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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Geospatial Characterization of Low-Temperature Heating and Cooling Demand in the United States

Geothermal resources at temperatures below 150 degrees C have great potential as energy sources for various direct-use applications including heating and cooling in residential and commercial buildings. This study geospatially quantifies U.S. heating and cooling demand in residential, commercial, and manufacturing sectors; heating demand in the agricultural sector; and cooling demand in data centers at the county level through end-use energy consumption, expenditure, and commissioned power analyses. Heating and cooling demand in the residential sector was estimated using energy consumption data obtained from the U.S. Energy Information Administration accounting for different U.S. climate zones. For commercial sector analysis, the end-use major fuel energy intensity at the census division level was disaggregated to the county level with respect to principal building activities. Heating and cooling demand analysis for the manufacturing sector was based on end-use energy consumption for direct-use total process categorized by the North America Industry Classification System. Fuel expenditures in the U.S. Department of Agriculture Farm Production Expenditures were examined for heating demand analysis in the agricultural sector, particularly for the greenhouse, nursery, and floriculture production category. Lastly, commissioned power for data centers in the United States were explored for cooling demand analysis. Results indicated a significant fraction of U.S. primary energy consumption is used for low-temperature heating and cooling applications. Heating and cooling demand in residential and commercial sectors is significantly affected by the number of housing units and climate zone designations, while heating and cooling demand in manufacturing and agricultural sectors and data centers are mainly dependent on the number of facilities and their locations. Maps were generated visualizing where heating and cooling demand is high and, overlain with geothermal resource maps, can indicate locations where geothermal energy can supply this heating and cooling demand.

cooling demand↗
DOE OSTI2020

Developing a Decision Support Engine to Enable Irrigation Modernization - Poster

Irrigation systems in the United States are some of the oldest continually utilized infrastructure in existence today, with some systems exceeding 100 years in age. They are operated to meet farming demands but are managed through a balance of varying influence: policies limiting water usage, stakeholder interests, and environmental impacts. Irrigation modernization is defined as a set of activities that update and improve existing irrigation systems, including, but not limited to improving water quantity, development of distributed energy resources for surrounding communities, ecosystem services, and improved agricultural yields. Modernizing an existing irrigation system can enable stakeholders to combat changing environmental and population demands but is difficult because the complexity involved in determining the potential benefits and consequences of irrigation modernization is high. We are combining a large amount of various geospatial, tabular, and temporal data with subject matter expertise into a decision support engine that will enable stakeholders to determine the benefits and consequences of irrigation modernization in their irrigation systems. A web-based GIS will allow the user to construct the modifications out of a palette of modernization options, which will be sent to the analytics engine for computations, and back to the web client for a graphical display and comparison of relevant metrics. Our development process involves four phases: 1) identify mechanisms of modernization, 2) identify data requirements, data streams, first principles and applicable algorithms necessary to quantify modernization mechanisms, 3) create ‘modules’ for each modernization mechanism, these modules will form the decision support engine, each capable of performing independently but can also inform other modules when needed, 4) Merge the decision support engine with a user interface, capable of ingesting user inputs and returning insights into the impacts of a modernization project as they relate to economic, environmental, monetary, and energy generation. Once complete, it is our intention that this tool will be fundamental in irrigation modernization projects, providing a strong analytical basis from which stakeholders can quickly make informed decisions regarding project development.

