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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Geographical variability of summer- and winter-dominant onshore wind

For each geographical region, one of the biggest challenges in reaching a zero-carbon grid is identifying sources of electricity that match the seasonal profile of the load. Summer-dominant solar electricity generation can often be balanced by winter-dominant wind electricity generation. Together with long-duration storage, balanced solar and wind generation are well positioned to provide reliable renewable electricity. However, in some locations the wind may not complement solar energy so well. For example, currently California's wind turbines produce more electricity during summer than winter, raising the question of whether all future wind plants in California will exhibit the same seasonality. As a response to this question, in this paper, we analyzed the generation from existing California wind plants and simulated potential onshore wind resource for the whole state using a metric that reflects the relative wind resource in winter. Our results indicate that the seasonality of the wind can vary for very small spatial difference with more than half of California showing stronger wind resource in the winter compared with the summer despite the current observation of the opposite trend. This study differentiates the seasonality of potential wind resources to inform the creation of a reliable, 100%-renewable-driven grid.

17 WIND ENERGY↗
DOE OSTI2020

How Should Machine Learning Be Successfully Used for Wind Speed Vertical Extrapolation?

An accurate characterization of the wind resource available at hub-height is required for an efficient and bankable wind farm project. However, direct measurement of wind speed at the constantly increasing height of the hub of commercial wind turbines is oftentimes challenging and expensive, so that it is common practice to vertically extrapolate the wind resource from lower and more easily accessible levels. Conventional techniques for wind speed vertical extrapolation include the use of a power law and a logarithmic profile. While simple, the limits in accuracy of these methods have been shown in various studies. Recently, machine learning has been proposed as a new method to vertically extrapolate winds. All the published studies on the topic assess the performance of machine learning techniques in vertically extrapolating the wind resource at the same location where the algorithm has been trained. However, in real-world applications, the wind resource is measured at the instrument location, but it then needs to be extrapolated at hub height at the location of the wind turbines within the find farm. To be able to fully recommend the use of machine learning techniques over the simple power law and logarithmic law, the spatial variability of the performance improvements of the machine learning approaches needs to be assessed. Here, we propose a round-robin validation of a machine learning-based method for wind speed extrapolation. We use 20 months of observations at four locations spanning a 100 km wide region at the Southern Great Plains (SGP) atmospheric observatory, in north-central Oklahoma. At each location, we train a random forest to predict 30-min average wind speed at 143 m AGL. We use as input features lidar wind speed at 65 m AGL, time of day, sonic anemometer wind speed at 4 m AGL, turbulent kinetic energy, and Obukhov length. First, we perform a same-site comparison of the performance of the proposed random forest against the conventional techniques for wind speed extrapolation (namely power law and logarithmic profile, with widely accepted stability corrections). We find that the random forest outperforms the power law in vertically extrapolating wind speed in all the considered stability regimes, with a 33% reduction in MAE for stable conditions, and a 31% reduction in unstable conditions. Similar results are found when comparing predictions of extrapolated winds from the logarithmic profile and the random forest with the observed values. Next, we propose a round-robin validation, to use the random forest trained at each site to extrapolate wind speed at the remaining three sites. We find that the performance of the random forest approach degrades when the algorithm is tested at a site different than the training one. However, even under those circumstances, the machine learning-based approach still outperforms the conventional techniques for wind speed extrapolation, with, on average, a reduction in mean absolute error between 15 and 20% over the conventional methods, with the largest benefits obtained under stable conditions.

Monte Carlo↗
DOE OSTI2013

Grassland Bird Distribution and Raptor Flight Patterns in the Competitive Renewable Energy Zones of the Texas Panhandle [Thesis]

