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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Geophysical data analysis and visualization using the Grid Analysis and Display System

Several problems posed by the rapidly growing volume of geophysical data are described, and a selected set of existing solutions to these problems is outlined. A recently developed desktop software tool called the Grid Analysis and Display System (GrADS) is presented. The GrADS' user interface is a natural extension of the standard procedures scientists apply to their geophysical data analysis problems. The basic GrADS operations have defaults that naturally map to data analysis actions, and there is a programmable interface for customizing data access and manipulation. The fundamental concept of the GrADS' dimension environment, which defines both the space in which the geophysical data reside and the 'slice' of data which is being analyzed at a given time, is expressed The GrADS' data storage and access model is described. An argument is made in favor of describable data formats rather than standard data formats. The manner in which GrADS users may perform operations on their data and display the results is also described. It is argued that two-dimensional graphics provides a powerful quantitative data analysis tool whose value is underestimated in the current development environment which emphasizes three dimensional structure modeling.

Doty, Brian E.↗
DOE OSTI2026

GAT (Grid Analysis Toolkit) [SWR-25-41]

Grid Analysis Toolkit (GAT) is a unified Python API and plotting for power system PCM and CEM results (Sienna, PLEXOS, ReEDS™). It's a toolkit for wrangling data for Bulk Grid Dispatch and Transmission Analysis. GAT aims to provide simplified access to PCM and CEM results in a standard format while also allowing raw data access to underlying datasets specific to the model. This software can also be found on PyPI at For plotting, GAT defaults to standard National Lab of the Rockies (NLR) color schemes and standard styles while allowing customization.

Webb, Micah [National Laboratory of the Rockies (N↗
DOE OSTI2013

High-Performance Computing for Real-Time Grid Analysis and Operation

Power grids worldwide are undergoing an unprecedented transition as a result of grid evolution meeting information revolution. The grid evolution is largely driven by the desire for green energy. Emerging grid technologies such as renewable generation, smart loads, plug-in hybrid vehicles, and distributed generation provide opportunities to generate energy from green sources and to manage energy use for better system efficiency. With utility companies actively deploying these technologies, a high level of penetration of these new technologies is expected in the next 5-10 years, bringing in a level of intermittency, uncertainties, and complexity that the grid did not see nor design for. On the other hand, the information infrastructure in the power grid is being revolutionized with large-scale deployment of sensors and meters in both the transmission and distribution networks. The future grid will have two-way flows of both electrons and information. The challenge is how to take advantage of the information revolution: pull the large amount of data in, process it in real time, and put information out to manage grid evolution. Without addressing this challenge, the opportunities in grid evolution will remain unfulfilled. This transition poses grand challenges in grid modeling, simulation, and information presentation. The computational complexity of underlying power grid modeling and simulation will significantly increase in the next decade due to an increased model size and a decreased time window allowed to compute model solutions. High-performance computing is essential to enable this transition. The essential technical barrier is to vastly increase the computational speed so operation response time can be reduced from minutes to seconds and sub-seconds. The speed at which key functions such as state estimation and contingency analysis are conducted (typically every 3-5 minutes) needs to be dramatically increased so that the analysis of contingencies is both comprehensive and real time. An even bigger challenge is how to incorporate dynamic information into real-time grid operation. Today’s online grid operation is based on a static grid model and can only provide a static snapshot of current system operation status, while dynamic analysis is conducted offline because of low computational efficiency. The offline analysis uses a worst-case scenario to determine transmission limits, resulting in under-utilization of grid assets. This conservative approach does not necessarily lead to reliability. Many times, actual power grid scenarios are not studied, and they will push the grid over the edge and resulting in outages and blackouts. This chapter addresses the HPC needs in power grid analysis and operations. Example applications such as state estimation and contingency analysis are given to demonstrate the value of HPC in power grid applications. Future research directions are suggested for high performance computing applications in power grids to improve the transparency, efficiency, and reliability of power grids.

Contingency Analysis↗
DOE OSTI2021

Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis

This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty electric vehicles (EVs) based on both geospatial and electric grid analysis. The geospatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node-level analysis of potential impacts on voltages and power losses in the distribution system. A case study using a realistic, three-phase, unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast-charging stations for heavy-duty EVs considering both electric and existing transportation infrastructures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis: Preprint

This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty Electric Vehicles (EVs) based on both geospatial and electrical grid analysis. The geo-spatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node level analysis of potential impacts on voltages and power losses in distribution system. A case study using realistic three-phase unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast charging station for heavy duty EVs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

GRid Analysis and Visualization Interface (GRAVI) [SWR-24-16]

GRAVI (GRid Analysis and Visualization Interface) is a web application for viewing and analyzing nodal Production Cost Model (PCM) and Capacity Expansion Model (CEM) simulations. The web application provides the ability to animate geospatially coupled timeseries data in an agnostic way regardless of the underlying simulation tool used to generate the data. GRAVI also provides capabilities to animate non-geospatial data relevant to a PCM or CEM model. Furthermore, this web application can be tailored as an real-time operational tool to better understand a live grid.

