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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
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

Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report

In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior in real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.

14 SOLAR ENERGY↗
NASA NTRS2020

Tree Rings and Observations Suggest No Stable Cycles In Sierra Nevada Cool‐Season Precipitation

California's water resources rely heavily on cool‐season (November–March) precipitation in the Sierra Nevada. Interannual variability is highly volatile and seasonal forecasting has little to no skill, making water management particularly challenging. Over 1902–2020, Sierra Nevada cool‐season precipitation totals exhibited significant 2.2‐ and 13–15‐year cycles, accounting for approximately 40% of total variability and perhaps signifying potential as seasonal forecasting tools. However, the underlying climate dynamics are not well understood and it is unclear whether these cycles are stable over the long‐term. We use tree rings to reconstruct Sierra Nevada cool‐season precipitation back to 1400. The reconstruction is skillful, accounting for 55–74% of observed variability and capturing the 20th‐century 2.2‐ and 13–15‐year cycles. Prior to 1900, the reconstruction indicates no other century‐long periods of significant spectral power in the 2.2‐ or 13–15‐year bands. The reconstruction does indicate significant cyclicity over other extended periods of several decades or longer, however, with dominant periodicities in the ranges of 2.1–2.7 and 3.5–8 years. The late 1700s through 1800s exhibited the highest‐amplitude cycles in the reconstruction, with periodicities of 2.4 and 5.7–7.4 years. The reconstruction should serve to caution against extrapolating the observed 2.2‐ and 13–15‐year cycles to guide future expectations. On the other hand, observations and the reconstruction suggest that interannual variability of Sierra Nevada cool‐season precipitation is not a purely white noise process and research should aim to diagnose the dynamical drivers of extended periods of cyclicity in this critical natural resource.

Paleoclimate↗
NASA NTRS2016

Towards a Global Water Scarcity Risk Assessment Framework: Incorporation of Probability Distributions and Hydro-Climatic Variability

Changing hydro-climatic and socioeconomic conditions increasingly put pressure on fresh water resources and are expected to aggravate water scarcity conditions towards the future. Despite numerous calls for risk-based water scarcity assessments, a global-scale framework that includes UNISDR's definition of risk does not yet exist. This study provides a first step towards such a risk based assessment, applying a Gamma distribution to estimate water scarcity conditions at the global scale under historic and future conditions, using multiple climate change and population growth scenarios. Our study highlights that water scarcity risk, expressed in terms of expected annual exposed population, increases given all future scenarios, up to greater than 56.2% of the global population in 2080. Looking at the drivers of risk, we find that population growth outweigh the impacts of climate change at global and regional scales. Using a risk-based method to assess water scarcity, we show the results to be less sensitive than traditional water scarcity assessments to the use of fixed threshold to represent different levels of water scarcity. This becomes especially important when moving from global to local scales, whereby deviations increase up to 50% of estimated risk levels.

Populations↗
NASA NTRS1971

Preprocessing techniques to reduce atmospheric and sensor variability in multispectral scanner data.

Multispectral scanner data are potentially useful in a variety of remote sensing applications. Large-area surveys of earth resources carried out by automated recognition processing of these data are particularly important. However, the practical realization of such surveys is limited by a variability in the scanner signals that results in improper recognition of the data. This paper discusses ways by which some of this variability can be removed from the data by preprocessing with resultant improvements in recognition results.

Crane, R. B.↗
DOE OSTI2018

The Use of Statistically Based Rolling Supply Curves for Electricity Market Analysis: A Preliminary Look

In light of the changing electricity resource mixes across the United States, an important question in electricity modeling is how additions and retirements of generation, including additions in variable renewable energy (VRE) generation could impact markets by changing hourly wholesale energy prices. Instead of using resource-intensive production cost models (PCMs) or building and using simple generator supply curves, this analysis uses a 'top-down' approach based on regression analysis of hourly historical energy and load data to estimate the impact of supply changes on wholesale electricity prices, provided the changes are not so substantial that they fundamentally alter the market and dispatch-order driven behavior of non-retiring units. The rolling supply curve (RSC) method used in this report estimates the shape of the supply curve that fits historical hourly price and load data for given time intervals, such as two-weeks, and then repeats this on a rolling basis through the year. These supply curves can then be modified on an hourly basis to reflect the impact of generation retirements or additions, including VRE and then reapplied to the same load data to estimate the change in hourly electricity price. The choice of duration over which these RSCs are estimated has a significant impact on goodness of fit. For example, in PJM in 2015, moving from fitting one curve per year to 26 rolling two-week supply curves improves the standard error of the regression from 16 dollars/MWh to 6 dollars/MWh and the R-squared of the estimate from 0.48 to 0.76. We illustrate the potential use and value of the RSC method by estimating wholesale price effects under various generator retirement and addition scenarios, and we discuss potential limits of the technique, some of which are inherent. The ability to do this type of analysis is important to a wide range of market participants and other stakeholders, and it may have a role in complementing use of or providing calibrating insights to PCMs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2010

