Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “system planning”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Operational Probabilistic Tools for Solar Uncertainty (OPTSUN) (Final Project Report for DOE Solar Forecasting II Project)

Increasing levels of solar PV can challenge system operations and may require novel methods to operate the power system reliably and efficiently. Power system operating plans generally use deterministic forecasts, in which the variable energy resources are represented by the expected value for each interval of the decision horizon. Probabilistic forecasts are relatively new but have the potential to address the shortfalls of deterministic forecasts. However, understanding how best to use such forecasts is still a key gap in industry and was the focus of this project. The project had three workstreams. In a forecasting workstream, improvements were made to baseline probabilistic forecasts using a number of new approaches such as machine learning methods and improved input data. In a design workstream, advanced simulation tools used these forecasts to investigate newly proposed reserve determination methods. Lastly, in a demonstration workstream a scheduling management platform (SMP) was developed to leverage probabilistic forecasts in a modular and customizable manner. In order to study the benefits that could be accrued, the project team collaborated with three utility partners (Duke Energy, Southern Company and Hawaiian Electric) to deliver improved probabilistic forecasts for each region and to model each region in case studies using advanced production cost modeling tools. Different methods to determine operating reserve requirements from probabilistic forecasts were developed, simulated, and tested across each region. The benefits of using these newly proposed methods varied by utility, but, in general, using probabilistic forecasts as well as historical data to set the reserve requirements seems to improve reliability related results, with less risk of reserve or supply shortfalls. The cost implications were not always straightforward; in some cases the new methods could show a reduction in expected operating costs, but often the increase in reserves associated with better risk mitigation using probabilistic forecasts could result in an increase in operating costs in the simulations. The SMP tool was developed to process probabilistic forecasts from their initial receipt through to scheduling decisions. This open-source tool consists of several modules for scenario development, reserve requirements calculation, and visualization. The SMP tool was demonstrated to a wide range of operators and stakeholders at all three utilities and further improved based on their feedback. The tool will be available on www.epri.com/optsun. The proposed probabilistic information-based reserve determination approaches have the potential to be implemented by different regions to ensure an economic and reliable power system operation on power systems integrating increasing levels of variable renewable resources. The innovative yet practical methods developed in this project demonstrated tangible benefits from using probabilistic forecasts beyond just study-based assessments to include three unique balancing areas. The demonstrated benefits across the multiple utility environments, are expected to provide system operators in all regions the confidence required and a platform to adopt the new forecasting and operating methods.

14 SOLAR ENERGY↗
DOE OSTI2025

Yes, No, Maybe So: Human Factors Considerations for Fostering Calibrated Trust in Foundation Models Under Uncertainty

High-stakes analytical environments require analysts to evaluate evidence and generate conclusions to inform critical decisions often under conditions of uncertainty. Probabilistic decision-making based on incomplete or inaccurate information can reduce productivity, compromise national interests, and endanger public safety. Researchers are developing expert systems built on foundation models (FMs) to support analysts’ decision-making processes by enabling human-artificial intelligence (AI) teaming, in part through the quantification and expression of uncertainty information. As FMs continue to mature, it is imperative to correspondingly consider analysts’ needs for appropriately interpreting and using uncertainty information. However, prior research indicates that it remains unclear how analysts engage with FM-generated uncertainty information and the extent to which these interactions influence trust in, and reliance on, expert systems. We plan to review the state of the science and conduct an exploratory, qualitative study to (a) understand how properly communicated uncertainty can foster calibrated trust and appropriate reliance and (b) identify approaches for effectively conveying FM-generated uncertainty information during analytical workflows. We will administer semi-structured interviews with analysts from a specific high-stakes analytical environment to collect their current experiences with job-related uncertainty and their impressions when viewing FM-generated uncertainty information. During the interview protocol, participants will be presented with several different FM outputs and invited to discuss their thoughts and beliefs about the uncertainty information displayed. Participants may provide insights into how trust and reliance may be influenced by uncertainty. The results of this study will help us to better understand how analysts currently interpret and use uncertainty information. Our findings may inform human factors recommendations for effectively conveying uncertainty information to foster calibrated trust in, and appropriate reliance on, expert systems. Interaction designers and FM developers can use this knowledge to enhance human-AI teaming and ensure the responsible deployment of FM-based expert systems in analytical workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The real-time data processing and acquisition system for Project 8 Phase II

