Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Wind Power Production”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Simulated meteorological impacts of offshore wind turbines and sensitivity to the amount of added turbulence kinetic energy

Offshore wind energy projects are currently in development off the east coast of the United States and may influence the local meteorology of the region. Wind power production and other commercial uses in this area are related to atmospheric conditions, and so it is important to understand how future wind plants may change the local meteorology. In the absence of measurements of potential wind plant impacts on meteorology, simulations offer the next-best possible insight into wake effects on boundary layer height, temperature, fluxes, and wind speeds. However, simulation tools that capture these effects offer multiple options for representing the amount of turbine-added turbulence that may impact assessments of micrometeorological effects. To explore this sensitivity, we compare 1 year of simulations from the Weather Research and Forecasting (WRF) model with and without wind plants incorporated, focusing on the lease area south of Massachusetts and Rhode Island. The simulations with wind plants are repeated to include both the maximum and minimum amounts of added turbulence to provide bounds on the potential impacts. We assess changes in wind speeds, 2 m temperature, surface heat flux, turbulence kinetic energy (TKE), and boundary layer height during different stability classifications and ambient wind speeds over the entire year and compare results for the degree of added turbulence in the wind plant simulations. Because the wake behavior may be a function of boundary layer stability, in this paper, we also present a machine learning algorithm to quantify the area and distance of the wake generated by the wind plant. This analysis enables us to identify the relationship between wake extent and boundary layer height. We find that hub-height wind speed is reduced within and downwind of the wind plant, with the strongest impacts occurring during stable conditions and faster wind speeds in region 3 of the turbine power curve, although impacts lessen as wind speeds increase past 15 m s−1. In contrast, wind speeds near the surface decrease when no turbine-added turbulence is included but can increase for stably stratified conditions when 100 % of possible TKE is included in the simulations. TKE increases at hub height in the simulations with added TKE for all stability classes, suggesting that atmospheric stability does not immediately modify the TKE generated by turbines. Negligible changes in hub-height TKE manifest in the simulations without the added TKE. At the surface, TKE increases in the simulations with maximum added turbulence only for unstable conditions. In the no-added-turbulence simulations, surface TKE decreases slightly in neutral and unstable simulations. Differences in 2 m temperatures and surface heat fluxes are small but vary considerably with atmospheric stability and the amount of added TKE. Boundary layer heights increase within the wind plant when turbine-added turbulence is included and decrease slightly downwind during stable conditions. In contrast, with no added turbulence, the boundary layer height is in general reduced in stable conditions with wind speeds less than 15 m s −1 and slightly increased in neutral conditions. Finally, shallower upwind boundary layer heights tend to correlate with larger wake areas and distances, though other factors likely also play a role in determining the extent of the wind plant wake. These simulation-based results provide a bound for micrometeorological impacts of wind plant wakes: simulations that couple the atmosphere to the ocean may reduce these impacts, and we await observational verification.

17 WIND ENERGY↗
DOE OSTI2016

Mid-Atlantic Offshore Wind Interconnection and Transmission (MAOWIT)

This project has carried out a detailed analysis to evaluate the pros and cons of offshore transmission, a possible method to decrease balance-of-system costs and permitting time identified in the DOE Office Wind Strategic Plan (DOE, 2011). It also addresses questions regarding the adequacy of existing transmission infrastructure and the ability of existing generating resources to provide the necessary Ancillary Services (A/S) support (spinning and contingency reserves) in the ISO territory. This project has completed the tasks identified in the proposal: 1. Evaluation of the offshore wind resource off PJM, then examination of offshore wind penetrations consistent with U.S. Department of Energy’s (DOE) targets and with their assumed resource size (DOE, 2011). 2. Comparison of piecemeal radial connections to the Independent System Operator (ISO) with connections via a high-voltage direct current (HVDC) offshore network similar to a team partner. 3. High-resolution examination of power fluctuations at each node due to wind energy variability 4. Analysis of wind power production profiles over the Eastern offshore region of the regional ISO to assess the effectiveness of long-distance, North- South transmission for leveling offshore wind energy output 5. Analysis of how the third and fourth items affect the need for ISO grid upgrades, congestion management, and demand for Ancillary Services (A/S) 6. Analysis of actual historic 36-hr and 24-hr forecasts to solve the unit commitment problem and determine the optimal mix of generators given the need to respond to both wind variability and wind forecasting uncertainties.

