Search NASA⌕ Search

SEARCH · Search NASA

Results for “Wind Power”

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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Power-Domain Impedance Theory for the Analysis and Mitigation of Interarea Oscillations: Preprint

This paper presents an impedance-based theory for the analysis and mitigation of interarea oscillations in power systems. Because low-frequency power system oscillations primarily manifest in phasor quantities, including active and reactive power flows, and the magnitude and frequency of bus voltages, a new-type of impedance, called power-domain impedance, is defined in terms of these phasor quantities. The power-domain impedance provides an intuitive framework for the analysis of interarea oscillations without requiring internal details of generators, and it is ideally suited for developing oscillation damping controls in inverter-based resources. The power-domain impedance theory is demonstrated in this paper using PSCAD simulations of a two-area system. The theory is also used for designing damping control in synchronous generators and a wind power plant in the two-area system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Stochastic DC optimal power flow with reserve saturation

We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that generator outputs respond to load imbalances according to an affine control policy, but introduces a model of saturation of generator reserves by assuming that when a generator’s target level hits its limit, it abandons the affine policy and produces at that limit. This is a particularly interesting feature in models where wind power plants, which have uncertain upper generation limits, are scheduled to provide reserves to balance load fluctuations. Here, the resulting model is a nonsmooth nonconvex two-stage stochastic program, and we use a stochastic approximation method to find stationary solutions to a smooth approximation. Computational results on 6-bus and 118-bus test instances demonstrate that by considering the effects of saturation, our model can yield solutions with lower expected generation costs (at the same target line violation probability level) than those obtained from a model that enforces the affine policy to stay within generator limits with high probability.

17 WIND ENERGY↗
DOE OSTI2022

What can surface wind observations tell us about interannual variation in wind energy output?

The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are ~25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (< 25 km) wind plants and compare annual estimated generation, based on a function of surface wind speed observations, to annual recorded generation. Researchers rely on this publicly available surface data because measurements co-located with wind plants are generally considered proprietary. Our analysis addresses a research gap: interannual variation in observed surface wind speeds is rarely compared to observed data at wind plant locations and turbine heights. We find that despite its common use for this purpose, generation estimates based on publicly available surface observational data provide a poor proxy for interannual variability in recorded wind generation. These findings suggest that caution is generally needed when researchers use surface wind speed measurements to investigate long-term wind energy trends.

17 WIND ENERGY↗
DOE OSTI2024

Evaluating the Effectiveness of a Detection and Deterrent System in Reducing Golden Eagle Fatalities at Operational Wind Facilities

The Renewable Energy Wildlife Institute (REWI) was appointed as the prime awardee of DOE award number DE-EE0007883 to lead a team of scientists, wind developers, and technology manufacturers toward the overarching goal of evaluating the effectiveness of the current DTBird system in minimizing the risk of golden eagles (Aquila chrysaetos) and other large soaring raptors from approaching the rotor-swept zone (RSZ) of operating wind turbines. As part of this goal, the team set out to 1) quantify the expected reduction in collision risk for golden eagles from operation of the detection and deterrence modules in a manner that supports the approach used by the U.S. Fish and Wildlife Service (USFWS) to assess and credit facility operators for their efforts to minimize predicted collision fatalities and 2) provide information to help improve the technology to maximize its effectiveness. DTBird is an automated detection and audio deterrent system created by the Spanish company Liquen, designed to discourage birds from entering the RSZ of spinning wind turbines. The system uses cameras to automatically detect airborne targets of interest, records each such event in an online database, and triggers a warning signal (loud sound) if the tracked object has moved close to the turbine. If the object moves even closer to the RSZ, a more aggressive dissuasion signal is broadcast. To meet our objectives, the team conducted a two-year experiment at the Goodnoe Hills wind facility in Washington state, in which 14 turbines were outfitted with DTBird units. Daily, each DTBird-equipped turbine was randomly assigned to a control or treatment group. Treatment turbines operated with DTBird running as intended—broadcasting warning or deterrent signals when DTBird detected a target within range. On control turbines, no sound signals were broadcast if a moving target triggered the DTBird system. The team also flew unmanned aerial vehicles (UAVs) designed to coarsely mimic the general size, weight, and coloration of golden eagles in programmed flight transects across DTBird detection ranges to quantify DTBird’s ability to detect intended targets and to evaluate factors that influence the probability of detection and DTBird’s response distances. Additionally, the team evaluated the behavioral responses of in situ eagles exposed to spinning turbines alone (visual and sound influences) versus spinning turbines plus broadcasted DTBird audio deterrents, to estimate the effectiveness of deterrence by the DTBird system. The data and results from these investigations were combined with those from a pilot study conducted at the Manzana Wind Power Project in California to better evaluate DTBird’s effectiveness across different landscapes.