Development of an Improved Methodology to Assess Potential Unconventional Gas Resources

Considering the important role played today by unconventional gas resources in North America and their enormous potential for the future around the world, it is vital to both policy makers and industry that the volumes of these resources and the impact of technology on these resources be assessed. To provide for optimal decision making regarding energy policy, research funding, and resource development, it is necessary to reliably quantify the uncertainty in these resource assessments. Since the 1970s, studies to assess potential unconventional gas resources have been conducted by various private and governmental agencies, the most rigorous of which was by the United States Geological Survey (USGS). The USGS employed a cell-based, probabilistic methodology which used analytical equations to calculate distributions of the resources assessed. USGS assessments have generally produced distributions for potential unconventional gas resources that, in our judgment, are unrealistically narrow for what are essentially undiscovered, untested resources. In this article, we present an improved methodology to assess potential unconventional gas resources. Our methodology is a stochastic approach that includes Monte Carlo simulation and correlation between input variables. Application of the improved methodology to the Uinta-Piceance province of Utah and Colorado with USGS data validates the means and standard deviations of resource distributions produced by the USGS methodology, but reveals that these distributions are not right skewed, as expected for a natural resource. Our investigation indicates that the unrealistic shape and width of the gas resource distributions are caused by the use of narrow triangular input parameter distributions. The stochastic methodology proposed here is more versatile and robust than the USGS analytic methodology. Adoption of the methodology, along with a careful examination and revision of input distributions, should allow a more realistic assessment of the uncertainty surrounding potential unconventional gas resources.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Dataset for Biorefinery-specific feedstock price variability, Part 1: Corn stover

This dataset was utilized in a report to highlight parameters that affect near-term sustainable supply of corn stover and forest resources at $56 and $74 per dry ton delivered. While the report focus is restricted to 2018, the modeling runs are available from 2016-2022. In the 2016 Billion-ton Report (BT16), two stover cases were presented. In this dataset, we vary technical levels of those assumptions to measure stover supply response and to evaluate the major determinants of stover supply. In each of these cases, the supply is modeled first at the farmgate at prices up to $80 per dry ton for five deterministic scenarios. Building on this dataset, a supplementary dataset of delivered supply was modeled for 800k dry ton per year capacity facilities in two facility siting approaches. Results were summarized across delivered supply curves for twelve scenarios. The resulting supply curves are highly elastic, resulting in a range of potential supplies across scenarios at specified prices. Interactive visualization of these data allows exploration into any specified nth plant supply sensitivity to key variables and spatial distribution of stover resources. The analysis is economic supply risk and doesn’t account for disruptions from competing demands, namely livestock feed and bedding markets. Scenario ending in _BC3080 is a reference scenario consistent with BT16 Basecase (BC1), but with corn stover price isolation. Scenario ending in _OHB080 includes high operational efficiency constraints for corn stover. Scenario ending in _OLB080 includes low operational efficiency constraints for corn stover. Scenario ending in _PLB080 includes low opportunity cost. Scenario ending in _PHB080 includes high opportunity cost. No Land Rental costs are applied to these scenarios. All scenarios were under an exogenous price simulation using POLYSYS (v2017).

