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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Illinois Compressed Air Energy Storage

Compressed Air Storage Energy (CAES) is one of the few mid- technology readiness level (TRL) energy storage technologies that can address the long-duration infrastructure needed for dealing with variable electric output from renewable energy sources and be reliable backup source for replacing natural gas during supply interruptions. In CAES the goal is to capture and store compressed air in subsurface sedimentary strata when off-peak power is available, or there is a need for grid balancing. The stored high-pressure air is returned to the surface and used to power turbines during reductions in either renewable energy or supply issues with fossil fuels. The Illinois CAES project evaluates the feasibility of capturing surplus electrical energy from renewable sources and off-peak energy at a fossil fuel power plant at the University of Illinois Urbana - Champaign (UIUC) campus. The UIUC Abbott Power Plant uses natural gas and coal to generate electricity (capacity: 35 MWe by coal and 49 MWe by NG). UIUC receives additional electricity from on campus solar farm, and off-campus wind farm. Also, UIUC offsets electricity usage by integrating geothermal energy systems into building heating Also, UIUC offsets steam, hot and chilled water usage by integrating geothermal energy systems into building heating and cooling systems. Furthermore, the two UIUC solar farms (Solar Farm 1 is 21 acres and Solar Farm 2 is 54 acres) to generate 4.68 megawatts (MW) and 12.1 MW, respectively. Campus receives 8.6% of the wind-generated electricity from the Rail Splitter Wind Farm. The project objectives were to design an integrated system to 1) capture surplus electrical energy from renewable sources and the Abbott Power Plant using a CAES system, 2) store both the compressed air and the thermal heat generated by compression in the subsurface as part of an adiabatic system, 3) simulate the movement of the air and heat in the subsurface, 4) recover the compressed air and stored thermal heat to rotate turbine generators during sustained interruption due to weather events or fossil fuel disruptions.

03 NATURAL GAS↗
DOE OSTI2023

Distributed Energy Resource (DER) Reliability for Backup Electric Power Systems

Hospitals, emergency services, military bases, ports, airports, industries, commercial facilities, and others rely on backup power systems to provide electricity for their critical loads during grid outages. The purpose of this report it to provide accurate reliability information on commonly deployed distributed energy resources (DERs) to improve quantitative estimates for the reliability of these backup power systems during a grid outage. A backup power system consists of DERs, an electric distribution system with its associated switches and other devices, and mechanisms to control and manage the flow of electricity. Too often, facilities and campuses fail to properly quantify the reliability of their backup power systems. DERs are assumed to be 100% reliable, with the only concern being the availability of fuel. Such assumptions can lead to gross errors in the backup system's reliability estimates, particularly for long-duration outages. This report provides a set of estimates for reliability of emergency diesel generators (EDGs), natural gas prime generators and combined heat and power (CHP) prime movers, solar photovoltaics (PV), wind turbines, and Li-ion battery energy storage systems (BESS). The estimates are derived from empirical data when available and supplemented by modeling results when needed. These reliability estimates are for the DERs ability to provide power during a grid outage, ranging from an hour to 2 weeks.

14 SOLAR ENERGY↗
DOE OSTI2021

MADE3D: Enabling the next generation of high-torque density wind generators by additive design and 3D printing

Direct-drive wind turbine generators are increasing in popularity, thanks to recent project developments—especially offshore, where reliability and efficiency are major cost drivers. Yet, high capital costs are forcing many original equipment manufacturers to consider lightweight, high-torque density generators for next-generation multi-megawatt turbines that may be difficult to realize by traditional design or manufacturing methods. In this study, we present a new design framework enabled by advanced machine learning and multimaterial additive manufacturing to perform a magnetic topology optimization that maximizes the torque per rotor active mass for a 15-megawatt direct-drive permanent magnet wind generator. A comparison of the proposed approach against conventional topology optimization demonstrated a significant increase in computational efficiency and accuracy in performance predictions. Results using single and multimaterial compositions for rotor core and magnets identify a wider choice of 3D printable designs for a given specification. A hybrid combination of sintered and dysprosium-free polymer-bonded magnets shows good potential for torque performance by saving material costs up to 8.75%. More than 30% improvement in rotor torque densities is identified which can marginally improve the overall generator torque density. With the rapid evolution of multipowder deposition technolgies, this study can greatly inspire a new paradigm for design-driven manufacturing with novel material compositions and lightweight, low-cost, high-strength multimaterial geometries that were previously unexplored for direct-drive generators.