13 HYDRO ENERGY↗
DOE OSTI2023

Evolution of Scenario Generation Capabilities in the ExaSGD Project

High-fidelity renewable energy scenarios and rare-event high-impact contingencies are essential for modeling operations and infrastructure expansion of the next generation power grids using exascale computing resources. As the ExaSGD project progressed, our capabilities for generating scenarios for modeling wind power output at multiple wind farms significantly improved, evolving from the use of independent random perturbations to importance sampling-based techniques capturing representative spatial-temporal relations. Contingency generation also improved, evolving from standard N-1 techniques to probabilistic models of infrastructure damage resulting from extreme weather events. We review the developments in the realistic intermittent-energy scenario forecasting and extreme-event contingency creation as the ExaSGD project progressed and discuss the future work in these areas.

economic dispatch↗
DOE OSTI2020

Application of On-Line Monitoring and Real-Time Characterization of Low Level Waste Samples from Hanford Tanks

Waste and treatment stream characterization is an essential part of safe, efficient, and cost-effective processing of Hanford materials. The demand for materials characterization, including chemical, radionuclide, and physical attributes during waste management, transfer, and staging operations in the Hanford tank farms, is expected to significantly increase as the U.S. Department of Energy Office of River Protection (ORP) progresses toward Hanford waste treatment and immobilization. The application of on-line monitoring can greatly improve operation timeframes and reduce costs by providing real-time data and supporting the reduction of necessary grab sample collection. Recently, our team applied Raman online monitoring and chemometric modeling to the supernate of Hanford tank 241-AP-105, and demonstrated the quantitative measurement of nine analytes within this waste. Application of this method has been expanded to additional Hanford tank wastes, AW-102 and AP-107. The performance of the process monitoring instruments and the accuracy of the chemometric models will be assessed. On-line monitoring results will be compared to anion results determined from the current standard, ion-chromatographic and inductively coupled plasma mass spectrometry methods. Comparisons of both determined values and uncertainties of results will be presented. Results will be shared with personnel working on flowsheet interfaces for the Direct-Feed Low Activity Waste mission at Hanford.

Lines, Amanda M.↗
DOE OSTI2021

Advancements of On-Line Monitoring and Real-Time Characterization of Actual Low-Level Hanford Tank-Waste Samples

Waste and process stream characterization is essential to the safe, efficient, and cost-effective processing of Hanford Site materials. The demand for materials characterization including chemical, radionuclide, and physical attributes during waste management, transfer, and staging operations in the Hanford tank farms is expected to significantly increase as the U.S. Department of Energy River Protection Project progresses toward Hanford waste treatment and immobilization. Our past work has applied Raman on-line monitoring and chemometric modeling to the supernate of Hanford tank 241-AP-105 and has demonstrated the quantitative measurement of nine analytes within this waste. To demonstrate broader applicability, this method has now been expanded to waste from two additional Hanford tanks, AW-102 and AP-107, and the offgas condensate from lab-scale melter runs of actual-tank waste feeds. To enhance the performance of the Raman method, instruments with various excitation wavelengths were compared, since it is known that the Raman response is enhanced by shorter wavelength excitation. The laser excitations for the three systems tested were 404 nm, 532 nm, and 671 nm. In addition to laser wavelength selection, the laser power, measurement integration times, and signal averaging techniques were also investigated to determine their effect on detection limits for oxy-anion analytes within tank wastes. These advancements in Raman capability are compared with past standards and will be presented.

Bryan, Samuel A.↗
DOE OSTI2023

A Two-Step Time-Series Data Clustering Method for Building-Level Load Profile

Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. The load profiles in the same group will have similar characteristics at the same time step, so grid operators can send the grid service signal to the customer group with a higher chance to respond at that time step. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation and the results have proved the effectiveness of this method.

AMI↗
DOE OSTI2023

A Two-Step Time-Series Data Clustering Method for Building-Level Load Profile: Preprint

Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.

advanced metering infrastructure (AMI)↗
DOE OSTI2023

2022 Prototype Manufacture and Installation Awardee: Sonsight Wind

The Sonsight Wind 3.5-kilowatt (kW) prototype turbine is designed to compete with solar power, meeting energy needs for a wide range of smaller-scale applications, such as powering individual homes and irrigation systems on farms and ranches in remote areas. The technology's innovative high-torque, low-rotations-per-minute generator permits the use of longer blades that, at moderate wind speed sites, generate the energy of a 5-kW turbine with lower thrust forces than similarly rated turbines. The 3.5-kW turbine is projected to ofer greater durability at a signifcantly lower levelized cost of energy. Sonsight Wind advanced the development of the prototype with a previous Competitiveness Improvement Project (CIP) funding award.