Agricultural residues, biomass resources, 2018, bi↗
DOE OSTI2023

Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorologically relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks to months is an area of active research and development. NASA's Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1–3-month lead times in the HMA region, including a portion of the Indian subcontinent, during the retrospective forecast period, 1981–2016. To assess forecast skill, we evaluate 2 m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to the similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from an anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system's ability to forecast HMA hydrometeorology.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

Seasonal Forecasting Skill for the High Mountain Asia Region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorological relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks-to-months is an area of active research and development. NASA’s 15 Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1-3 month lead times in the HMA region, including a portion of the Indian Subcontinent, during the retrospective forecast period, 1981-2016. To assess forecast skill, we evaluate 2-m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and 20 Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are 25 evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology.

GEOS↗
NASA NTRS2007

Improving Land-Surface Model Hydrology: Is an Explicit Aquifer Model Better than a Deeper Soil Profile?

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the storage and movement of water (including soil moisture, snow, evaporation, and runoff) after it falls to the ground as precipitation. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy. Hence LSMs have been developed to integrate the available information, including satellite observations, using powerful computers, in order to track water storage and redistribution. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. Recently, the models have begun to simulate groundwater storage. In this paper, we compare several possible approaches, and examine the pitfalls associated with trying to estimate aquifer parameters (such as porosity) that are required by the models. We find that explicit representation of groundwater, as opposed to the addition of deeper soil layers, considerably decreases the sensitivity of modeled terrestrial water storage to aquifer parameter choices. We also show that approximate knowledge of parameter values is not sufficient to guarantee realistic model performance: because interaction among parameters is significant, they must be prescribed as a harmonious set.

Gulden, L. E.↗
DOE OSTI2022

Dimension-adaptive machine learning-based quantum state reconstruction

Here, we introduce an approach for performing quantum state reconstruction on systems of n qubits using a machine learning-based reconstruction system trained exclusively on m qubits, where m ≥ n. This approach removes the necessity of exactly matching the dimensionality of a system under consideration with the dimension of a model used for training. We demonstrate our technique by performing quantum state reconstruction on randomly sampled systems of one, two, and three qubits using machine learning-based methods trained exclusively on systems containing at least one additional qubit. The reconstruction time required for machine learning-based methods scales significantly more favorably than the training time; hence this technique can offer an overall saving of resources by leveraging a single neural network for dimension-variable state reconstruction, obviating the need to train dedicated machine learning systems for each Hilbert space.

42 ENGINEERING↗
DOE OSTI2023

State-Level Trends in Renewable Energy Procurement via Solar Installation versus Green Electricity

In recent years, options for procuring renewable energy have increased, ranging from rooftop solar installation to utility green pricing to Community Choice Aggregation. These options vary in terms of costs and benefits to the consumer as well as grid integration implications. However, little is known regarding how the presence of a wide range of voluntary utility-scale renewable procurement options as well as their growth could affect adoption of distributed residential solar. To examine this relationship, we fit a two-stage least squares random effects regression model on panel data from 2016 to 2019 for all fifty US states plus the District of Columbia, controlling for variables that measure state-level policies, economic factors, and resource availability. Although there was no evidence of a strong relationship between demand for utility-scale and distributed options across all states, the state-level correlations suggest a wide variation between states including a positive, zero or negative relationship between utility-scale and distributed generation.

consumer demand↗
DOE OSTI2026

From Reproducible Edge–Cloud Experimentation to Real-World Practice: The E2Clab Experience

Reproducibility is already difficult in distributed systems; on the computing continuum, it becomes substantially harder. Applications that span sensing devices, edge and fog resources, and cloud platforms must be evaluated across heterogeneous hardware, variable network conditions, cross-layer orchestration decisions, and long-running workflow lifecycles. We use E2Clab as a case study to examine these challenges and their implications for experimental methodology. We explain why reproducible experimentation is harder on the continuum, then revisit E2Clab as an initial response based on explicit modeling of infrastructure, workflow lifecycle, and artifacts. Lastly, we discuss how its evolution toward more realistic application settings can be understood through the lens of Translational Computer Science. We argue that reproducible continuum experimentation requires methods that are rigorous enough for research while remaining adaptable to real-world practice.