In Phase II of the Project 8 neutrino mass experiment, electrons from the decays of tritium or 83 m Kr are detected via their ≈ 26GHz cyclotron radiation while contained within a circular waveguide. The signal from a given electron is characterized as a brief chirp, lasting ≲ 10ms and changing in frequency by ≲ 1MHz/ms. To detect these signals, the Project 8 collaboration developed a Data Acquisition (DAQ) system tailored to the signal properties. The DAQ is responsible for simultaneously selecting up to three 100MHz-wide frequency windows to study, detect, and trigger on likely signals from different electron kinetic energies, and for writing the relevant data to disk. We describe the Phase II DAQ system in detail and address how the system was used for data-taking operations.

Ashtari Esfahani, A. [Washington U., Seattle; Shar↗
DOE OSTI2025

Data-driven Community-centered Resilient Assessment and Planning Toolkit for Nexus of Energy and Water (DCRAPT-NEW)

Urban areas, including Detroit and Pittsburgh, have suffered significant dual outages of the electrical and water infrastructure in the past decade due, in part, to the increasing number of extreme weather events. With increasing temperatures and rainfall intensity, these regions need to prepare for increasing extreme events through community-based energy and water resilience analysis, planning, and enhancement. This project developed a suite of open-source, open-access, community-centered, data-driven assessment and distributed energy resource (DER) and planning tools for energy and water resilience enhancement in urban areas. Through establishing a multi-level community awareness and engagement mechanism and a comprehensive collection of power outage and flooding data, an innovative group of community energy and water resilience assessment and planning tools have been developed for a wide range of users with differing and variable sets of data available to them. The developed tools include (1) DOE EAGLE-I data-driven, deep-learning assisted resilience assessment and DER planning tools at the county level with socioeconomic factors incorporated; (2) Utility annual power outage data-driven tools for long term resilience assessment and DER planning and 15-min power outage data-driven tools for short term resilience assessment and planning; (3) Detailed engineering tools for energy and water systems resilience assessment and planning when the system topology and component fragility curves are available; (4) Alternative Resiliency Metric Calculation that extracts and separates outage and restoration processes; and (5) Co-optimization tools that evaluate the resilience of the power and sewage system and allow users to conduct joint planning with energy and wastewater systems. The developed tools provide planners, decision-makers, and stakeholders with powerful capabilities to systematically evaluate system/community resilience and optimal and actionable guidance for enhancing resilience while prioritizing DER investments. The tools have been used and validated in Detroit and Pittsburgh and can be used in other areas of the nation. In addition, this project will (1) advance the knowledge and applications of machine-learning methods in analyzing and fusing different layers of information and generating meaningful data points such as generating rare weather events; (2) significantly improve the energy and water resilience of the identified communities in Detroit and Pittsburgh and prepare for more frequent and severe weather conditions; (3) help communities assess extreme weather event impacts and address short-term and long-term resilience-related issues The developed tools have been made public via GitHub and demonstrated to community stakeholders and utility companies via the two annual workshops and numerous community engagement meetings. The project outcomes are also disseminated through publications in various journals and conference proceedings, and presentations at top conferences.

13 HYDRO ENERGY↗
DOE OSTI2021

Planning for a Resilient Home Electricity Supply System

Resilience of power systems is already a key issue that is getting frequent attention all over the world. It is useful to analyze resilience issues not only for bulk supply, but at all levels including at a customer level. This is because distributed energy resources can play a prominent role in enhancing resilience. Although the literature on planning models, tools and data for bulk supply and distribution systems have expanded in recent years, customer-centric planning, e.g., for an individual household, is yet to receive adequate attention. Although solar PV and battery storage at a household level have been analyzed, how these resources can be optimally combined, together with grid supply, from a resilience perspective is the focus of this study. The study demonstrates how a conceptual framework can be developed to show the trade-off between system costs and resilience including its dimensions such as duration, depth and frequency of service outages. A planning model is developed that incorporates multiple facets of resilience and individual customer preferences. The model considers power system resilience explicitly as a constraint. The model is implemented for a household level case study in Miami, Florida. The results show there are complex trade-offs among different dimensions of resilience. The study demonstrates how combined resilience metrics can be formulated and evaluated using the proposed least-cost planning model at a household level to optimize grid supply together with solar, battery storage and diesel generators. The model allows a planner to directly embed a resilience standard to drive the optimal supply mix. These concepts and the modeling construct can also be applied at other levels of planning, including community level and bulk supply system planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Leveraging operational information from wastewater injection wells to evaluate CO 2 injection performance for carbon storage applications in the Appalachian Basin

