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2016 State of Wind Development in the United States by Region

Significant expansion of wind energy development will be required to achieve the scenarios outlined in the U.S. Department of Energy's (DOE)'s Wind Vision: 20% wind energy by 2030 and 35% wind energy by 2050. Wind energy currently provides nearly 5% of the nation's electricity but has the potential to provide much more. The wind industry and the DOE's Wind Energy Technologies Office are addressing technical wind energy challenges, such as reducing turbine costs and increasing energy production and reliability. The Office recognizes that public acceptance of wind energy can be challenging, depending on the proximity of proposed wind farms to local populations. Informed decision makers and communities equipped with unbiased information about the benefits and impacts of wind energy development are better prepared to navigate the sometimes contentious development process. In 2014, DOE established six Regional Resource Centers (RRCs) across the United States to communicate unbiased, credible information about wind energy to stakeholders through regional networks. The RRCs provide ready access to this information to familiarize the public with wind energy; raise awareness about potential benefits and issues; and disseminate data on siting considerations such as turbine sound and wildlife habitat protection. This document summarizes the status and drivers for U.S. wind energy development during 2016. RRC leaders provided a report of wind energy development in their regions, which was combined with findings from National Renewable Energy Laboratory (NREL) researchers to provide an account of the state of the regions, as well as updates on developments in individual states. NREL researchers and state partners added updates for all states that are not directly supported by an RRC. Accounts for each region include updates on renewable portfolio standards, the Clean Power Plan, workforce development, manufacturing and economic development, and individual state updates for installed wind capacity, ongoing policy developments, planned projects and their status, transmission progress reports, etc. This report also highlights the efforts of the RRCs to engage stakeholders in their individual regions.

17 WIND ENERGY↗

2015 Wind Technologies Market Report

Annual wind power capacity additions in the United States surged in 2015 and are projected to continue at a rapid clip in the coming five years. Recent and projected near-term growth is supported by the industry’s primary federal incentive—the production tax credit (PTC)—having been extended for several years (though with a phase-down schedule, described further on pages 68-69), as well as a myriad of state-level policies. Wind additions are also being driven by improvements in the cost and performance of wind power technologies, yielding low power sales prices for utility, corporate, and other purchasers. At the same time, the prospects for growth beyond the current PTC cycle remain uncertain: growth could be blunted by declining federal tax support, expectations for low natural gas prices, and modest electricity demand growth. This annual report—now in its tenth year—provides a detailed overview of developments and trends in the U.S. wind power market, with a particular focus on 2015. The report begins with an overview of key installation-related trends: trends in U.S. wind power capacity growth; how that growth compares to other countries and generation sources; the amount and percentage of wind energy in individual states; the status of offshore wind power development; and the quantity of proposed wind power capacity in various interconnection queues in the United States. Next, the report covers an array of wind power industry trends: developments in turbine manufacturer market share; manufacturing and supply-chain developments; wind turbine and component imports into and exports from the United States; project financing developments; and trends among wind power project owners and power purchasers. The report then turns to a summary of wind turbine technology trends: turbine size, hub height, rotor diameter, specific power, and IEC Class. After that, the report discusses wind power performance, cost, and pricing trends. In so doing, it describes trends in project performance, wind turbine transaction prices, installed project costs, and operations and maintenance (O&M) expenses. It also reviews the prices paid for wind power in the United States and how those prices compare to short-term wholesale electricity prices and forecasts of future natural gas prices. Next, the report examines policy and market factors impacting the domestic wind power market, including federal and state policy drivers as well as transmission and grid integration issues. The report concludes with a preview of possible near-term market developments. This edition of the annual report updates data presented in previous editions while highlighting key trends and important new developments from 2015. The report concentrates on larger, utility-scale wind turbines, defined here as individual turbines that exceed 100 kW in size.