17 WIND ENERGY↗
DOE OSTI2025

Activity-based Informed Curtailment: Using Acoustics to Design and Validate Smart Curtailment to Reduce Risk to Bats at Wind Farms

Rapid expansion of renewable energy infrastructure is a key part of any global strategy to reduce the pace and severity of anthropogenic climate change, although the potential impacts of renewable energy infrastructure on wildlife are also becoming increasingly apparent. Bats appear vulnerable to population-level impacts from the cumulative effect of turbine-related fatalities at commercial wind energy facilities in North America, particularly as the industry continues to expand to meet renewable energy generation targets. Turbine curtailment is the most widely used and consistently effective method to reduce bat fatality rates and involves pitching turbine blades parallel to prevailing winds to restrict turbine rotation when turbines would otherwise be operating and capable of producing power. Recognizing the need to expand the wind industry while managing risk to bats highlights the need to understand and manage turbine-related impacts to bats more aggressively and strategically than the current use of blanket curtailment allows.

17 WIND ENERGY↗
DOE OSTI2024

Economic dispatch of offshore renewable energy resources for islanded communities with optimal storage sizing

Coastal or isolated microgrids depend on diesel generators and could benefit from renewable energy resources, especially offshore wind and wave energy. Integrating these resources into microgrids is complicated by their high intermittency, which requires optimal economic dispatch to effectively evaluate. This study considers three coastal or islanded sites, and uses mid-fidelity models of wind and wave energy technologies, and local demand data to solve the optimal economic dispatch problem. An optimal storage sizing method is developed that finds the smallest capacity of energy storage required to meet the microgrid load during each season. The storage capacity decreases by a factor of two at most when adding wave energy converters to a system. Adding wave energy converters to a farm decreases cost by about 30%. Furthermore, the required storage size varies by two to three times from summer to winter. Compared with the state-of-the-art approaches that often overlook realistic offshore renewable energy technology in microgrid economic dispatch and optimal storage sizing, the proposed solution introduced in this study allows for better site selection, microgrid design, converter selection, and storage sizing considerations for isolated microgrids.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

AnnualReportingWind [SWR-26-026]

AnnualReportingWind is a version-controlled repository used to generate the cost and performance results published in major wind energy reports such as the Cost of Wind Energy Review (COWER) and the Annual Technology Baseline (ATB). The repository consolidates the model configurations, assumptions, and scripts used in each reporting year so that published results can be transparently reproduced and traced back to the exact analytical setup used in the analysis. The workflows rely on several techno-economic modeling tools developed at the National Laboratory of the Rockies (NLR), including WAVES, ORBIT, LandBOSSE, WOMBAT, and FLORIS.

Mulas Hernando, Daniel [National Laboratory of the↗
NASA NTRS1995

Variable speed generator application on the MOD-5A 7.3 mW wind turbine generator

This paper describes the application of a Scherbiustat type variable speed subsystem in the MOD-5A Wind Turbine Generator. As designed by General Electric Company, Advanced Energy Programs Department, under contract DEN3-153 with NASA Lewis Research Center and DOE, the MOD-5A utilizes the subsystem for both starting assistance in a motoring mode and generation in a controlled airgap torque mode. Reactive power control is also provided. The Scherbiustat type arrangement of a wound rotor machine with a cycloconverter in the rotor circuit was selected after an evaluation of variable speed technologies that followed a system evaluation of drivetrain cost and risk. The paper describes the evaluation factors considered, the results of the evaluations and summarizes operating strategy and performance simulations.

Barton, Robert S.↗
DOE OSTI2022

Large Scale Pumped Storage to Support Renewable Deployment; PSH Resource Assessment and Complementary Analysis for Alaska

The State of Alaska has a rather unique electric power system, as it has two larger transmission grids (Railbelt and Southeast Alaska) and over 150 islanded stand-alone power systems that are serving remote rural communities. In 2010, the Alaska legislature enacted a non-binding goal for 50% of renewable electricity generation by 2025. With the expected increase in wind and solar generation in the future, the role of energy storage becomes increasingly important. Considering the specific power system characteristics in Alaska, energy storage technologies that can supply electricity over an extended period of time, such as pumped storage hydropower (PSH), may play a key role in enabling the reliability and resiliency of both integrated and rural power systems. The overarching objective of the subject study of this presentation is to investigate the prospects and opportunities for PSH in Alaska.