17 WIND ENERGY↗
DOE OSTI2015

Technology Performance Report: Duke Energy Notrees Wind Storage Demonstration Project

Duke Energy Renewables owns and operates the Notrees Wind Farm in west Texas’s Ector and Winkler counties. The wind farm, which was commissioned in April 2009, has a total capacity of 152.6 MW generated by 55 Vestas V82 turbines, one Vestas 1-V90 experimental turbine, and 40 GE 1.5-MW turbines. The Vestas V82 turbines have a generating capacity of 1.65 MW each, the Vestas V90 turbine has a generating capacity of 1.86 MW, and the GE turbines have a generating capacity of 1.5 MW each. The objective of the Notrees Wind Storage Demonstration Project is to validate that energy storage increases the value and practical application of intermittent wind generation and is commercially viable at utility scale. The project incorporates both new and existing technologies and techniques to evaluate the performance and potential of wind energy storage. In addition, it could serve as a model for others to adopt and replicate. Wind power resources are expected to play a significant part in reducing greenhouse gas emissions from electric power generation by 2030. However, the large variability and intermittent nature of wind presents a barrier to integrating it within electric markets, particularly when competing against conventional generation that is more reliable. In addition, wind power production often peaks at night or other times when demand and electricity prices are lowest. Energy storage systems can overcome those barriers and enable wind to become a valuable asset and equal competitor to conventional fossil fuel generation.

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 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 OSTI2020

How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, also known as shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), β bulk (a measure of bulk rotor-disk-layer veer), β total (a measure of total rotor-disk-layer veer), and rotor-equivalent wind speed (REWS; a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWS θ , although statistically significant differences in power production do not occur between REWS and REWS θ at our site. When REWS differs from the hub-height wind speed (as measured by either the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW or up to 5 % of the rated power for a single 1.5 MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

17 WIND ENERGY↗
DOE OSTI2025

Raw ERCOT 60-Day SCED Disclosure Reports

This dataset contains ERCOT plant-level wind power production data at a 15-minute time resolution. Data at ERCOT are called 60-Day SCED Disclosure Report; data product number NP3-965-ER. Raw data are in local time. DST is handled as "skip hour" (12a, 1a, 3a,...) in spring, "extra hour" (12a, 1a, 2a, 2a, 3a,...) in fall.

17 WIND ENERGY↗
DOE OSTI2024

Probing the atmospheric boundary layer with integrated remote-sensing platforms during the American WAKE ExperimeNt (AWAKEN) campaign

The American WAKE ExperimeNt (AWAKEN) collaboration is an observational-based field campaign in northern Oklahoma intended to analyze the potential influence of onshore wind farms and their collective wakes on wind power production, turbine structural loads, and on the atmospheric boundary layer (ABL). Focusing on the ABL effects, the University of Oklahoma and the Lawrence Livermore National Laboratory collected continuous high-resolution kinematic and thermodynamic profile measurements during 2022 and Summer 2023. The deployment strategy for these campaigns is detailed first, followed by an initial comparison of data from two sites in the AWAKEN domain: a near-farm site to examine collective wake impacts on the ABL, and a far-field site remaining outside the wind farm-waked region. Here, we summarize the datasets available and demonstrate the benefits of these observations and multiple value-added products (VAPs) for investigation of ABL features observed during AWAKEN. We also highlight examples of preliminary analyses, including ABL height detection and nocturnal low-level jet examination, which are produced using novel VAPs based on optimal estimation to retrieve deeper Doppler lidar wind profiles than previously resolved, along with their uncertainty. By including the near-farm and far-field site in these analyses, we identified a pattern of stronger lower-atmospheric mixing at the near-farm site than the far-field site, motivating deeper investigation into the relationship between wind farms and general ABL characteristics. Future analysis will delve deeper into this relationship by examining other ABL characteristics, such as atmospheric stability and convection.