competitiveness improvement project↗
DOE OSTI2023

Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data: Preprint

To meet the demand for accuracy and real-time capability of PV system degradation evaluation, massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators have a series of difficulties with PV inverter data, such as data collection from multiple channels, massive data storage, data management and massive data analysis. To address these challenges, we developed an integrated data management platform capable of data acquisition, processing, storage, query, and performing big data analysis utilizing AI algorithms. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive data and establish a connection to distributed computational frameworks.

data management↗
DOE OSTI2023

Topological Guided Detection of Extreme Wind Phenomena: Implications for Wind Energy: Preprint

Extreme wind phenomena play a crucial role in the efficient operation of wind farms for renewable energy generation. However, existing detection methods are computationally expensive, limited to specific coordinate. In real-world scenarios, understanding the occurrence of these phenomena over a large area is essential. Therefore, there is a significant demand for a fast and accurate approach to forecast such events. In this paper, we propose a novel method for detecting wind phenomena using topological analysis, leveraging the gradient of wind speed or critical points in a topological framework. By extracting topological features from the wind speed profile within a defined region, we employ topological distance to identify extreme wind phenomena. Our results demonstrate the effectiveness of utilizing topological features derived from regional wind speed profiles. We validate our approach using high-resolution simulations with the Weather Research and Forecasting model (WRF) over a month in the US East Coast.

MATHEMATICS AND COMPUTING,WIND ENERGY↗
DOE OSTI2023

Summary Analysis of Different Offshore Wind Capacity Density Drivers in Proposed U.S. Projects and Impacts on Progress Towards State and Federal Deployment Targets

Understanding the density of offshore wind development - or the "capacity density" - is important for state and federal offshore wind planning efforts and to track progress towards policy goals. NREL has traditionally assumed a capacity density of 3 megawatts per square kilometer (MW/km2) when estimating the U.S. project pipeline (Musial et al. 2022), but capacity densities for existing European offshore wind farms ranged from 2-19 MW/km2 (Borrmann et al. 2018). We track the expected capacity densities of the proposed U.S. projects in the East Coast and make an analysis by state and by developer to identify what are the capacity density norms in the industry and whether the state in which the development efforts are taking place has some impact on capacity density. We also analyze the effects of the area occupied by station keeping systems on capacity density for lease areas dedicated to floating projects and provide a list of main capacity density drivers. This summary analysis sets a baseline to study how different capacity density drivers could impact the progress towards state and federal deployment targets. Given the currently delineated Renewable Energy Areas in the Outer Continental Shelf (OCS) and the basis from the summary analysis, we estimate the expected offshore wind deployment over time federally and by state and compare it to the state and federal deployment targets. The proposed analysis helps identify gaps and needs to meet state and federal targets, which provides sufficient information to propose strategic supply chain and policy planning with the objective of meeting deployment goals.

capacity density↗
DOE OSTI2023

WOMBAT Tutorial

This workshop will provide a comprehensive overview to Windfarm Operations and Maintenance cost-Benefit Analysis Tool (WOMBAT), NREL's first wind O&M software model. The session will discuss how to utilize WOMBAT to answer O&M research questions covering land-based, offshore, and distributed wind farms. It will offer an introduction to the model and how it operates. The team will then offer a demonstration of the various modeling parameters by converting the CORE Wind Floating Wind Strategies O&M Assessment assumptions into an analysis-ready configuration, diving into the results, and exploring further model variations.