The consistent wind resource in the Great Plains of North America has encouraged the development of wind energy facilities across this region. In the Texas Panhandle, a high quality wind resource is only one factor that has led to the expansion of wind energy development. Other factors include federal tax incentives and the availability of subsidies. Moreover, the State Renewable Portfolio Standards (RPS), mandating production of 10,000 mega-watts of renewable energy in the state by 2025, has contributed to an amicable regulatory and permitting environment (State Energy Conservation Office 2010). Considering the current rate of development, the RPS will be met in coming years (American Wind Energy Association 2011) and the rate of development is likely to continue. To meet increased energy demands in the face of a chronically constrained transmission grid, Texas has developed a comprehensive plan that organizes and prioritizes new transmission systems in high quality wind resource areas called Competitive Renewable Energy Zones (CREZ). The CREZ plan provides developers a solution to transmission constraints and unlocks large areas of undeveloped wind resource areas. In the northern Texas panhandle, there are two CREZs that are classified Class 3 wind (Class 5 is the highest) and range from 862,725 to 1,772,328 ha in size (Public Utility Commission of Texas 2008). Grassland bird populations have declined more than any other bird group in North America (Peterjohn and Sauer 1999, Sauer et al. 2004). Loss of grassland habitat from agricultural development has been the greatest contributor to the decline of grassland bird populations, but development of non-renewable (i.e., oil, coal, and gas) and renewable energy (i.e., wind, solar, biomass, and geothermal) sources have contributed to the decline as well (Pimentel et al. 2002, Maybe and Paul 2007). The effects of wind energy development on declining grassland bird populations has become an area of extensive research, as we attempt to understand and minimize potential impacts of a growing energy sector on declining bird populations. Based on data from post-construction fatality surveys, two grassland bird groups have been the specific focus of research, passerines (songbird guild) and raptors (birds of prey). The effects of wind energy development on these two groups of birds, both of conservation concern, have been examined over the last decade. The primary focus of this research has been on mortality resulting from collision with wind turbines (Kuvlesky et al. 2007). Most studies just quantify post-construction fatality levels (e.g., Erickson et al. 2002) while very few studies provide a comparison of bird populations prior to development through a Before-After-Control-Impact (BACI) study design. Before-After-Control-Impact studies provide powerful evidence of avian/wind energy relationships (Anderson et al. 1999). Despite repeated urgency on conducting these types of studies (Anderson et al. 1999, Madders and Whitfield 2006, Kuvelsky et al. 2007), few have been conducted in North America. Although several European researchers (Larsson 1994, de Lucas et al. 2007) have used BACI designs to examine whether wind facilities modified raptor behavior, there is a scarcity of BACI data relating to North America grassland ecosystems that examine avian-wind energy relationships. There are less than a handful of studies in the entire United States, let alone the southern short grass prairie ecosystem, that incorporate preconstruction data to form the baseline for post-construction impact estimates (Johnson et al. 2000, Erickson et al. 2002). Although declines in grassland bird populations are well-documented (Peterjohn and Sauer 1999, Sauer et al. 2004), the causal mechanisms affecting the decline of grassland birds with increasing wind energy development in the southern short grass prairie are not well-understood (Kuvlesky et al. 2007, Maybe and Paul 2007). Several factors may potentially affect the bird population when wind turbines are constructed in areas with high bird densities (de Lucas et al. 2007). Habitat fragmentation, noise from turbines, physical movement of turbine blades, and increased vehicle traffic have been suggested as causes of decreased density of nesting grassland birds in Minnesota (Leddy et al. 1999), Oklahoma (O’Connell and Piorkowski 2006), and South Dakota (Shaffer and Johnson 2008). Similarly, constructing turbines in areas where bird flight patterns place them at similar heights of turbine blades increases the potential for bird collisions (Johnson et al. 2000, Hoover 2002). Raptor fatalities have been associated with topographic features such as ridges, saddles and rims where birds use updrafts from prevailing winds (Erickson et al. 2000, Johnson et al. 2000, Barrios and Rodriquez 2004, Hoover and Morrison 2005). Thus, wind energy development can result in indirect (e.g., habitat avoidance, decreased nest success) and direct (e.g., collision fatalities) impacts to bird populations (Anderson et al. 1999). Directly quantifying the level of potential impacts (e.g., estimated fatalities/mega-watthour) from wind energy development is beyond the scope of this study. Instead, I aim to quantify density, occupancy and flight behavior for the two bird groups mentioned earlier: obligate grassland songbirds and raptors, respectively, predict where impacts may occur, and provide management recommendations to minimize potential impacts. The United States Department of Energy (DOE), through the Office of Energy Efficiency and Renewable Energy Allocation, contracted Texas Tech University to investigate grassland bird patterns of occurrence in the anticipated CREZ in support of DOE’s 20% Wind Energy by 2030 initiative. In cooperation with Iberdrola Renewables, Inc., studies initiated by Wulff (2010) at Texas Tech University were continued at an area proposed for wind energy development and a separate reference site unassociated with wind energy development. I focused on four primary objectives and this thesis is accordingly organized in four separate chapters that address grassland bird density, grassland bird occupancy, raptor flight patterns, and finally I summarize species diversity and composition. The following chapters use formatting from the Journal of Wildlife Management guidelines (Block et al. 2011) with modifications as required by the Texas Tech University Graduate School. 1) I estimate pre-construction bird density patterns using methods that adjust for imperfect detection. I used a distance sampling protocol that effectively accounts for incomplete detection in the field where birds are present but not detected (Buckland et al. 2001). I improved density estimates with hierarchical distance sampling models, a modeling technique that effectively incorporates the detection process with environmental covariates that further influence bird density (Royle et al. 2004, Royle and Dorazio 2008). Covariates included road density and current oil and gas infrastructure to determine the relationship between existing energy development and bird density patterns. Further, I used remote sensing techniques and vegetation field data to investigate how landcover characteristics influenced bird density patterns. I focused species-specific analyses on obligate grassland birds with >70 detections per season namely grasshopper sparrow (Ammodramus savannarum) and horned lark (Eremophila alpestris). Chapter II focuses on hierarchical models that model and describe relationships between grassland bird density and anthropogenic and landscape features. 2) A large number of bird detections (>70) are needed to estimate density using distance sampling and collection of such quantity are often not feasible, particularly for cryptic species or species that naturally occur at low densities (Buckland et al. 2001). Occupancy models operate with far fewer data and are often used as a surrogate for bird abundance when there are fewer detections (MacKenzie and Nichols 2004). I used occupancy models that allow for the possibility of imperfect detection and species abundance to improve estimates of occurrence probability (Royal 2004). I focused species-specific analyses on grassland birds with few detections: Cassin’s sparrow (Peucaea cassinii), eastern meadowlark (Sturnella magna), and upland sandpiper (Bartramia longicauda). Chapter III uses a multi-season dynamic site occupancy model that incorporates bird abundance to better estimate occurrence probability. 3) When I considered the topographic relief of the study sites, the proposed design of the wind facility and its location within the central U.S. migratory corridor, I expanded the study to investigate raptor abundance and flight behavior (Hoover 2002, Miller 2008). I developed a new survey technique that improved the accuracy of raptor flight height estimates and compared seasonal counts and flight heights at the plateau rim and areas further inland. I used counts and flight behaviors to calculate species-specific collision risk indices for raptors based on topographic features. I focused species-specific analyses on raptors with the highest counts: American kestrel (Falco sparverius), northern harrier (Circus cyaneus), red-tailed hawk (Buteo jamaicensis), Swainson’s hawk (Buteo swainsoni), and turkey vulture (Cathartes aura). Chapter IV describes patterns of seasonal raptor abundance and flight behavior and how topography modulates collision risk with proposed wind energy turbines. 4) Finally, for completeness, in Chapter V I summarize morning point count data for all species and provide estimates of relative composition and species diversity with the Shannon-Wiener Diversity Index (Shannon and Weaver 1949).