17 WIND ENERGY↗
DOE OSTI2018

Uncertainty quantification in the analyses of operational wind power plant performance

In the present work, we examine the variation introduced in the evaluation of an operating plant's wind power production as a result of the choices analysts make in the processing of the operational data. For this study, an idealized power production for individual turbines over an operational period was predicted by fitting power curves to the turbine production data collected during expected operation (that is, without curtailment or availability losses). A set of 240 possible methods were developed for (a) defining what data represented expected operation and (b) modeling the power curve. The spread in the idealized power production as predicted by the different methods was on average almost 3% for the 100 turbines considered. Such significant variation places a lower bound on the precision with which analysts may employ such data as benchmarks for calibration of their energy estimation processes and limits the potential for identification of refinements to the energy estimation models for improved accuracy.

17 WIND ENERGY↗
DOE OSTI2024

Atmospheric Boundary Layer-Wind Farm Interactions Field Campaign Report

The American WAKE ExperimeNt (AWAKEN) was a field campaign in northern Oklahoma intended to analyze the potential influence of wind farms and their collective wakes on the atmospheric boundary layer (ABL), wind power production, and turbine structural loads. This report summarizes the deployment of instruments by the University of Oklahoma (OU), National Atmospheric and Atmospheric Administration National Severe Storms Laboratory (NOAA NSSL), and Lawrence Livermore National Laboratory (LLNL) during AWAKEN. Two Collaborative Lower Atmosphere Profiling Systems (CLAMPS) and LLNL ZephIR profiling lidars were co-deployed from October 3, 2022, to December 20, 2022 (winter campaign). At the end of the winter campaign, both ZephIR lidars were moved to different AWAKEN sites to be a part of a targeted wake study at the King Plains wind farm. The two CLAMPS were redeployed for a second observation period from July 1st, 2023, to September 28, 2023 (summer campaign; the ZephIR lidars were not co-located with CLAMPS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Impacts of Federal Tax Credit Extensions on Renewable Deployment and Power Sector Emissions

Federal tax credits for renewable energy (RE) have served as one of the primary financial incentives for RE deployment over the last two decades in the United States. In December 2015, the wind power production tax credit and solar investment tax credits were extended for five years as part of the Consolidated Appropriations Act of 2016. This report explores the impact that these tax credit extensions might have on future RE capacity deployment and power sector carbon dioxide (CO2) emissions. The analysis examines the impacts of the tax credit extensions under two distinct natural gas price futures as natural gas prices have been key factors in influencing the economic competitiveness of new RE development. The analysis finds that, in both natural gas price futures, RE tax credit extensions can spur RE capacity investments at least through the early 2020s and can help lower emissions from the U.S. electricity system. More specifically, the RE tax credit extensions are estimated to drive a net peak increase of 48-53 GW in installed RE capacity in the early 2020s -- longer term impacts are less certain. In the longer term after the tax credits ramp down, greater RE capacity is driven by a combination of assumed RE cost declines, rising fossil fuel prices, and other clean energy policies such as the Clean Power Plan. The tax credit extension-driven acceleration in RE capacity development can reduce fossil fuel-based generation and lower electric sector CO2 emissions. Cumulative emissions reductions over a 15-year period (spanning 2016-2030) as a result of the tax credit extensions are estimated to range from 540 to 1420 million metric tonnes CO2. These findings suggest that tax credit extensions can have a measurable impact on future RE deployment and electric sector CO2 emissions under a range of natural gas price futures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Chapter 6: Wind Energy: Effects on Bats

