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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

In-situ x-ray scatter imaging of battery electrodes

A method and apparatus for in-situ x-ray scatter imaging of battery electrodes. An apparatus includes an X-ray source, a grid, the grid comprising stainless steel wires with uniform spacing, and a cell, the X-ray source directing a beam of energy through the metal grid and components of the cell, the cell blurring a previously sharp projection of grid wires on an image detector. A method includes providing a Spatial Frequency Heterodyne Imaging system, providing a grid, providing a cell, generating X-rays from the Spatial Frequency Heterodyne Imaging system that pass through components of the cell and the grid, and detecting a scatter image from the X-rays.

Rose-Petruck, Christoph↗
NASA NTRS2010

Modeling Vortex Generators in the Wind-US Code

A source term model which simulates the effects of vortex generators was implemented into the Wind-US Navier Stokes code. The source term added to the Navier-Stokes equations simulates the lift force which would result from a vane-type vortex generator in the flowfield. The implementation is user-friendly, requiring the user to specify only three quantities for each desired vortex generator: the range of grid points over which the force is to be applied and the planform area and angle of incidence of the physical vane. The model behavior was evaluated for subsonic flow in a rectangular duct with a single vane vortex generator, supersonic flow in a rectangular duct with a counterrotating vortex generator pair, and subsonic flow in an S-duct with 22 co-rotating vortex generators. The validation results indicate that the source term vortex generator model provides a useful tool for screening vortex generator configurations and gives comparable results to solutions computed using a gridded vane.

Dudek, Julianne C.↗
DOE OSTI2024

Digital Twin + AI: Control Room of the Future [Slides]

The control room functions as the central brain of the grid, essential for balancing supply and demand and ensuring moment-to-moment grid reliability. Like the human brain, which processes sensory data to make decisions, control room operators analyze operational data from power generation, transmission, and distribution to make informed decisions. Currently, decision-making primarily rests with operators due to hardware and software limitations. However, with technological advancements, Digital Twins and AI are becoming high interest points in the control room's decision-making pilot programs. NREL is developing a comprehensive decision-making platform that integrates Digital Twins, AI, and advanced visualization techniques. As this integration progresses, the role of Digital Twins will evolve from conducting automated simulations to serving as a Trustworthy AI enabler, offering verification and validation of AI-generated response for power systems or providing physics-aware synthetic data of AI pre-training.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Accelerating Development of Advanced Inverters.

Increasing the penetration of distributed renewable sources, including photovoltaic (PV) generators, poses technical challenges for grid management. The grid has been optimized over decades to rely on large centralized power plants with well-established feedback controls. Conventional generators provide relatively constant dispatchable power and help to regulate both voltage and frequency. In contrast, photovoltaic (PV) power is variable, is only as predictable as the weather, and provides no control action. Thus, as conventional generation is displaced by PV power, utility operation stake holders are concerned about managing fluctuations in grid voltage and frequency. Furthermore, since the operation of these distributed resources are bound by certain rules that require they stop delivering power when measured voltage or frequency deviate from the nominal operating point, there are also concerns that a single grid event may cause a large fraction of generation to turn off, triggering a black out or break-up of an electric power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Paths to Sustainable Distributed Generation through 2050: Matching Local Waste Biomass Resources with Grid, Industrial, and Community Needs

This project identifies and evaluates current and future organic waste fuel types and matches these wastes with local grid, industrial, building power, and thermal energy needs. The team developed data on organic waste streams and thermal energy needs, and then constructed a techno-economic analysis model that can process the data and compute viable production of electricity, waste heat, biogas, and/or biomethane for a range of energy price points. Scenarios generated by the model include allocation of local organic waste streams to existing or new conversion infrastructure and are evaluated in a life-cycle assessment model to determine local and global environmental impacts. The project’s webbased tool provides rapid evaluation of potential waste-to-energy sites, including retrofits/expansions of existing facilities or entirely new construction, on the basis of resource availability, potential for waste heat use, and economic and environmental metrics.

09 BIOMASS FUELS↗
DOE OSTI2020

Grid stabilization using adjusted voltage regulator response to grid characteristics

Apparatus and methods are disclosed for control systems for improving stability of electrical grids by temporarily reducing voltage output of electrical generators responsive to transient events on an electrical grid. In one example of the disclosed technology, a controller is coupled is to an automatic voltage regulator, which in turn adjusts excitation current of an electrical generator responsive to changes in frequency detected for the electrical grid. Reducing the output voltage temporarily allows for smaller generators to provide power to the microgrid. Methods for selecting parameters determining how the controller generates a regulation signal used to adjust the excitation current are further disclosed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