17 WIND ENERGY↗

2016 Wind Technologies Market Report

The U.S. Department of Energy (DOE)’s Wind Technologies Market Report provides an annual overview of trends in the U.S. wind power market. You can find the report, a presentation, and a data file on the Files tab, below. Additionally, several data visualizations are available in the Data Visualizations tab. Highlights of this year’s report include: -Wind power additions continued at a rapid clip in 2016: $13 billion was invested in new wind power plants in 2016. In 2016, wind energy contributed 5.6% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and 29% to 37% in three of those states—Iowa, South Dakota, and Kansas. -Bigger turbines are enhancing wind project performance: Increased blade lengths, in particular, have dramatically increased wind project capacity factors, one measure of project performance. For example, the average 2016 capacity factor among projects built in 2014 and 2015 was 42.6%, compared to an average of 32.1% among projects built from 2004 to 2011 and 25.4% among projects built from 1998 to 2001. -Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen from their highs in 2008, to $800–$1,100/kW. Overall, the average installed cost of wind projects in 2016 was $1,590/kW, down $780/kW from the peak in 2009 and 2010. -Wind energy prices remain low: After topping out at nearly 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to around 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country (such as Texas, Iowa, Oklahoma). These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. -The supply chain continued to adjust to swings in domestic demand for wind equipment: Wind sector employment reached a new high of more than 101,000 full-time workers at the end of 2016. For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (65-80%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the turbine. -Continued strong growth in wind capacity is anticipated in the near term: With federal tax incentives still available, though declining, various forecasts for the domestic market show expected wind power capacity additions averaging more than 9,000 MW/year from 2017 to 2020.

17 WIND ENERGY↗

2017 Wind Technologies Market Report

The U.S. Department of Energy (DOE)’s Wind Technologies Market Report provides an annual overview of trends in the U.S. wind power market. Highlights of this year’s report include: -Wind power capacity additions continued at a rapid pace in 2017: $11 billion was invested in new wind power plants in 2017. In 2017, wind energy contributed 6.3% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and more than 30% in four of those states—Iowa, Kansas, Oklahoma, and South Dakota. -Bigger turbines are enhancing wind project performance: Increased blade lengths have dramatically increased wind project capacity factors, one measure of project performance, and taller towers appear to be on the horizon. The average 2017 capacity factor among projects built from 2014 through 2016 was 42%, compared to an average of 31.5% among projects built from 2004 to 2011 and 23.5% among projects built from 1998 to 2001. -Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen from their highs in 2008 to $750–$950/kW. Overall, the average installed cost of wind projects in 2017 was $1,610/kW, down $795/kW from the peak in 2009 and 2010. -Wind energy prices remain low: After topping out at 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to around 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country (such as Oklahoma, Nebraska, Kansas). These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. -The domestic supply chain for wind equipment is diverse: Wind sector employment reached a new high of more than 105,000 full-time workers at the end of 2017. For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (70-90%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the turbine. -Continued strong growth in wind capacity is anticipated in the near term: With federal tax incentives still available, various forecasts for the domestic market show expected wind power capacity additions of 8,000 to 11,000 MW/year from 2018 to 2020, with market contraction anticipated beginning in 2021 as those tax incentives are phased out.

17 WIND ENERGY↗

ARCUS Vertical-Axis Wind Turbine (Final Scientific/Technical Report)

While land-based wind energy has become economically competitive with traditional energy generation sources in the U.S., offshore wind is not. For floating offshore wind this difference is even more substantial where the levelized cost of energy (LCOE) is projected to be around 3-5 times more expensive than land-based wind. The turbine capital costs represent around 50% of the LCOE for land-based wind sites, but the increased system costs for floating offshore wind reduce this to 20%. The platform and mooring costs are the single largest contributor to the LCOE for floating offshore wind where their mass must counteract the overturning moment caused by the turbine’s thrust force. Despite the high costs of the platform and relatively low cost of the turbine, current offshore wind turbines are designed essentially the same as for land-based sites. Reducing the LCOE is the greatest challenge to realize the benefits of sustained development of floating offshore wind in the U.S. Reducing the complicated system costs of floating offshore wind will enable the industry to continue to grow and outpace current projections if reduced cost curves can be reached. The ideal wind energy system would remove all mass and cost that is not directly capturing energy from the wind. For floating offshore wind energy systems, this objective is even more significant as increased mass above the water level must be supported by larger and more expensive floating platforms. For this reason, vertical-axis wind turbines (VAWTs) are ideal for floating offshore sites and have several advantages over horizontal-axis wind turbines (HAWTs) at this scale. Large VAWTs offer opportunities for improved energy capture over HAWTs as single units and with reduced wind plant aerodynamic losses through enhanced wake recovery. Additionally, the platform-level placement of the VAWT drivetrain greatly reduces the demands placed on the floating platform and its mass and cost. The ARCUS vertical-axis wind turbine concept (U.S. 11,421,650 B2) is an iteration beyond traditional Darrieus-type VAWTs that replaces the rigid tower with blades that are bent into shape and held in place with tensioned center supports, like a bow. The ARCUS design has been shown to further decrease the VAWT rotor mass properties, with a 50% reduction over traditional Darrieus VAWTs quantified in the ATLANTIS program. The ARCUS VAWT’s efficient use of material for the rotor and turbine support structures combined with its lowered center of gravity enables a tension-leg platform (TLP) with simplified installation procedures. TLPs have been an emerging platform architecture in the Oil and Gas industry and demonstrated to have the lowest hull mass requirements while maintaining stability with minimal roll and pitch deflections in operation. A 22 MW ARCUS turbine has been designed with a three-column TLP that enables quayside integration of the turbine while maintaining system stability during tow-out and installation and having optimal mass and cost properties. A comprehensive analysis shows the optimal ARCUS TLP system design minimizes LCOE through efficient material usage and increased energy capture to yield a competitive LCOE estimate of $\$$55/MWh. A comparison with a reference HAWT, having the same swept area, quantifies the advantages that helped to produce this improved LCOE for the ARCUS concept: (1) 30% reduction in total turbine mass, (2) 70% reduction in turbine center of gravity, and (3) 45% increase in energy production over what is optimal for a HAWT. Intellectual property has been generated through the ATLANTIS program providing opportunities to further reduce the LCOE and improve the performance of the ARCUS turbine and TLP system, expanding the list of innovations to support commercial development of the ARCUS concept.