17 WIND ENERGY↗
DOE OSTI2025

Wind and Weather Variability within the Californian Offshore Wind Energy Areas

Weather variability over the Northeast Pacific (NEP) region and its influence on wind resources within the Californian offshore wind energy areas (WEAs) at Humboldt and Morro Bay are characterized using 20-years reanalysis model and satellite data. The hub-height (180 m) winds at both locations are predominantly northwesterly driven by the NEP high pressure system, with strong coastal gradients in surface pressure, fluxes, planetary boundary layer (PBL) depths and cloudiness. These sharp coastal gradients and strong annual cycles of temperature and moisture advections pose potential challenges in accurately modeling the local wind resource. Hub-height wind speeds and power capacity factors significantly vary for different regimes of PBL depths, surface fluxes and rain area fractions. This highlights the importance of studying the physical mechanisms driving these weather regimes, hence our analysis of how large-scale NEP weather variability drives the local meteorology at the WEAs. Furthermore, at both WEAs, PBL tops and cloud boundaries intersect the rotor layer (80-280 m) more than 30% and 20% of the time, respectively. While PBL depths significantly modulates hub-height winds and power, cloud boundaries do not have a similar impact, likely due to reanalysis errors in simulating cloud boundaries accurately. These findings underscore the challenges in deploying tall wind turbines in shallow cloudy boundary layers, where the interaction between clouds, precipitation, and atmospheric layers can impact turbine efficiency. As turbines grow taller and are deployed in more complex meteorological conditions, understanding these interactions is crucial for improving wind power forecasting and optimizing energy production in coastal regions.

17 WIND ENERGY↗
DOE OSTI2021

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub-height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup in which a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, for which only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both of the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R 2 >0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability in the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.

17 WIND ENERGY↗
DOE OSTI2011

DOE: Integrating Southwest Power Pool Wind Energy into Southeast Electricity Markets