42 ENGINEERING↗
DOE OSTI2012

Wind Power Opportunities in St. Thomas, USVI: A Site-Specific Evaluation and Analysis

This NREL technical report utilizes a development framework originated by NREL and known by the acronym SROPTTC to assist the U.S. Virgin Islands in identifying and understanding concrete opportunities for wind power development in the territory. The report covers each of the seven components of the SROPTTC framework: Site, Resource, Off-take, Permitting, Technology, Team, and Capital as they apply to wind power in the USVI and specifically to a site in Bovoni, St. Thomas. The report concludes that Bovoni peninsula is a strong candidate for utility-scale wind generation in the territory. It represents a reasonable compromise in terms of wind resource, distance from residences, and developable terrain. Hurricane risk and variable terrain on the peninsula and on potential equipment transport routes add technical and logistical challenges but do not appear to represent insurmountable barriers. In addition, integration of wind power into the St. Thomas power system will present operational challenges, but based on experience in other islanded power systems, there are reasonable solutions for addressing these challenges.

17 WIND ENERGY↗
DOE OSTI2013

Resource Planning Model: An Integrated Resource Planning and Dispatch Tool for Regional Electric Systems

This report introduces a new capacity expansion model, the Resource Planning Model (RPM), with high spatial and temporal resolution that can be used for mid- and long-term scenario planning of regional power systems. Although RPM can be adapted to any geographic region, the report describes an initial version of the model adapted for the power system in Colorado. It presents examples of scenario results from the first version of the model, including an example of a 30%-by-2020 renewable electricity penetration scenario.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

National Offshore Wind Energy Grid Interconnection Study

The National Offshore Wind Energy Grid Interconnection Study (NOWEGIS) considers the availability and potential impacts of interconnecting large amounts of offshore wind energy into the transmission system of the lower 48 contiguous United States. A total of 54GW of offshore wind was assumed to be the target for the analyses conducted. A variety of issues are considered including: the anticipated staging of offshore wind; the offshore wind resource availability; offshore wind energy power production profiles; offshore wind variability; present and potential technologies for collection and delivery of offshore wind energy to the onshore grid; potential impacts to existing utility systems most likely to receive large amounts of offshore wind; and regulatory influences on offshore wind development. The technologies considered the reliability of various high-voltage ac (HVAC) and high-voltage dc (HVDC) technology options and configurations. The utility system impacts of GW-scale integration of offshore wind are considered from an operational steady-state perspective and from a regional and national production cost perspective.

17 WIND ENERGY↗
DOE OSTI2017

Measuring changes in ambient noise levels from the installation and operation of a surge wave energy converter in the coastal ocean

Ecosystem impacts resulting from elevated underwater noise levels generated by anthropogenic activities in the coastal ocean are poorly understood and remain difficult to address as a result of a significant gap in knowledge for existing nearshore sound levels. Ambient noise is an important habitat component for marine mammals and fish that use sound for essential functions such as communication, navigation, and foraging. Questions surrounding the amplitudes, frequency distributions, and durations of noise emissions from renewable wave energy conversion (WEC) projects during their construction and operation present concerns for long-term consequences in marine habitats. Oregon’s dynamic nearshore environment presents significant challenges for passive acoustic monitoring that include flow noise contamination from wave orbital motions, turbulence from breaking surf, equipment burial, and fishing pressure from sport and commercial crabbers. This project included 2 techniques for passive acoustic data collection: 1) campaign style deployments of fixed hydrophone lander stations to capture temporal variations in noise levels and 2) a drifting hydrophone system to record spatial variations within the project site. The hydrophone lander deployments were effective and economically feasible for enabling robust temporal measurements of ambient noise levels in a variety of sea state conditions. Limiting factors for the fixed stations included 1) a flow shield mitigation strategy failure in the first deployment resulting in significant wideband data contamination and 2) flow noise contamination of the unshielded sensors restricting valuable analysis to frequencies above 500 Hz for subsequent deployments. Drifting hydrophone measurements were also effective and economically feasible (although logistically challenging in the beginning of the project due to vessel time constraints) providing a spatial distribution of sound levels, comparisons of noise levels in varying levels of vessel traffic during similar sea states, and reducing the frequencies contaminated by flow noise to f < 50 Hz by an effective drifting hydrophone system design strategy. Results from this project can still assist regulatory agencies and WEC developers in permitting and licensing, reducing project costs overall and assisting the economic development of the WEC industry, thus furthering the MHK energy industry and easing the U.S. reliance on foreign oil for energy production. Additionally, results from this project can be used to help inform coastal resource managers and regulatory agencies on existing baseline noise level variability and ecosystem health.

16 TIDAL AND WAVE POWER↗