This paper investigates the relative merits of grid- and subbasin-based land surface modeling approaches for hydrologic simulations, with a focus on their scalability (i.e., abilities to perform consistently across a range of spatial resolutions) in simulating runoff generation. Simulations produced by the grid- and subbasin-based configurations of the Community Land Model (CLM) are compared at four spatial resolutions (0.125o, 0.25o, 0.5o and 1o) over the topographically diverse region of the U.S. Pacific Northwest. Using the 0.125o resolution simulation as the “reference”, statistical skill metrics are calculated and compared across simulations at 0.25o, 0.5o and 1o spatial resolutions of each modeling approach at basin and topographic region levels. Results suggest significant scalability advantage for the subbasin-based approach compared to the grid-based approach for runoff generation. Basin level annual average relative errors of surface runoff at 0.25o, 0.5o, and 1o compared to 0.125o are 3%, 4%, and 6% for the subbasin-based configuration and 4%, 7%, and 11% for the grid-based configuration, respectively. The scalability advantages of the subbasin-based approach are more pronounced during winter/spring and over mountainous regions. The source of runoff scalability is found to be related to the scalability of major meteorological and land surface parameters of runoff generation. More specifically, the subbasin-based approach is more consistent across spatial scales than the grid-based approach in snowfall/rainfall partitioning, which is related to air temperature and surface elevation. Scalability of a topographic parameter used in the runoff parameterization also contributes to improved scalability of the rain driven saturated surface runoff component, particularly during winter. Hence this study demonstrates the importance of spatial structure for multi-scale modeling of hydrological processes, with implications to surface heat fluxes in coupled land-atmosphere modeling.

Tesfa, Teklu K.↗
DOE OSTI2021

Power Grid Reliability Estimation via Adaptive Importance Sampling

Electricity production currently generates approximately 25% of greenhouse gas emissions in the USA. Thus, increasing the amount of renewable energy is a key step to carbon neutrality. However, integrating a large amount of fluctuating renewable generation is a significant challenge for power grid operating and planning. Grid reliability, i.e., an ability to meet operational constraints under power fluctuations, is probably the most important of them. In this letter, we propose computationally efficient and accurate methods to estimate the probability of line overflow, i.e., reliability constraints violation, under a known distribution of renewable energy generation. To this end, we investigate an importance sampling approach, a flexible extension of Monte-Carlo methods, which adaptively changes the sampling distribution to generate more samples near the reliability boundary. The approach allows to estimate overload probability in real-time based only on a few dozens of random samples, compared to thousands required by the plain Monte-Carlo. Our study focuses on high voltage direct current power transmission grids with linear reliability constraints on power injections and line currents. Herein, we propose a novel theoretically justified physics-informed adaptive importance sampling algorithm and compare its performance to state-of-the-art methods on multiple IEEE power grid test cases.

power system faults↗
DOE OSTI2015

Hybrid Energy: Combining Nuclear and Other Energy Sources

The leading cause of global climate change is generally accepted to be growing emissions of greenhouse gas (GHG) as a result of increased use of fossil fuels [1]. Among various sources of GHG, the global electricity supply sector generates the largest share of GHG emissions (37.5% of total CO2 emissions) [2]. Since the current electricity production heavily relies on fossil fuels, it is envisioned that bolstering generation technologies based on non-emitting energy sources, i.e., nuclear and/or renewables could reduce future GHG emissions. Integrated nuclear-renewable hybrid energy systems HES) are very-low-emitting options, but they are capital-intensive technologies that should operate at full capacities to maximize profits. Hence, electricity generators often pay the grid to take electricity when demand is low, resulting in negative profits for many hours per year. Instead of wasting an excess generation capacity at negative profit during off-peak hours when electricity prices are low, nuclear-renewable HES could result in positive profits by storing and/or utilizing surplus thermal and/or electrical energy to produce useful storable products to meet industrial and transportation demands. Consequently, it is necessary (1) to identify key integrated system options based on specific regions and (2) to propose optimal operating strategy to economically produce products on demand. In prioritizing region-specific HES options, available resources, markets, existing infrastructures, and etc. need to be researched to identify attractive system options. For example, the scarcity of water (market) and the availability of abundant solar radiation make solar energy (resource) a suitable option to mitigate the water deficit the Central-Southern region of the U.S. Thus, a solar energy-driven desalination process would be an attractive option to be integrated into a nuclear power plant to support the production of fresh water in this region. In this work, we introduce a particular HES option proposed for a specific U.S. region and briefly describe our modeling assumptions and procedure utilized for its analysis. Preliminary simulation results are also included addressing several technical characteristics of the proposed nuclear-renewable HES.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Adaptive Neurocontrol for Grid-Following Inverters

The new generation of power systems involve a large number of autonomous operating units with the flow of data being restricted by privacy concerns or infrastructure hurdles. The systems are also time-varying so the control should adjust in a timely manner to avoid failures that usually have very negative economical implications. Adaptive neurocontrol which takes elements from adaptive control (great for time-varying problems) and model identification (could be in the form of neural network) by using local available data is a compelling tool. In this work, we summarize analytical results for adaptive neurocontrol, followed with numerical verification of how the method could work for future grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Solid State Power Substations (SSPS): A Multi-Hierarchical Architecture from Substation to Grid Edge

With the growing deployment of distributed generation, or power electronic interfaced renewable energy and storage technologies, the nature and behavior of the grid is changing. Synchronous machine-driven asset contributions to the generation mix are shrinking, leading to concerns regarding grid stability. Furthermore, the scale of smaller distributed PE resources needed for managing the electrical network could dwarf the existing system leading to more complex optimization problems and communication interconnections. This paper introduces the concept of a hierarchal system of controllers that spans the grid edge or the customer end to distribution scale substations or solid-state power substation (SSPS). This concept focuses on minimizing the number of interfaces and optimization considerations in the grid by clustering resources into nodes and hubs. The work validates the concept in a controller hardware-in-the-loop (cHIL) platform.