Wind power development in the United States is outpacing previous estimates for many regions, particularly those with good wind resources. The pace of wind power deployment may soon outstrip regional capabilities to provide transmission and integration services to achieve the most economic power system operation. Conversely, regions such as the Southeastern United States do not have good wind resources and will have difficulty meeting proposed federal Renewable Portfolio Standards with local supply. There is a growing need to explore innovative solutions for collaborating between regions to achieve the least cost solution for meeting such a renewable energy mandate. The DOE-funded project 'Integrating Southwest Power Pool Wind Energy into Southeast Electricity Markets' aims to evaluate the benefits of coordination of scheduling and balancing for Southwest Power Pool (SPP) wind transfers to Southeastern Electric Reliability Council (SERC) Balancing Authorities (BAs). The primary objective of this project is to analyze the benefits of different balancing approaches with increasing levels of inter-regional cooperation. Scenarios were defined, modeled and investigated to address production variability and uncertainty and the associated balancing of large quantities of wind power in SPP and delivery to energy markets in the southern regions of the SERC. The primary analysis of the project is based on unit commitment (UC) and economic dispatch (ED) simulations of the SPP-SERC regions as modeled for the year 2022. The UC/ED models utilized for the project were developed through extensive consultation with the project utility partners, to ensure the various regions and operational practices are represented as accurately as possible realizing that all such future scenario models are quite uncertain. SPP, Entergy, Oglethorpe Power Company (OPC), Southern Company, and the Tennessee Valley Authority (TVA) actively participated in the project providing input data for the models and review of simulation results and conclusions. While other SERC utility systems are modeled, the listed SERC utilities were explicitly included as active participants in the project due to the size of their load and relative proximity to SPP for importing wind energy. The analysis aspects of the project comprised 4 primary tasks: (1) Development of SCUC/SCED model of the SPP-SERC footprint for the year 2022 with only 7 GW of installed wind capacity in SPP for internal SPP consumption with no intended wind exports to SERC. This model is referred to as the 'Non-RES' model as it does not reflect the need for the SPP or SERC BAs to meet a federal Renewable Energy Standard (RES). (2) Analysis of hourly-resolution simulation results of the Non-RES model for the year 2022 to provide project stakeholders with confidence in the model and analytical framework for a scenario that is similar to the existing system and more easily evaluated than the high-wind transfer scenarios that are analyzed subsequently. (3) Development of SCUC/SCED model of the SPP-SERC footprint for the year 2022 with sufficient installed wind capacity in SPP (approximately 48 GW) for both SPP and the participating SERC BAs to meet an RES of 20% energy. This model is referred to as the 'High-Wind Transfer' model with several different scenarios represented. The development of the High-Wind Transfer model not only included identification and allocation of SPP wind to individual SERC BAs, but also included the evaluation of various methods to allow the model to export the SPP wind to SERC without developing an actual transmission plan to support the transfers. (4) Analysis of hourly-resolution simulation results of several different High-Wind Transfer model scenarios for the year 2022 to determine balancing costs and potential benefits of collaboration among SPP and SERC BAs to provide the required balancing.

17 WIND ENERGY↗
DOE OSTI2011

Analysis of Unit-Level Changes in Operations with Increased SPP Wind from EPRI/LCG Balancing Study

Wind power development in the United States is outpacing previous estimates for many regions, particularly those with good wind resources. The pace of wind power deployment may soon outstrip regional capabilities to provide transmission and integration services to achieve the most economic power system operation. Conversely, regions such as the Southeastern United States do not have good wind resources and will have difficulty meeting proposed federal Renewable Portfolio Standards with local supply. There is a growing need to explore innovative solutions for collaborating between regions to achieve the least cost solution for meeting such a renewable energy mandate. The Department of Energy funded the project 'Integrating Midwest Wind Energy into Southeast Electricity Markets' to be led by EPRI in coordination with the main authorities for the regions: SPP, Entergy, TVA, Southern Company and OPC. EPRI utilized several subcontractors for the project including LCG, the developers of the model UPLAN. The study aims to evaluate the operating cost benefits of coordination of scheduling and balancing for Southwest Power Pool (SPP) wind transfers to Southeastern Electric Reliability Council (SERC) Balancing Authorities (BAs). The primary objective of this project is to analyze the benefits of regional cooperation for integrating mid-western wind energy into southeast electricity markets. Scenarios were defined, modeled and investigated to address production variability and uncertainty and the associated balancing of large quantities of wind power in SPP and delivery to energy markets in the southern regions of the SERC. DOE funded Oak Ridge National Laboratory to provide additional support to the project, including a review of results and any side analysis that may provide additional insight. This report is a unit-by-unit analysis of changes in operations due to the different scenarios used in the overall study. It focuses on the change in capacity factors and the number of start-ups required for each unit since those criteria summarize key aspects of plant operations, how often are they called upon and how much do they operate. The primary analysis of the overall project is based on security-constrained unit commitment (SCUC) and economic dispatch (SCED) simulations of the SPP-SERC regions as modeled for the year 2022. The SCUC/SCED models utilized for the project were developed through extensive consultation with the project utility partners, to ensure the various regions and operational practices are represented as best as possible in the model. SPP, Entergy, Oglethorpe Power Company (OPC), Southern Company, and the Tennessee Valley Authority (TVA) actively participated in the project providing input data for the models and review of simulation results and conclusions. While other SERC utility systems are modeled, the listed SERC utilities were explicitly included as active participants in the project due to the size of their load and relative proximity to SPP for importing wind energy.