Abstract Geologic parameters, geophysical logging, injection testing, and operational metrics from wastewater injection wells were integrated to develop a preliminary design of a carbon storage facility in the Appalachian Basin. A scattered group of 10–20 commercial wastewater injection wells dispose off produced water from oil and gas wells in the region, utilizing a sequence of stacked deep saline formations for injection zones. These wastewater injection wells provide practical benchmarks for understanding the feasibility of carbon dioxide (CO 2 ) storage. Geologic models were developed based on characterization data from the wastewater injection wells. Reservoir simulations were calibrated according to injection testing and operational data from the wastewater injection wells. Long‐term operational data on injection flow rates and pressures measured in the wastewater injection wells were especially useful to evaluate the performance of carbon storage applications. The simulations were used to estimate injection pressures, radius of CO 2 saturation, and pressure response for industrial scale CO 2 storage applications. Results were also used to provide a design basis in terms of number of injection wells, well spacing, area of review, injection system components, monitoring plan, and CO 2 pipeline distribution system. The analysis demonstrates that there is sufficient injectivity in the deep saline formations in the west‐central Appalachian Basin to store commercial volumes of anthropogenic CO 2 . The geologic system appears suitable for supporting CO 2 injection rates of 0.5–1.0 million metric tons per year at injection pressures below formation fracture pressure in a single well. The long‐term operational data of wastewater injection wells within the study area suggested a lower permeability‐thickness values than indicated by initial reservoir tests. A workflow for developing realistic permeability values for input into reservoir simulations is presented. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

Valluri, Manoj↗
DOE OSTI2026

Impacts of Solar Land Availability on the Evolution of the U.S. Power System

Long-term planning scenarios of the United States electricity system have not identified solar resource availability to be a limiting factor in how much solar capacity might be deployed. However, there are some smaller regions where siting challenges indicate solar deployment could be limited by solar resource availability. In this work, we use geospatial modeling tools to develop a restrictive siting scenario that significantly reduces the amount and location of utility-scale solar resources. We use that restrictive siting regime, along with other previously defined siting scenarios, to examine how solar resource availability impacts the buildout of the electricity system using business-as-usual and decarbonization futures in a national-scale capacity expansion model. The restrictive siting scenario uses observed siting pressures to exclude many types of land on which development is allowed today and results in a greater than 90% reduction in solar resource potential-to approximately 3000 GW. Even with this drastic reduction, utility-scale solar capacity grows by more than 3 times through 2050 in the business-as-usual scenarios and by more than 6 times in the decarbonization scenarios, though less solar is deployed in scenarios with lower available solar resource. We quantify how system costs and generation mixes change as solar resource availability changes.

14 SOLAR ENERGY↗
DOE OSTI2022

Investigation of Timing Properties for an Event Driven with Access and Reset Decoder Readout Architecture for a Pixel Array

The large number of data generating sources (data channels) on a single chip requires appropriate techniques to manage a readout from these channels. One of the main methods is sharing a medium of transmission, which requires arbitration to avoid collisions or deadlocks. Existing solutions face several problems such as a dead time, unintended prioritization or metastability. That is why we decided to create a new readout architecture named EDWARD i.e., Event Driven with Access and Reset Decoder. The EDWARD architecture gets rid of the earlier mentioned problems and mitigates the other ones. However, due to the use of logic circuits outside a standard cell library, which are hard to characterize, we were challenged to perform an additional transient analysis to validate the architecture. Here we show a methodology and the results of the simulations. Based on the results obtained we can confirm the functional correctness of the system and plan the optimization of operating conditions in order to achieve better performance. Our goal is to use the EDWARD architecture in the future radiation detectors to be built at Brookhaven National Laboratory.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method

The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Finally, simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.