Chinthavali, Madhu Sudhan↗
DOE OSTI2021

Deploying Intra-hour Uncertainty Analysis Tools to ABB’s GridView - CRADA 445

The quickly-changing generation resource mix in the US grid, with large additions of variable resources, retirements of traditional thermal generation, distributed generation and demand response, are creating new challenges on the traditional operation of both the generation and transmission systems. The ability to perform intra-hour high fidelity production cost modeling (PCM) is needed to allow grid operators and grid planners to integrate high penetration (more than 50%) of variable energy resources, and to make prompt well informed decisions in market operations and planning. In the last decade, PNNL has developed several stand-alone tools to enable grid operators and planners to understand the impact of high variable generation on their systems. These tools have been used is studies such as: (1) Evaluation the benefits of WECC balancing authorities coordination under high variable generation penetration , (2) Benefits of Energy Imbalance Market in the North West Power Pool and (3) Duke Energy and NV Energy solar integration studies. ABB’s GridView is a widely used commercial PCM tool. It is the tool used by WECC and their stakeholders to develop the WECC PCM planning model on bi-annual basis. This proposal is focused on the integrating of PNNL intra-hour uncertainty analysis tools to GridView.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN)

Building electrical load profiles can improve understanding of building energy efficiency, demand flexibility, and building-grid interactions. Current approaches to generating load profiles are time-consuming and not capable of reflecting the dynamic and stochastic behaviors of real buildings; some approaches also trigger data privacy concerns. In this study, we proposed a novel approach for generating realistic electrical load profiles of buildings through the Generative Adversarial Network (GAN), a machine learning technique that is capable of revealing an unknown probability distribution purely from data. The proposed approach has three main steps: (1) normalizing the daily 24-hour load profiles, (2) clustering the daily load profiles with the k-means algorithm, and (3) using GAN to generate daily load profiles for each cluster. The approach was tested with an open-source database – the Building Data Genome Project. We validated the proposed method by comparing the mean, standard deviation, and distribution of key parameters of the generated load profiles with those of the real ones. The KL divergence of the generated and real load profiles are within 0.3 for majority of parameters and clusters. Additionally, results showed the load profiles generated by GAN can capture not only the general trend but also the random variations of the actual electrical loads in buildings. We report the proposed GAN approach can be used to generate building electrical load profiles, verify other load profile generation models, detect changes to load profiles, and more importantly, anonymize smart meter data for sharing, to support research and applications of grid-interactive efficient buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Analysis of Wind Ramping Product Formulations in a Ramp-Constrained Power Grid

Flexible ramping products are designed to compensate the variability and uncertainty of load and intermittent generation. Since their market implementation by the California Independent System Operator (CAISO) and Midcontinent System Operator (MISO), flexible ramping products have garnered much attention. However, it is still unclear how to best formulate wind power plants’ participation in the ramping requirement. This paper investigates different wind ramping product formulations and increasing wind power penetration in the context of a security-constrained unit commitment (SCUC) model. We demonstrate that the ramping model that captures both the intra- and inter-temporal output ramp capability of individual wind power plants reflects the true ramp contribution of the wind fleet. With increasing wind penetration, wind generation curtailments can support the grid’s ramping needs. In addition, we found that increased wind penetration has the potential of lowering ramping and production costs. Numerical case studies performed on the TAMU 2000-bus synthetic network support the findings.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2015

A Petri Net model for distributed energy system

Electrical networks need to evolve to become more intelligent, more flexible and less costly. The smart grid is the next generation power energy, uses two-way flows of electricity and information to create a distributed automated energy delivery network. Building a comprehensive smart grid is a challenge for system protection, optimization and energy efficient. Proper modeling and analysis is needed to build an extensive distributed energy system and intelligent electricity infrastructure. In this paper, the whole model of smart grid have been proposed using Generalized Stochastic Petri Nets (GSPN). The simulation of created model is also explored. The simulation of the model has allowed the analysis of how close the behavior of the model is to the usage of the real smart grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Grid Forming Control of PMSG-based Wind Turbines under Fault Conditions

Grid-forming control is widely envisioned as core technology ensuring stable operation of future power systems with increasing deployment of converter-interfaced renewable generation. However, research on grid forming control of wind turbines, especially under faults, is still in its early stages. In this context, this paper augments dual-port grid-forming control for type IV wind turbines with auxiliary controls to enable reliable fault ride through capabilities. In particular, the proposed control does not require a dc chopper or mode switching to grid-following control. Instead, energy-balancing dual-port GFM control is augmented with active rotor speed damping control and threshold virtual impedance for current limiting during faults. Different operating conditions of wind turbines are considered in a detailed electromagnetic transient simulation study to validate the effectiveness of the proposed control.

Lyu, Xue↗
DOE OSTI2026

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