17 WIND ENERGY↗
DOE OSTI2023

Developing a 20-year high-resolution wind data set for Puerto Rico

The purpose of this study is to develop a high-resolution wind resource data set for Puerto Rico as part of the wind resource assessment in the Puerto Rico Grid Resilience and Transition to 100 % Renewable Energy Study (PR100) project. The Weather and Research and Forecasting (WRF) is used to model 20-years (2001-2020) of wind resource data on a 3-km grid for the Puerto Rico region. Because accurately representing the planetary boundary layer (PBL)-physics in the WRF is key to accurately model low-level wind speed, 11 different PBL schemes are examined and evaluated to find a WRF configuration that can provide the most accurate wind data. The modeled wind speeds are validated with buoy observations, and the Shin-Hong PBL scheme is selected to generate the final wind data set. Here, the overall results demonstrate that the high-resolution data correctly represents the climate of Puerto Rico (e.g., a dominant easterly trade wind). At 160 m above ground, the southern regions of Puerto Rico show strong offshore wind speed (>8 m/s on average) throughout the daytime and nighttime. For land-based wind, average wind speed ranges from 5 m/s-7 m/s during daytime, whereas notable high wind speeds (>9 m/s) appear in the mountain regions during nighttime.

17 WIND ENERGY↗
DOE OSTI2015

2015 Key Wind Program and National Laboratory Accomplishments

The U.S. Department of Energy (DOE) Wind Program is committed to helping the nation secure cost-competitive sources of renewable energy through the development and deployment of innovative wind power technologies. By investing in improvements to wind plant design, technology development, and operation as well as developing tools to identify the highest quality wind resources, the Wind Program serves as a leader in making wind energy technologies more competitive with traditional sources of energy and a larger part of our nation’s renewable energy portfolio.

wind, accomplishments↗
DOE OSTI2020

How Wrong Can the Operational AEP Uncertainty Estimate Be When We Ignore the Correlations Between the Uncertainty Components?

Calculations of wind farm annual energy production (AEP) on operational data are essential for a variety of financial transactions during the life of wind plants. The AEP estimate is associated with an uncertainty value, which is calculated by combining contributions connected to on-site measurements, long-term reference measurements, losses, regression, windiness adjustment, and wind resource interannual variability. Although very limited documentation on the topic exists, the conventional approach currently used by the wind energy community to assess the uncertainty connected to the operational AEP estimate assume that the different uncertainty components are uncorrelated and therefore calculates the overall uncertainty with a sum of squares approach. In this analysis, we contrast the traditional technique to estimate the overall AEP uncertainty by ignoring the correlation between its different components with a novel Monte Carlo based approach, which can instead take into account the correlation between different uncertainty categories. We consider monthly operational data from 472 wind farms from the Energy Information Administration (EIA) 923 database to assess the difference between the two approaches. Long-term wind speed data needed for the AEP assessment are taken from three reanalysis products: the Modern-Era Retrospective analysis for Research and Applications v2 (MERRA-2), the European Reanalysis Interim (ERA-interim), and the National Centers for Environmental Prediction v2 (NCEP-2). The results of the Monte Carlo approach show that three pairs of AEP uncertainty components do show a statistically significant correlation: the uncertainty connected with wind resource inter-annual variability is positively correlated with the one related to the windiness correction and negatively correlated with the one due to the regression, and the wind measurement uncertainty is positively correlated with the regression uncertainty. All these correlations, which are found between uncertainty components which are not only part of an operational analysis, but also of a wind resource assessment, are currently ignored in the conventional techniques used as industry standard. We further investigate the causes of these correlations, in terms of common dependencies of different uncertainty components on wind resource variability, number of data points, and quality of the regression between wind speed and energy production data. Next, we quantify the error in the current industry standard technique, in terms of the percentage difference in total uncertainty calculated with the two considered approaches, for all the analyzed wind farms. We find a mean absolute percentage difference of about 6%, with the largest differences being greater than 20%. The data clearly confirm that ignoring the actual correlation between the uncertainty components can lead to large errors in the assessment of the operational AEP uncertainty, and the proposed Monte Carlo approach should be preferred.

Monte Carlo↗
DOE OSTI2018

Evaluating the Methodologies of Assessing Long-Term Variability of Wind Speed

Wind resources vary from year to year, and hence the long-term variability of wind speed is a key component of the overall uncertainty in the wind resource assessment process. Because wind-turbine power generation is highly sensitive to wind speed, the variability of wind resources has important implications for renewable power production. As many approaches of calculating the spread of distribution exist, we aim to identify the methods most appropriate for all types of wind-speed distributions. We review and compare various methods, such as the Coefficient of Variation (CoV) and the Robust Coefficient of Variation (RCoV). Because the length of data available also affects the resultant spread, we determine the minimum length of wind-speed records to quantify variability accurately. We use the net monthly energy production of 607 wind farms in the United States from the Energy Information Administration and the wind speeds of the MERRA-2 reanalysis dataset from NASA. After filtering the energy data via linear regression, we assess the accuracy of the variability calculations by comparing the correlations between the wind-speed variability estimates and the variabilities of wind-farm power productions. Results from statistically robust and resistant methods yield higher correlations between wind-speed and energy-production variabilities, as robust methods do not assume Gaussian wind-speed distributions and resistant methods are insensitive to wind speed extremes. We recommend RCoV to systematically estimate long-term variability, with 10 years of monthly mean data to achieve 90% statistical confidence. We will also provide generalized guidance on how to estimate long-term variability including case studies where we identify regions with high wind speeds and low wind-speed RCoVs, such as the Great Plains and the Upper Midwest in central United States.