Alaska↗
DOE OSTI2023

In Situ Wind and Turbulence Measurements in a Field of Full-Size Parabolic Trough Solar Collectors

Concentrated Solar Power (CSP) is a promising method for using Solar power for electricity generation with thermal energy storage. One of the primary drivers of structural design costs of CSP collector structures is wind loading. To date, the design of these structures has relied on data from wind tunnels that do not adequately capture the dynamic effects observed at scale. NREL initiated a field measurement campaign at the operational Nevada Solar One (NSO) powerplant that uses parabolic troughs as solar collectors. The aim of the project is a detailed characterization of prevailing wind and turbulence conditions and resulting operational loads on parabolic troughs, providing insights on structural dynamic response, and generating a first-of-a-kind, comprehensive, high-resolution wind-loading dataset available for validating simulations of wind loading on collector structures. The measurements at NSO consist of Sonic anemometers on masts at different heights to characterize the incoming flow and conditions within and above the trough field, in combination with a Doppler Lidar scanning the horizontal plane above the troughs. The wind measurements at NSO have been continuously operating since October 2021 and provide a year-long dataset characterizing wind and turbulence conditions. The structural load measurements start in November 2022 and will complement the wind measurements. In this poster, we present first results of the wind measurement campaign by highlighting days with different atmospheric flow conditions. We identified three main factors altering the flow over the parabolic troughs: Wind speed, wind direction, and the angle of the parabolic troughs. The highest loads are expected when the wind blows perpendicular to the trough rows. In this case, the first rows experience the highest loads and block the subsequent rows, creating conditions with decreased wind speed and enhanced turbulence within the trough field. Also, turbulent length scales change. These affected conditions produce unique load cases on the structures that will be captured by the load measurements. If the wind blows along the trough rows, wind and turbulence conditions are less influenced by the troughs.

atmospheric turbulence↗
DOE OSTI2021

The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling

The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.

13 HYDRO ENERGY↗
DOE OSTI2026

Loss Factors for Small Distributed Wind Turbines Based on Field Data in the United States

While wind energy production loss due to unavailability, environmental impacts, curtailment, and other causes has been studied and characterized at the utility-scale wind farm level, observation-based characterization of project loss is lacking for distributed wind energy, particularly for projects involving small wind turbines. Contemporary tools and research that support pre-construction distributed wind energy characterization present a wide range of default loss factors to convert gross energy estimates to net: 7-18%. We hypothesize that we can use generation observations from operational distributed wind projects to develop more accurate representations of loss. Using a density-based filtering technique on distributed wind power generation timeseries, we determine periods of typical performance and use them with regression algorithms in a measure-correlate-predict fashion to simulate what the generation would have been during periods of atypical or unreported performance. From there, the actual versus predicted generation leads to the establishment of observation-informed loss factors (median = 17%) for small, single turbine installation distributed wind projects.

17 WIND ENERGY↗
NASA NTRS1979

Control strategy for a variable-speed wind energy conversion system

A control concept for a variable-speed wind energy conversion system is proposed, for which a self-exited asynchronous cage generator is used along with a system of thyristor converters. The control loops are the following: (1) regulation of the entrainment speed as function of available mechanical energy by acting on the resistance couple of the asynchronous generator; (2) control of electric power delivered to the asynchronous machine, functioning as a motor, for start-up of the vertical axis wind converter; and (3) limitation of the slip value, and by consequence, of the induction currents in the presence of sudden variations of input parameters.

Jacob, A.↗
DOE OSTI2024

Stochastic economic dispatch of wind power under uncertainty using clustering-based extreme scenarios

Operation of power systems with high penetrations of renewable energy sources requires tools for robust decision making under uncertainty. Stochastic economic dispatch and stochastic unit commitment are effective techniques for planning and operation under uncertainty, whose effectiveness depends on the cardinality and quality of the scenario set. Here, this article proposes a machine learning method using -means clustering for capturing relevant physical information from a large population of analog scenarios. Extreme scenario samples drawn from the clusters are used in a two-stage stochastic economic dispatch computation. The effectiveness of the proposed approach is assessed on a synthetic 200-bus system with a geographic footprint over Illinois, USA for four months from each season of WIND Toolkit data. The combination of -means clustering with importance sampling is shown to reduce the total operational cost by over 43% compared to sampling from populations based on heuristic clustering-based methods. Additionally, the variability in the mean cost is about 56% lower than the variability using Monte Carlo sampling. Moreover, the operational cost with the presented approach is shown to be close to the cost calculated based on a hindsight exact wind profile, signifying a highly accurate quantification of wind uncertainty by the presented -means clustering based sampling method.