42 ENGINEERING↗
DOE OSTI2023

Frequency Recovery in Power Grids Using High-Performance Computing

Maintaining electric power system stability is paramount, especially in extreme contingencies involving unexpected outages of multiple generators or transmission lines that are typical during severe weather events. Such outages often lead to large supply-demand mismatches followed by subsequent system frequency deviations from their nominal value. The extent of frequency deviations is an important metric of system resilience, and its timely mitigation is a central goal of power system operation and control. This paper develops a novel nonlinear model predictive control (NMPC) method to minimize frequency deviations when the grid is affected by an unforeseen loss of multiple components. Our method is based on a novel multi-period alternating current optimal power flow (ACOPF) formulation that accurately models both nonlinear electric power flow physics and the primary and secondary frequency response of generator control mechanisms. We develop a distributed parallel Julia package for solving the large-scale nonlinear optimization problems that result from our NMPC method and thereby address realistic test instances on existing high-performance computing architectures. Our method demonstrates superior performance in terms of frequency recovery over existing industry practices, where generator levels are set based on the solution of single-period classical ACOPF models.

nonlinear model predictive control↗
DOE OSTI2025

Accelerating Advanced Light Source Science Through Multi-Facility HPC Workflows

Synchrotron light sources support a wide array of techniques to investigate materials, often producing complex, high-volume data that challenge traditional workflows. At the Advanced Light Source (ALS), we developed infrastructure to move microtomography data over ESnet to ALCF and NERSC, where CPU- and GPU-based algorithms generate 3D reconstructed volumes of experimental samples. We employ two data movement and reconstruction models: real-time processing as data streams directly to NERSC compute nodes, and automated file transfer to NERSC and ALCF file systems. The streaming pipeline provides users with feedback in under ten seconds, while the file-based workflow produces high-quality reconstructions suitable for deeper analysis in 20-30 minutes. This infrastructure enables users to utilize HPC resources without direct access to backend systems. We plan to extend this architecture to more endstations, supporting our beamline scientists and users.

Abramov, David↗
DOE OSTI2021

Impact of Spontaneous Fission Neutron Emission Energy on Neutron Detector Response

A parametric study was simulated in MCNP to investigate the impact of spontaneous fission neutron energy on neutron detector response. A total of 880 configurations were simulated by varying 3 He pressure and polyethylene reflector thickness for 8 different isotopes. Results showed that deviations between the simulated isotopes become more prevalent at higher energies with 238 U showing the greatest deviation. It was also observed that configurations comparable to most detection setups, 10 atm and 6 - 8 cm polyethylene, were closer to the upper bound of deviations from a 252 Cf. 240 Pu, a common spontaneous fission source of interest, ranged from 2% - 3% within this space. This suggests that further investigation is required to determine exact correction factors when calibrating neutron detectors with a 252 Cf. Although demonstrated here for a 4π-detection system, future plans include simulating a more detailed model of the MC-15 detection setup to determine exact correction factors for future experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Solidification of REDC Organics and Airborne Release Fraction Measurements

The Radiochemical Engineering Development Center (REDC) generates various liquid organic wastes from processing irradiated targets to recover heavy elements. In the past, these organics were discharged to the Oak Ridge National Laboratory (ORNL) liquid low-level waste (LLLW) system, along with the aqueous waste. Because of a reduction in aqueous LLLW from other ORNL generators and an increase in the radionuclide concentration, particularly 238 Pu, in the organic waste stream, the organics can no longer be discharged to the LLLW system. The plan is to solidify the liquid organic waste for disposal as solid debris waste. The documented safety analysis for REDC uses an airborne release fraction of 5 x 10 -4 with a respirable fraction of 0.2 for plutonium and other actinides from packaged solid debris waste during a fire scenario, for an overall airborne release fraction/respirable fraction of 10 -4 . Solidifying the organic liquids, either by using PM-199 Organoclay ® or by grouting, would produce solid waste forms with no free liquid, which should qualify for disposal at the Waste Isolation Pilot Plant. The use of Organoclay would be much simpler to implement inside a hot cell. Granular Organoclay can be added to the organic waste solutions until there is a dry layer on top of the liquid and then allowed to cure for a few hours. Grouting the liquids would require using multiple ingredients and mixing steps. Both methods produced waste forms that trapped cerium, as a surrogate for plutonium, inside the waste, with airborne release fractions during fire scenario tests of well below the 10 -4 limit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