17 WIND ENERGY↗
DOE OSTI2021

An overview of wind-energy-production prediction bias, losses, and uncertainties

Abstract. The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the reported average energy prediction bias is declining. Herein, we present a literature review of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary of how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the article.

17 WIND ENERGY↗
DOE OSTI2013

Feasibility of Wind to Serve Upper Skagit’s Bow Hill Tribal Lands and Feasibility Update for Residential Renewable Energy

A two year wind resource assessment was conducted to determine the feasibility of developing a community scale wind generation system for the Upper Skagit Indian Tribe's Bow Hill land base, and the project researched residential wind resource technologies to determine the feasibility of contributing renewable wind resource to the mix of energy options for our single and multi-family residential units.

17 WIND ENERGY↗
DOE OSTI2012

Energy By Design: Science-Based Wind Energy Siting to Avoid Environmental Impact In the Continental United States (Final Technical Report)

The United States has abundant wind resources, such that only about 3% of the resource would need to be developed to achieve the goal of producing 20% of electricity in the United States by 2030. Inappropriately sited wind development may result in conflicts with wildlife that can delay or derail development projects, increase projects costs, and may degrade important conservation values. The most cost-effective approach to reducing such conflicts is through landscape-scale siting early in project development. To support landscape scale siting that avoids sensitive areas for wildlife, we compiled a database on species distributions, wind resource, disturbed areas, and land ownership. This database can be viewed and obtained via http://wind.tnc.org/awwi. Wind project developers can use this web tool to identify potentially sensitive areas and areas that are already disturbed and are therefore likely to be less sensitive to additional impacts from wind development. The United States goal of producing 20% of its electricity from wind energy by the year 2030 would require 241 GW of terrestrial nameplate capacity. We analyzed whether this goal could be met by using lands that are already disturbed, which would minimize impacts to wildlife. Our research shows that over 14 times the DOE goal could be produced on lands that are already disturbed (primarily cropland and oil and gas fields), after taking into account wind resource availability and areas that would be precluded from wind development because of existing urban development or because of development restrictions. This work was published in the peer reviewed science journal PLoS ONE (a free online journal) and can be viewed here: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0017566. Even projects that are sited appropriately may have some impacts on wildlife habitat that can be offset with offsite compensatory mitigation. We demonstrate one approach to mapping and quantifying mitigation costs, using the state of Kansas as a case study. Our approach considers a range of conservation targets (species and habitat) and calculates mitigation costs based on actual costs of the conservation actions (protection and restoration) that would be needed to fully offset impacts. This work was published in the peer reviewed science journal PLoS ONE (a free online journal) and can be viewed here: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0026698.

17 WIND ENERGY↗
DOE OSTI2015

Carolina Offshore Wind Integration Case Study: Phases I and II Final Technical Report

Duke Energy performed a phase 1 study to assess the impact of offshore wind development in the waters off the coasts of North Carolina and South Carolina. The study analyzed the impacts to the Duke Energy Carolinas electric power system of multiple wind deployment scenarios. Focusing on an integrated utility system in the Carolinas provided a unique opportunity to assess the impacts of offshore wind development in a region that has received less attention regarding renewables than others in the US. North Carolina is the only state in the Southeastern United States that currently has a renewable portfolio standard (RPS) which requires that 12.5% of the state’s total energy requirements be met with renewable resources by 2021. 12.5% of the state’s total energy requirements in 2021 equates to approximately 17,000 GWH of energy needed from renewable resources. Wind resources represent one of the ways to potentially meet this requirement. The study builds upon and augments ongoing work, including a study by UNC to identify potential wind development sites and the analysis of impacts to the regional transmission system performed by the NCTPC, an Order 890 planning entity of which DEC is a member. Furthermore, because the region does not have an independent system operator (ISO) or regional transmission organization (RTO), the study will provide additional information unique to non-RTO/ISO systems. The Phase 2 study builds on the results of Phase 1 and investigates the dynamic stability of the electrical network in Task 4, the operating characteristics of the wind turbines as they impact operating reserve requirements of the DEC utility in Task 5, and the production cost of integrating the offshore wind resources into the DEC generation fleet making comparisons to future planned operation without the addition of the wind resources in Task 6.

