative coarse-grid correction to enable a dynamic multigrid hierarchy. We also develop a new type of smoother that allows one to coarsen together the different types of nodes that appear in power grid simulations. In developing a specific multigrid method, one must make a number of choices that can significantly affect the method's performance, such as how to construct the restriction and interpolation operators, what smoother to use, and how aggressively to coarsen. In this paper, we make simple but reasonable choices that result in a scalable and robust power flow solver. Experiments demonstrate this scalability and show that it is significantly more robust to poor initial guesses than current state-of-the-art solvers.

97 MATHEMATICS AND COMPUTING↗

Short-term wind forecasting using statistical models with a fully observable wind flow

The utility of model output data from the Weather Research and Forecasting mesoscale model is explored for very short-term forecasting (5-30 minutes horizon) of wind speed to be used in large scale simulations of an autonomous electric power grid. Using this synthetic data for the development and evaluation of short-term forecasting algorithms offer many unique advantages over observational data, such as the ability to observe the full wind flow field in the surrounding region. Several short-term forecasting algorithms are implemented and evaluated using the synthetic data at several different time horizons and for three different geographic locations. Comparison is made with observational data from one location. We find that short-term forecasts of the synthetic data considering wind flow from the surrounding region perform 26% better than persistence in terms of root mean square error at the 5-minute time horizon. This improvement is comparable to studies of observational data in the literature. These results provide motivation to use synthetic data for short term forecasting in grid simulations, and open the door to future algorithmic improvements.

17 WIND ENERGY↗

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗

Impedance Characterization of Utility-Scale Renewable Energy and Storage Systems: Preprint

This paper presents the impedance characterization of utility-scale wind turbines and PV/storage inverters using a multimegawatt grid simulator-based grid integration testbed. The paper describes testbed components including a 7 MVA grid simulator, 5 MW dynamometer, medium-voltage measurement system, and real-time control system used for automated impedance characterization of wind turbines and inverters. Different aspects of impedance measurement are discussed, including the selection of the magnitude and type of perturbation, measurement of frequency cross-coupling between sequence impedance responses, and the alignment of the reference frame for sequence impedance measurement. The paper also demonstrates high-fidelity validation of EMT simulation models and characterization of grid-forming inverters using impedance measurements.

17 WIND ENERGY↗

Novel Data Driven Noise Emulation Framework using Deep Neural Network for Generating Synthetic PMU Measurements

Sensors play a critical role in supporting day-to-day grid operations and they are essential to operator’s decision-making process. Furthermore, sensors and sensor behaviors need to be emulated with grid simulations to perform modeling studies and to design cutting edge power systems applications. Ensuring the accurate behavior of these applications requires accurate emulation of sensors and pertinent signals. However, most grid simulators and modeling tools assume either zero error scenarios or simplistic noise models that may not always correlate to real-world sensors. To address the above issue, this work presents an initial study on the noise characteristics of phasor measurement units (PMUs), along with models for recreating their unique noise signatures. The proposed methods (both analytical and machine-learning-based) provide a substantial increase in a sensor’s model fidelity, a feature that can be leveraged by an end-user application to yield more accurate system representations. The proposed methods were then applied to micro PMU data from the EPFL microgrid campus to extract sensor noise profiles. This data was used to train a deep learning model, which was tested to emulate the noise characteristics present in actual signals. Based on the observed results and the employed data-driven methodology, the proposed methods may be adapted to replicate the behavior of other grid sensors and power new applications capable of detecting sensor degradation and eventual device failures in near real-time.

PMU, noise emulation, synthetic measurements, deep↗

Toward Adaptive Mixed-Element Unstructured Grids for Simulations of Viscous Flows

Accurate prediction of surface skin friction and heat transfer for viscous flow applications using computational fluid dynamics tools necessitates thin wall-normal elements in the boundary layer. A tetrahedral adaptation process is extended to incorporate the advancing front local reconnection procedure. Thin boundary-layer grids are adapted from the solution, which governs the boundary-layer grid parameters such as first cell height off the wall. The process enables grid-converged steady-state results from a CAD file. Results are presented across the speed range from low subsonic flow over an airfoil to high-speed flows over blunt bodies and a rocket.

CFD↗

Toward Adaptive Mixed-Element Unstructured Grids for Simulations of Viscous Flows

Accurate prediction of surface skin friction and heat transfer for viscous flow applications using computational fluid dynamics tools necessitates thin wall-normal elements in the boundary layer. A tetrahedral adaptation process is extended to incorporate the advancing front local reconnection procedure. Thin boundary-layer grids are adapted from the solution, which governs the boundary-layer grid parameters such as first cell height off the wall. The process enables grid-converged steady-state results from a CAD file. Results are presented across the speed range from low subsonic flow over an airfoil to high-speed flows over blunt bodies and a rocket.

chemically reacting flow↗