17 WIND ENERGY↗
DOE OSTI2022

Effectively using multifidelity optimization for wind turbine design

Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems – with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization – the multifidelity method finds the optimal design using 38 %–58 % of the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.

17 WIND ENERGY↗
DOE OSTI2020

Offshore Wind in the US Gulf of Mexico: Regional Economic Modeling and Site-Specific Analyses

The goal of this study is to assess offshore wind energy resources in the Gulf of Mexico (GOM) and to quantify its technical and economic potential in order to inform Federal and GOM state strategic energy planning over the next decade. The objectives are to: describe site-specific and regional benefits and challenges of deploying offshore wind in the GOM and discuss the current research to mitigate challenges; review and quantify the wind resource capacity and energy potential in the GOM region for each GOM state including state and federal waters, distance from shore, and water depth; perform geospatial regional economic assessments of levelized cost of energy (LCOE), levelized avoided cost of energy (LACE), and net value (Section 3.4.2); evaluate three representative hypothetical locations for site-specific analysis; perform a high-level assessment of local supporting infrastructure availability, including existing services (e.g., fabrication facilities, vessels, ports) and possible grid connection options; assess the regional economic impacts of a 600 megawatt (MW) offshore wind power plant installed at a representative site in the GOM.

17 WIND ENERGY↗
DOE OSTI2022

Forecasted Dynamic Line Ratings for Regional Transmission Lines

This report was prepared for the Wind Energy Technology Office for the FY 2022, quarter 3 deliverable. This details the use of forecasted dynamic line rating as an improvement over static rating for three transmission lines in three regions of interest. A selection of wind plants across Idaho, the Columbia River Gorge and offshore wind in the NYSERDA territory were modeled for their concurrent cooling effects for dynamic line rating. Regional transmission lines for the wind plant interconnections were determined and a CFD domain was created to determine local wind flows over the lines. A dynamic line rating for each of the lines was calculated for both weather observation data and HRRR model forecast data. The comparison of peaks in the wind power production were compared to the DLR peaks. It was determined that for shorter regional transmission lines, the effect of concurrent cooling that commonly occur with gen-tie lines were still valid. Concurrent cooling effects drop as the length of the regional transmission lines increase in distance from the wind plants. In addition, the accuracy of the forecasted ampacity to the weather observation data for ampacity was assessed. In general, the accuracy was best for the INL region with the NOAA-maintained sites. The Columbia River Gorge and Long Island sites showed similar levels of accuracy for the forecasted DLR. The accuracy levels were around 11% for the INL region, 15% for Columbia River Gorge and 7% for New York Long Island. The higher error for the Columbia Gorge region is likely due to the sparse availability of weather stations available, whereas both the INL site and Long Island are well instrumented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Wind Turbine Drivetrain Reliability Research - Gearbox Bearing Axial Cracking Failure Mode Example