discrete event simulation↗
DOE OSTI2023

2022 Small Turbine Certification Awardee: NPS Solutions

Inverters convert the direct current power generated by wind turbines into the alternating current needed to serve local loads and feed onto the electricity grid. Although distributed wind systems typically supply electricity for homes and businesses near the turbines, these systems can also provide clean power to strengthen grid resilience. Certification to UL 1741 SA ensures that inverters will enable distributed wind turbines to provide grid support services and stay online when the grid experiences stability issues. Small companies often find the electrical listing process too complex and expensive to undertake on their own, so few distributed wind turbine inverters are certified to this standard. A U.S. Department of Energy (DOE) Competitiveness Improvement Project (CIP) award is helping NPS Solutions (NPS) secure this critical certification for the inverter in its 100-kilowatt NPS 100C wind turbine, which is optimized for on-site generation at farms, businesses, schools, hospitals, and remote locations.

CIP↗
DOE OSTI2023

2022 Product Commercialization and Market Development Awardee: Bergey Windpower

In 2023, consumers will finance more than 80% of the residential solar energy systems they purchase, which they use as collateral (called "non-recourse loans"). Similar loans are not yet offered to fund distributed wind energy systems. To accelerate deployment of small wind systems, which power individual rural homes and farms, the United States will need to make consumer loans available at reasonable rates with modest downpayments and collateral requirements. A team led by Bergey Windpower Co. is creating new consumer financing options to reduce or eliminate the upfront cash needed to buy distributed wind energy systems. This new financing structure would decrease purchase costs for small wind turbines produced by Bergey Windpower and possibly other manufacturers. Product financing is instrumental in growing wind power market share, clean energy manufacturing, and installation jobs while reducing greenhouse gas emissions. Bergey Windpower's previous Competitiveness Improvement Project (CIP) awards have led to the development of affordable, high-performance wind turbines, microgrids, and components.

CIP↗
DOE OSTI2023

Distributed reinforcement learning and consensus control of energy systems

Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.

King, Jennifer Rose Rose↗
DOE OSTI2023

Large-Amplitude Motion Platform: A New Ocean Simulator Can Help Technologies Succeed

Offshore renewable energy technologies - like wave energy devices, wind turbines, floating solar panels, and hybrid systems - can help decarbonize our power grids as well as offshore activities like international shipping and seafood farming. But it's not always easy to build technologies hearty enough to operate in a powerful ocean environment. And subjecting promising prototypes to real ocean waves can be an expensive, time-consuming, and risky way to get these technologies ready for the high seas. Now, with the National Renewable Energy Laboratory's (NREL's) new testing platform, called the large-amplitude motion platform (or LAMP for short), our experts can replicate powerful ocean waves in a low-risk laboratory setting. The LAMP, coupled with NREL's diverse array of testing instrumentation, can help technology developers rapidly hone their prototypes before embarking on a potentially costly and time-consuming ocean trial.

marine energy↗
DOE OSTI2023

ExaWind at NREL: Upping the Ante

The objective of the ExaWind component of the Exascale Computing Project is to deliver many-turbine blade-resolved simulations in complex terrain. These simulations bring new challenges to both compute and analysis of the resulting data. In this paper/video, we visually explore the impact of ExaWind on wind simulations through two studies of a small wind farm under two atmospheric conditions. We then turn to analysis and review tools that visualization researchers at NREL use to answer the challenges that ExaWind brings.

collaborative visualization↗
DOE OSTI2023

21-Year Modeled Spatiotemporal Low-Level Jet Climatology Along the U.S. Mid-Atlantic Coast

Offshore wind farms are influenced by a variety of meteorological phenomena, ranging in spatial and temporal extent. One of these phenomena, the low-level jet (LLJ), is defined as a local maximum in wind speed with height in the planetary boundary layer, exhibiting a "nose-like" structure usually in the lowest few hundred meters above the surface. This vertical extent of LLJs may coincide with the rotor-swept area of a wind turbine, thus having implications for potential power production and mechanical loading. Previous studies in this area based on observations have confirmed the prevalence of LLJs here, with implications on power generation, wind turbine loads, and wake recovery depending on the LLJ characteristics, such as nose height and what part of the wind turbine power curve is being impacted.

LLJ↗