17 WIND ENERGY↗
NASA NTRS1982

Economics of wind energy for utilities

Utility acceptance of this technology will be contingent upon the establishment of both its technical and economic feasibility. This paper presents preliminary results from a study currently underway to establish the economic value of central station wind energy to certain utility systems. The results for the various utilities are compared specifically in terms of three parameters which have a major influence on the economic value: (1) wind resource, (2) mix of conventional generation sources, and (3) specific utility financial parameters including projected fuel costs. The wind energy is derived from modeling either MOD-2 or MOD-0A wind turbines in wind resources determined by a year of data obtained from the DOE supported meteorological towers with a two-minute sampling frequency. In this paper, preliminary results for six of the utilities studied are presented and compared.

Mccabe, T. F.↗
DOE OSTI2020

Benefit Analysis of Long-Duration Energy Storage in Power Systems with High Renewable Energy Shares

The integration of high shares of variable renewable energy raises challenges for the reliability and cost-effectiveness of power systems. The value of long-duration energy storage, which helps address variability in renewable energy supply across days and seasons, is poised to grow significantly as power systems shift to larger shares of variable generation such as wind and solar. This study explores the system-level services and associated benefits of long-duration energy storage on the 2050 Western Interconnection (WI). The operation of the future WI system with 85% renewable penetration is simulated using a two-stage production cost model. The impact of long duration energy storage on systemwide operations is examined for the 2050 WI system, using a range of round-trip efficiencies corresponding to four different energy storage technologies. The analysis projects the energy storage dispatch profile, system-wide production cost savings (from both diurnal and seasonal operation), and impacts on generation mix, and change in renewable generation curtailment.

25 ENERGY STORAGE↗
DOE OSTI2024

The Role of Interface Band Alignment in Epitaxial SrTiO 3 /GaAs Heterojunctions

Recent concerns surrounding climate change and the contribution of fossil fuels to greenhouse gas (GHG) emissions have sparked interest and advancements in renewable energy sources including wind, solar, and hydroelectricity. These energy sources, often referred to as “clean energy”, generate no operational onsite GHG emissions. They also offer the potential for clean hydrogen production through water electrolysis, presenting a viable solution to create an environmentally friendly alternative energy carrier with the potential to decarbonize industrial processes reliant on hydrogen. To conduct a full life cycle analysis, it is crucial to account for the embodied emissions associated with renewable and nuclear power generation plants as they can significantly impact the GHG emissions linked to hydrogen production and its derived products. In this work, we conducted a comprehensive analysis of the embodied emissions associated with solar photovoltaic (PV), wind, hydro, and nuclear electricity. We investigated the implications of including plant-embodied emissions in the overall emission estimates of electrolysis hydrogen production and subsequently on the production of synthetic ammonia, methanol, and Fischer– Tropsch (FT) fuels. Results show that average embodied GHG emissions of solar PV, wind, hydro, and nuclear electricity generation in the United States (U.S.) were estimated to be 37, 9.8, 7.2, and 0.3 g CO 2 e/kWh, respectively. Life cycle GHG emissions of electrolytic hydrogen produced from solar PV, wind, and hydroelectricity were estimated as 2.1, 0.6, and 0.4 kg of CO 2 e/kg of H 2 , respectively, in contrast to the zero-emissions often used when the embodied emissions in their construction were excluded. Average life cycle emission estimates (CO 2 e/kg) of synthetic ammonia, methanol, and FT-fuel from solar PV electricity are increased by 5.5, 16, and 49 times, respectively, compared to the case when embodied emissions are excluded. This change also depends on the local irradiance for solar power, which can result in a further increase of GHG emissions by 35–41% in areas of low irradiance or reduce GHG emissions by 21–25% in areas with higher irradiance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary optimization where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The new approach to shape optimization resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.

additive manufacturing↗