The U.S. Department of Energy's National Renewable Energy Laboratory and Argonne National Laboratory have been conducting wind turbine drivetrain (formerly gearbox) reliability research for many years. Although the drivetrain focus has not changed, detailed projects are adjusted every few years based on dynamic needs seen in the field across the wind industry. This webinar will walk through the research methodology by using wind turbine gearbox bearing axial cracking failure mode as an example. The detailed steps include: 1) top failure mode identification based on actual failure data collected from project partners, 2) bench-top testing to identify possible contributing factors and formulate a damage metric, 3) physics domain modeling and validation through testing, 4) reliability assessment and prognosis based on the physics domain model and data domain inputs, and further enhancement through machine learning algorithms, using actual wind plant operational and failure event data. Hopefully, the presented work is of interest to the IISE community, and some members can apply their expertise to wind turbine and plant applications, helping enhance wind power generation technology advancement and its broader deployment.

axial cracking↗
DOE OSTI2020

Predicting and Managing Risk to Bats at Commercial Wind Farms using Acoustics

Bat populations in North America face novel threats from white-nose syndrome and widespread turbine-related mortality related to the rapidly expanding wind power industry in addition to long-standing pressures from habitat loss and degradation. Bats, unlike most small mammals, are long-lived and slow to reproduce, highlighting the importance of understanding and managing anthropogenic sources of mortality. My dissertation research used acoustic bat detectors to measure bat activity at commercial wind projects, predict patterns in risk, and design strategic measures to reduce fatality rates by curtailing turbine operation during periods when bats are most active. Bats collide with wind turbines only when their rotors are spinning, and risk of turbine-related fatality is therefore a dynamic factor that can be manipulated by curtailing turbine operation when bats are active. We first measured inter-detector variation in metrics of acoustic bat activity to understand how the acoustic detection process may affect inferences related to spatial and temporal variation in bat activity. Using acoustic detectors mounted on top of wind turbines at two commercial wind farms in West Virginia, we then demonstrated that the amount of bat activity recorded when turbines were operating aligned closely with bat fatality rates on multiple scales. Accordingly, the metric of bat activity exposed to turbine operation provides a meaningful, quantitative indicator of turbine-related bat fatality risk. Further, bats responded consistently to changing wind speed and temperature at turbines in both wind farms across multiple years, enabling exposed bat activity to be predicted accurately among turbines and years. Building on these results, we simulated exposure of bats to turbine operation and energy loss for curtailment strategies recommended by state and federal agencies in the United States and Canada. By adjusting parameters such as cut-in wind speeds and temperature thresholds, we demonstrated the ability to design strategic curtailment programs that achieve equivalent or greater predicted reductions in bat activity exposure for substantially less energy-production loss. Characterizing fatality risk on a finer scale using acoustics will help regulatory agencies and the wind industry alike reduce risks of population-level impacts to vulnerable bat species while continuing to expand large-scale renewable energy generation.

17 WIND ENERGY↗
DOE OSTI2020

Using Concentrating Solar Power to Create a Geological Thermal Energy Reservoir for Seasonal Storage and Flexible Power Plant Operation

Abstract We propose a hybrid renewable energy system—a geothermal energy storage system (GeoTES) with solar—to provide low-cost dispatchable power at various timescales from daily, to weekly, to seasonally. GeoTES with solar uses a concentrating solar power collector field to produce hot water that is injected into a sedimentary basin to create a synthetic geothermal resource. The stored geothermal heat can then be dispatched when required by the electrical grid. GeoTES is particularly valuable for a grid with a high penetration of non-flexible renewable technologies such as photovoltaic and wind power. In this work, a sophisticated hybrid model is developed to assess the technical and economic potential of GeoTES by combining IPSEpro, which is a power-cycle simulation tool, and SAM, an economic analysis tool by National Renewable Energy Laboratory (NREL). The analysis shows with proper initial charging period that the heat loss in storage is almost negligible and is a suitable technology for long-term energy storage. Various power-cycle options are evaluated, and the most suitable power cycle is selected for further study. Annual calculations of the GeoTES system indicate that a levelized cost of storage (LCOS) of 12.4 ¢/kWh e can be achieved for seasonal storage of 4000 h; this value is much lower than the existing long-term storage. The LCOS of GeoTES is insensitive to the storage duration above 8 h, unlike battery and molten-salt thermal storage systems. This result demonstrates that GeoTES can be a competitive seasonal storage technology in the future electricity market. The levelized cost of electricity of the GeoTES system is also carefully analyzed and can vary between 10.0 and 16.4 ¢/kWh e , depending on solar-collector prices.