Utility-scale wind energy facilities require no fuel, consume no water, and produce no greenhouse gas emissions or other pollutants during energy production. Wind power currently supplies approximately 6.4% of the electricity consumed in the United States (U.S.), with continued growth expected in the coming years. Although further expansion of wind power is anticipated to provide environmental and economic benefits, there are increasing concerns about bat fatalities occurring at wind energy facilities across North America. Recent estimates place the number of bat fatalities in the several hundreds of thousands on an annual basis, and that number is projected to rise. As a result, population-level impacts of wind turbine-caused mortality are of increasing concern. For example, modeling efforts for a widespread species, the hoary bat (Lasiurus cinereus), indicate that the population could decline by 90% within 50 years, assuming no growth in installed wind power capacity and no significant implementation of conservation measures. Given increasing demand for wind energy and increasing evidence suggesting that bats are attracted to wind turbines, the need to develop cost-effective and practical impact minimization strategies is a high priority. Current strategies include siting restrictions and operational minimization, both of which limit wind power generation. Potential solutions that do not limit power generation include broadcasting ultrasound from wind turbines or using ultraviolet (UV) light to deter bats from approaching and entering the rotor-swept area where fatalities occur. Another possible solution is the development of turbine-surface materials that reduce the relative attractiveness of wind turbine towers to bats. In this chapter, we provide a succinct summary of known impacts of wind energy on bats and present current and future research priorities. We also describe the challenges and opportunities associated with developing and implementing effective solutions to minimize wind turbine-caused bat fatality.

17 WIND ENERGY↗
DOE OSTI2015

Final Report for Project: Impacts of stratification and non-equilibrium winds and waves on hub-height winds

This project used a combination of turbulence-resolving large-eddy simulations, single-column modeling (where turbulence is parameterized), and currently available observations to improve, assess, and develop a parameterization of the impact of non-equilibrium wave states and stratification on the buoy-observed winds to establish reliable wind data at the turbine hub-height level. Analysis of turbulence-resolving simulations and observations illuminates the non-linear coupling between the atmosphere and the undulating sea surface. This analysis guides modification of existing boundary layer parameterizations to include wave influences for upward extrapolation of surface-based observations through the turbine layer. Our surface roughness modifications account for the interaction between stratification and the effects of swell’s amplitude and wavelength as well as swell’s relative motion with respect to the mean wind direction. The single-column version of the open source Weather and Research Forecasting (WRF) model (Skamarock et al., 2008) serves as our platform to test our proposed planetary boundary layer parameterization modifications that account for wave effects on marine atmospheric boundary layer flows. WRF has been widely adopted for wind resource analysis and forecasting. The single column version is particularly suitable to development, analysis, and testing of new boundary layer parameterizations. We utilize WRF’s single-column version to verify and validate our proposed modifications to the Mellor-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization (Nakanishi and Niino, 2004). We explore the implications of our modifications for two-way coupling between WRF and wave models (e.g.,Wavewatch III). The newly implemented parameterization accounting for marine atmospheric boundary layer-wave coupling is then tested in three-dimensional WRF simulations at grid sizes near 1 km. These simulations identify the behavior of simulated winds at the wind plant scale. Overall project conclusions include; In the presence of fast-moving swell (significant wave height Hs = 6.4 m, and phase speed cp = 18 ms -1 ), the atmospheric boundary layer grows more rapidly when waves propagate opposite to the winds compared to when winds and waves are aligned. Pressure drag increases by nearly a factor of 2 relative to the turbulent stress for the extreme case where waves propagate at 180° compared to the pressure gradient forcing. Net wind speed reduces by nearly 15% at hub-height for the 180°-case compared to the 0°-case, and turbulence intensities increase by nearly a factor of 2. These impacts diminish with decreasing wave age; Stratification increases hub height wind speeds and increases the vertical shear of the mean wind across the rotor plane. Fortuitously, this stability-induced enhanced shear does not influence turbulence intensity at hub height, but does increase (decrease) turbulence intensity below (above) hub height. Increased stability also increases the wave-induced pressure stress by ~ 10%; Off the East Coast of the United States during Coupled Boundary Layers Air-Sea Transfer - Low Wind (CBLAST-Low), cases with short fetch include thin stable boundary layers with depths of only a few tens of meters. In the coastal zone, the relationship between the mean wind and the surface fiction velocity (u*(V )) is significantly related to wind direction for weak winds but is not systematically related to the air sea difference of virtual potential temperature, δθv; since waves generally propagate from the south at the Air-Sea Interaction Tower (ASIT) tower, these results suggest that under weak wind conditions waves likely influence surface stress more than stratification does; and Winds and waves are frequently misaligned in the coastal zone. Stability conditions persist for long duration. Over a four year period, the Forschungsplattformen in Nord- und Ostsee Nr. 1 (FINO1) tower (a site with long fetch) primarily experienced weakly-unstable conditions, while stability at the ASIT tower (with a larger influence of offshore winds) experiences a mix of both unstable and stable conditions, where the summer months are predominantly stable. Wind-wave misalignment likely explains the large scatter in observed non-dimensional surface roughness under swell-dominated conditions. Andreas et al.’s (2012) relationship between u* and the 10-m wind speed under predicts the increased u* produced by wave-induced pressure drag produced by misaligned winds and waves. Incorporating wave-state (speed and direction) influences in parameterizations improves predictive skill. In a broad sense, these results suggest that one needs information on winds, temperature, and wave state to upscale buoy measurements to hub-height and across the rotor plane. Our parameterization of wave-state influences on surface drag has been submitted for inclusion in the next publicly available release. In combination, our project elucidates the impacts of two important physical processes (non-equilibrium wind/waves and stratification) on the atmosphere within which offshore turbines operate. This knowledge should help guide and inform manufacturers making critical decisions surrounding design criteria of future turbines to be deployed in the coastal zone. Reductions in annually averaged hub height wind speed error using our new wave-state-aware surface layer parameterization are relatively modest. However since wind turbine power production depends on the wind speed cubed, the error in estimated power production is close to 5%; which is significant and can substantially impact wind resource assessment and decision making with regards to the viability of particular location for a wind plant location. For a single 30-hour forecast, significant reductions in wind speed prediction errors can yield substantially improved wind power forecast skill, thereby mitigating costs and/or increasing revenue through improved; forecasting for maintenance operations and planning; day-ahead forecasting for power trading and resource allocation; and short-term forecasting for dispatch and grid balancing.