17 WIND ENERGY↗
DOE OSTI2026

Evaluation of a high-resolution regional climate simulation for surface and hub-height wind climatology over North America

Assessing the availability of key wind resources requires augmenting observations to support the implementation of wind energy infrastructure. However, observations are limited, necessitating the development of high-resolution, long-term gridded datasets. This study presents a robust, dynamically downscaled climatological dataset, offering 20 years of hourly wind data at a 4 km spatial resolution across North America, and evaluates its performance against observations, including meteorological towers and automated surface-observing system (ASOS) stations, as well as coarse-resolution reanalysis data (the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5)). Results demonstrate that the downscaled high-resolution wind data outperform ERA5 in regions of complex terrain and coastal areas, with improved overlap coefficients for wind data distributions and reduced root mean square errors (RMSEs) for hub-height and near-surface diurnal wind patterns. The downscaled simulation also captures the synoptic drivers of seasonal wind direction patterns reasonably well, indicated by high wind rose similarity indices. This study also provides an analysis of interannual variability, utilizing the dataset's full 20-year period, and model uncertainty, generated by varying model initial conditions and physics parameterizations across 1-year ensemble members, which are key considerations for wind resource assessment in wind farm development.

17 WIND ENERGY↗
DOE OSTI2020

Wind ramp events validation in NWP forecast models during the second Wind Forecast Improvement Project (WFIP2) using the Ramp Tool and Metric (RT&M)

The second Wind Forecast Improvement Project (WFIP2) is a multi-agency field campaign held in the Columbia Gorge area (October 2015 - March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well-known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011 - August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and 3 lidars, and to forecasts from the High Resolution Rapid Refresh (HRRR), 3-km, and from the High Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.

Djalalova, Irina V.↗
DOE OSTI2017

Field evaluation of remote wind sensing technologies: Shore-based and buoy mounted LIDAR systems