Webb, Micah↗
NASA NTRS1994

The Grid Analysis and Display System (GrADS)

During the period 1 September 1993 - 31 August 1994, further development of the Grid Analysis and Display System (GrADS) was conducted at the Center for Ocean-Land-Atmosphere Studies (COLA) of the Institute of Global Environment and Society, Inc. (IGES) under subcontract 5555-31 from the University Space Research Association (USRA) administered by The Center of Excellence in Space Data and Information Sciences (CESDIS). This final report documents progress made under this subcontract and provides directions on how to access the software and documentation developed therein. A short description of GrADS is provided followed by summary of progress completed and a summary of the distribution of the software to date and the establishment of research collaborations.

Kinter, James L., III↗
DOE OSTI2015

Guest Editorial Introduction to the Special Issue on 'Advanced Signal Processing Techniques and Telecommunications Network Infrastructures for Smart Grid Analysis, Monitoring, and Management'

Electrical power systems are undergoing a radical change in structure, components, and operational paradigms, and are progressively approaching the new concept of smart grids (SGs). Future power distribution systems will be characterized by the simultaneous presence of various distributed resources, such as renewable energy systems (i.e., photovoltaic power plant and wind farms), storage systems, and controllable/non-controllable loads. Control and optimization architectures will enable network-wide coordination of these grid components in order to improve system efficiency and reliability and to limit greenhouse gas emissions. In this context, the energy flows will be bidirectional from large power plants to end users and vice versa; producers and consumers will continuously interact at different voltage levels to determine in advance the requests of loads and to adapt the production and demand for electricity flexibly and efficiently also taking into account the presence of storage systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Towards Real-Time High Performance Computing For Power Grid Analysis

Real-time computing has traditionally been considered largely in the context of single-processor and embedded systems, and indeed, the terms real-time computing, embedded systems, and control systems are often mentioned in closely related contexts. However, real-time computing in the context of multinode systems, specifically high-performance, cluster-computing systems, remains relatively unexplored. Imposing real-time constraints on a parallel (cluster) computing environment introduces a variety of challenges with respect to the formal verification of the system's timing properties. In this paper, we give a motivating example to demonstrate the need for such a system--- an application to estimate the electromechanical states of the power grid--- and we introduce a formal method for performing verification of certain temporal properties within a system of parallel processes. We describe our work towards a full real-time implementation of the target application--- namely, our progress towards extracting a key mathematical kernel from the application, the formal process by which we analyze the intricate timing behavior of the processes on the cluster, as well as timing measurements taken on our test cluster to demonstrate use of these concepts.

High performance computing, real-time-computing, p↗
DOE OSTI2025

Scalable Hybrid Large-Scale dc-ac Grid Analysis Methods (Phase II)

The goals of the project included the identification and evaluation of a voltage source converter multiterminal high-voltage direct current (VSC-MTdc) system architecture suitable for a high amount of power transfer through long transmission lines.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

Discharge Hollow Cathode and Extraction Grid Analysis for the MiXI Ion Thruster

Miniature ion thrusters are well-suited future space missions such as Terrestrial Planet Finder - Interferometer (TPF-I), where high efficiency thrusters using non-contaminating noble gas propellant are desirable. Transient dynamic and orbital analyses have shown that the low-noise, continuous thrust of the Miniature Xenon Ion (MiXI) thruster is desirable for TPF-I formation rotation maneuvers when compared with other thruster options [1], [2]. The 3cm diameter MiXI thruster, Figure 1, was originally designed using experimental methods and is capable of high Isp (> 3,000 sec), propellant efficiency > 80%, and thrust from <0.1 mN to >1.5 mN [3]. The MiXI thruster must demonstrate high levels of thrust resolution and a low minimum impulse bit to ensure it meets the precision formation flying needs of missions such as TPF-I. A novel concept for controlling the ion extraction voltages yields the necessary thrust characteristics for the MiXI thruster. Experiments verify these techniques and two dimensional computational models show that such techniques should have minimal effect on the lifetime of the thruster. During this effort, the MiXI thruster incorporates, for the first time, flight like hollow cathodes for both the discharge chamber and beam neutralization.

ions↗
DOE OSTI2023

A Transductive Graph Neural Network learning for Grid Resilience Analysis

Power grids are critical infrastructures that require robust resilience analysis to ensure reliable and uninterrupted electricity supply. Traditional simulation-based methods for grid resilience analysis suffer from computational complexity and limited ability to capture the full spectrum of potential disruptions. This paper presents a novel approach to enhance grid resilience by leveraging transductive graph neural network (GNN) learning to identify critical nodes and links. By leveraging the graph structure and system features, GNNs effectively learn resilience metrics and accurately identify critical nodes based on actual grid operational behavior. The efficacy of the proposed approach is demonstrated through case studies on node criticality scoring and critical node/line identification in cascading outage scenarios. The results highlight the advantages of learning-based methods over traditional simulation-based approaches and their potential to revolutionize grid resilience analysis. The contributions of this paper include a graph-based scalable approach for fast cascading analysis, an inductive formulation for training GNN models, and a transfer learning-based approach to scale the model to largescale power systems.

grid resilience, graph neural networks, transducti↗