17 WIND ENERGY↗
DOE OSTI2021

Power production from the U.S. east coast offshore wind lease areas

The accompanying file include information regarding the set up of WRF simulations of power production and wake extents from offshore wind lease areas along the U.S. east coast, and also data presented in figures in the "Wind power production from the U.S. east coast offshore lease areas" paper and the associated MATLAB data processing code.

17 WIND ENERGY↗
DOE OSTI2020

FAST.Farm Development and Validation of Structural Load Prediction Against Large Eddy Simulations

FAST.Farm is a mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on several principles of the dynamic wake meandering (DWM) model, but also addresses limitations of previous DWM implementations. Previous FAST.Farm studies have shown the similarities and differences between FAST.Farm and large eddy simulations for rigid turbine cases. The objective of this work is to quantify the ability of FAST.Farm to accurately predict turbine structural response in a small wind farm. This is done by comparing FAST.Farm structural response to SOWFA-OpenFAST results for three laterally-aligned turbines. The purpose of this study is to characterize the similarities and differences between FAST.Farm and a higher fidelity model for predicting turbine structural response in a wind farm for differing atmospheric inflows. Strong statistical agreement was found between FAST.Farm and SOWFA-OpenFAST structural response for the non-waked upstream turbine, and good agreement was found for the downstream turbines for most structural quantities. Higher differences were seen for downstream turbines with low ambient turbulence intensity or yawed turbines, suggesting areas for FAST.Farm wake dynamics modeling improvements. For all cases and turbines, small statistical differences were seen between blade deflections and bending moments, with larger differences for tower-top and tower-base bending moments. Overall, the results establish confidence for applying FAST.Farm to wind farm power and loads analyses and identify areas where further model validations and model improvements should be targeted.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

Updated weather model captures subtleties of cold-air pool events

Imagine cold air building up inside a valley or other topographic basin, like a cold swimming pool in the atmosphere. Known in meteorology as cold-air pools, or simply cold pools, these events affect wind plant power production in two key ways. Because they are associated with stagnant air and low winds near the ground, cold pools can severely limit wind energy output over periods of hours to days. Then, when cold pools dissolve, known as cold pool “erosion,” increased wind speeds allow wind power production to recover.

Energy↗