25 ENERGY STORAGE↗
DOE OSTI2026

Processed Data Reformatted

This dataset contains plant-level wind power production data from ERCOT, recorded at 15-minute intervals. The source of this data is ERCOT's 60-Day SCED Disclosure Report (product number NP3-965-ER), with timestamps provided in UTC.

17 WIND 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↗
DOE OSTI2024

Potential of U.S. Marine and Offshore Wind Energy to Power Marine Carbon Dioxide Removal

The goal of this project was to provide a preliminary feasibility assessment of powering different marine carbon dioxide removal (mCDR), carbon capture (mCC), and carbon sequestration (mCS) strategies with marine energy. The investigation focused on methods with clear power requirements at sea beyond monitoring such as artificial upwelling, deep ocean storage, electrochemical (eChem) mCDR and mCC, offshore microalgae cultivation, and seaweed farming and sinking. The project found that eChem mCDR powered by marine energy and offshore wind energy available in the United States could meet global CDR scales needed by 2040 and 2050 to limit warming to 1.5 degrees C by 2100 and marine energy alone could greatly contribute to reaching scales needed by 2040. Note that this preliminary estimate assumes that it is possible to harvest all the marine and offshore wind resources available in the United States with existing technology options. Though the biological methods were limited in scale they still hold promise in developing carbon negative fuels and products which can reduce emissions in the short term.

afforestation↗
DOE OSTI2023

Modeling Systems’ Disruption and Social Acceptance—A Proof-of-Concept Leveraging Reinforcement Learning

As the need for a just and equitable energy transition accelerates, disruptive clean energy technologies are becoming more visible to the public. Clean energy technologies, such as solar photovoltaics and wind power, can substantially contribute to a more sustainable world and have been around for decades. However, the fast pace at which they are projected to be deployed in the United States (US) and the world poses numerous technical and nontechnical challenges, such as in terms of their integration into the electricity grid, public opposition and competition for land use. For instance, as more land-based wind turbines are built across the US, contention risks may become more acute. This article presents a methodology based on reinforcement learning (RL) that minimizes contention risks and maximizes renewable energy production during siting decisions. As a proof-of-concept, the methodology is tested on a case study of wind turbine siting in Illinois during the 2022–2035 period. Results show that using RL halves potential delays due to contention compared to a random decision process. This approach could be further developed to study the acceptance of offshore wind projects or other clean energy technologies.

17 WIND ENERGY↗
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
DOE OSTI2023

2021 Cost of Wind Energy Review [Slides]

This analysis uses representative utility-scale and distributed wind energy projects to estimate the levelized cost of energy (LCOE) for land-based, offshore, and distributed wind power in the United States. Data and results detailed here are derived from 2021 commissioned plants and representative industry data as well as state-of-the-art modeling capabilities. Modeling is conducted to provide more granular detail on specific cost categories. This study represents the 11th annual installment and is intended to provide insight into current component-level costs as well as a basis for understanding variability in wind energy LCOE across the country.

17 WIND ENERGY↗
DOE OSTI2022

Realistic Estimation of CO2 Emission Reductions Due to Building Retrofit

Constant CO2 emission factors of electricity are often used to study the emissions of buildings. However, in the era of increased renewable energy for power generation, emission factors of electricity are dynamically changing. This study is to investigate the impact of dynamic CO2 emission factors, which are more realistic than constant ones, on the emission reductions due to building retrofits. The results reveal that if dynamic CO2 emission factors are used for U.S. medium office buildings, improving the efficiency of lighting or electric equipment will lead to more emission reductions in locations with high wind power penetration (e.g., 12% more reductions in Rochester) and fewer reductions in locations with high solar power penetration (e.g., 8% fewer reductions in San Diego). As for improving envelope insulation, dynamic CO2 emission factors have more impact on the emission reductions in hot climates than in cold climates.

Lou, Yingli↗