In developing a national energy strategy, the United States has a number of objectives, including increasing economic growth, improving environmental quality, and enhancing national energy security. Wind power contributes to these objectives through the deployment of clean, affordable and reliable domestic energy. To achieve U.S. wind generation objectives, the Wind and Water Power Program within the Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE) instituted the U.S. Offshore Wind: Removing Market Barriers Program in FY 2011. Accurate and comprehensive information on offshore wind resource characteristics across a range of spatial and temporal scales is one market barrier that needs to be addressed through advanced research in remote sensing technologies. There is a pressing need for reliable offshore wind-speed measurements to assess the availability of the potential wind energy resource in terms of power production and to identify any frequently occurring spatial variability in the offshore wind resource that may impact the operational reliability and lifetime of wind turbines and their components and to provide a verification program to validate the “bankability” of the output of these alternative technologies for use by finance institutions for the financing of offshore wind farm construction. The application of emerging remote sensing technologies is viewed as a means to cost-effectively meet the data needs of the offshore wind industry. In particular, scanning and buoy mounted LIDAR have been proposed as a means to obtain accurate offshore wind data at multiple locations without the high cost and regulatory hurdles associated with the construction of offshore meteorological towers. However; before these remote sensing technologies can be accepted the validity of the measured data must be evaluated to ensure their accuracy. The proposed research will establish a unique coastal ocean test-bed in the Mid-Atlantic for the evaluation of LIDAR-based wind measurement systems to validate the accuracy of remotely measured wind data in marine applications. Specifically, the test-bed will be utilized to systematically evaluate the capability of emerging scanning LIDAR and buoy mounted vertically profiling LIDAR by: (1) Evaluating a fixed scanning LIDAR against land-based 50 and 60 meter high meteorological masts fitted with research quality cup-vane and/or sonic anemometers; (2) Evaluating a buoy mounted vertically profiling LIDAR fixed on land and floating in a sheltered bay against a co-located 60 meter high meteorological mast fitted with a research quality cup-vane and/or sonic anemometers and the fixed scanning LIDAR; and (3) Offshore field evaluation of both LIDAR platforms through a comparison of the fixed scanning LIDAR data and data obtained by the buoy mounted LIDAR located 10 miles offshore. The proposed research will systematically validate Light Detection and Ranging (LIDAR) based wind measurement systems and assess the temporal and spatial variability of the offshore wind resource in the Mid-Atlantic east of New Jersey. The goal of the proposed project is to address the technical and commercial challenges of the offshore wind energy industry by validating and assessing cost-effective, over ocean wind resource characterization technologies. The objective is to systematically evaluate the capability of both scanning and vertically profiling LIDARs to accurately measure 3D wind fields through comparison with fixed met masts and intercomparison among LIDAR platforms. Once validated, data collected by both buoy mounted vertically profiling LIDARs and shore-based, pulsed horizontally scanning LIDARs can be used to accurately assess offshore wind resources and to quantify the spatial and temporal variability in the offshore wind fields. One of the fundamental research questions to be addressed in phase 1 is the assessment of various measurement and data processing schemes to retrieve accurate wind vectors in the marine environment over large sampling ranges (10 to 12 km) and varying atmospheric aerosol levels. Atmospheric conditions and aerosol content within the coastal ocean region of the Mid-Atlantic seaboard of the US can vary significantly over short time periods in response to frontal passages and extratropical and tropical low pressure system passage offshore of the coast. Since aerosols provide the scattering medium for the determination of LIDAR Doppler shifts in the atmosphere the accuracy and range of LIDAR derived velocity measurements as a function of variation in aerosol content in the marine environment is a key research question to be addressed. In phase 1, it is desired to capture as much variation in atmospheric conditions and aerosol content as possible. To this end, collocated measurements of LIDAR and standard anemometer wind fields will be captured by the project PIs over all four seasons and during specific events (e.g., coastal low pressure system passage) in year 1. Additionally, since the meteorological masts are permanent structures, additional events can be captured over the three year duration of the field research project. All research instruments are owned by Fishermen’s Energy and made available to the PIs though a lease agreement as part of the DOE grant. Energy Fishermen’s Energy will be responsible for the operation and maintenance of the scanning LIDAR and met mast anemometers. On a daily basis, environmental data and systems performance indicators will be transmitted from each measurement station to the Fishermen’s project team consisting of both in-house personnel and equipment manufacturer engineers. Data sets include compiled LIDAR files as well as data sets from ancillary sensors. Diagnostic parameters to be monitored include standard deviations of measured values, battery levels and charging systems output, and the operational status. Once data have been confirmed as complete and reliable, files will be transferred to the Garrad Hassan (a subcontractor to Fishermen’s Energy) for incorporation in to the validation database, which is accessible to other scientific team members. Data collection times and durations will be determined by the PI and Co-PIs in consultation with instrument engineers to ensure the capture of data representative of the expected range of mid-Atlantic atmospheric conditions (e.g., temperature, moisture, coastal low pressure systems, tropical systems, rain, snow, fog). The collection and processing of the data is a function of site specific measurement requirements (Kelley et.al. 2007; Hannon et.al. 2008). To determine the optimal profiles of wind speed and direction from the LIDAR radial velocities as a function of azimuth angle, rigorous estimates of the bias and random error of each radial velocity estimate are required. Lockheed Martin Coherent Technologies, Inc., under contract with Fishermen’s Energy, will provide analyses of raw and processed data using various scan patterns to determine optimal performance settings for the pulsed scanning LIDAR. Once optimized, appropriate processing and analyses techniques will be evaluated by Garrad Hassan for use in validating the accuracy of the LIDAR wind field measurements against the standard anemometer measurements from the meteorological masts. The most attractive capability of the scanning LIDAR is the ability to provide high spatial resolution observations in a three-dimensional volume which provides superior statistical accuracy due to the large number of samples obtained. Each radial scan provides measurements in 100 range gates over a distance of 10 to 12 km at an update rate of 5 to 10 Hz and rotation of 2.5° per second. Each rotation at a fixed azimuth requires 2.4 minutes. Depending on the number of azimuths desired a complete scan can take up to 10 minutes or longer to complete. Once collected the radial velocities are processed to produce vector wind velocity estimates based on a set of data distributed in angle and range around points of interest, typically a standard grid within the radial wind map. To calculate wind vectors over a limited spatial area of interest for the comparison of data with other measurement platforms a localized least-squares approach has been applied by Hannon et.al. (2008) and a Variation Assimilation (VAR) processing technique has been applied by Chan and al Assimilation (VAR) processing technique has been applied by Chan and Shao (2006). Additionally, Kelley et.al. (2007) applied a “stare” technique that fixed a scanning LIDAR in both azimuth and elevation angles to measure over collocated sampling volumes of the LIDAR and a 3D sonic anemometer mounted to a fixed mast. Although the stare technique is limited to wind directions aligned with the sampling radial of the LIDAR, it does provide a direct comparison of sampling volumes. Each of the processing techniques described above (and possibly others) will be evaluated to determine the validity of the LIDAR derived wind fields in the marine environment. Numerical methods such as linear regression and comparison of probability density functions of wind fields measured by each instrument platform will be used to assess the processing techniques. Linear regression has the advantage of directly evaluating corresponding pairs of wind data measured by each instrument and can lend insight into deviations and bias between instruments as a function of wind speed. Assessment of the coherency between the probability density function of the wind measured by each instrument provides insight into processes that may not be accurately resolved by each instrument at specific frequencies. Once processed and assessed the most appropriate technique will be utilized to provide valid wind measurements from the pulsed scanning LIDAR. A detailed analysis of the measurement data from the LIDAR and the three meteorological towers and comparison of the coastal wind characteristics from the different systems will be performed by the CO_PI at NREL. This analysis will evaluate how the WT LIDAR performance and measurement of the wind characteristics vary with distance from the LIDAR and by atmospheric conditions; using the tower measurements at different distances (4.8, 9.6, and 19 km) from the LIDAR as a reference. The comparative analysis will include, to the extent possible, evaluation of parameters such as wind speed and direction distributions, wind shear, turbulence intensity and their variations by atmospheric conditions, month or season, and time of day.

17 WIND ENERGY↗