17 WIND ENERGY↗

Coupled Aerodynamic and Hydrodynamic Hybrid Simulation of Floating Offshore Wind Turbines

The development and innovation of floating offshore wind energy in the U.S. requires detailed high-fidelity observations and measurements of turbine and platform loading due to wind, waves, and currents. However, full-scale and quasi-full-scale experiments require significant financial and temporal investments for construction, experimental testing, and long-term field campaigns. To support the commercial advancement of the offshore wind energy industry, specialized wind tunnel and wave basin experimental facilities are critical to be able to test FOWT designs at small scale under controlled conditions prior to full-scale deployment. Oregon State University (OSU) is internationally known as a leader in water and energy research, development, and testing. The O.H. Hinsdale Wave Research Laboratory (HWRL) and the Wallace Energy Systems and Renewables Facility (WESRF) at OSU have extensive experience building, modeling, monitoring, controlling, and actuating scaled systems. Experiments on wave-structure interaction have been performed at the HWRL since its establishment in 1972. Studies have included the interaction of waves with coastal structures (breakwaters, seawalls, buildings, cylinders, bridges, fixed foundations of offshore wind turbines, etc.) and with floating structures (e.g., wave energy converters, maneuvering of vessels, etc.). Hinsdale is actively used by marine energy technology developers, both for private testing and OSU-collaborative research projects. However, despite the availability of several large-scale facilities for hydrodynamic testing (at OSU and elsewhere in the U.S.), existing experimental laboratories are generally limited in their ability to accurately generate combined wind and wave conditions. The simulation of both wind and waves in experimental testing is complicated due to a number of constraints, including: [i] incompatible similitude laws governing the wind and waves for scaled experiments, [ii] producing accurate wind over a large enough control volume via fans, and [iii] generating wind that reasonably represents the atmospheric boundary layer in existing wave basins/flumes. Hence, physical test data providing insight into the simultaneous wave- and wind-structure response of floating offshore wind components can be difficult to generate. Given the aforementioned challenges in classic hydrodynamic experiments, the motivation of this project is to establish a real-time hybrid simulation (RTHS) approach that can apply aero- and hydro-dynamic loading by augmenting wave-only experimental facilities with virtual aerodynamic forces through numerical models representing the remaining dynamic forces. RTHS is a physical-numerical approach that partitions a prototype system into physical and numerical sub-assemblies that interact with each other through actuators and sensors in real time. In coupling physical and numerical models, the hybrid simulation approach applied herein is ideal for problems with: (1) structures subjected to different scaling laws, such as floating offshore wind turbines subjected to combined aero/hydro-dynamic loading, (2) structures that are too large or complex to be tested entirely in a laboratory setting, such as deep-water mooring applications, and (3) component testing, where the behavior of a portion of the assembly is uncertain but still interacts with other portions of the structure, such as testing the fatigue life of turbine blades. Few U.S. experimental facilities are able to test simultaneous aero- and hydro-dynamic loading and none can accurately produce aero/hydro-dynamic response on scaled FOWT models due to conflicting similitude laws between the wind (commonly Reynolds) and the waves (commonly Froude). To aid in accelerating the development of the U.S. floating offshore industry, there is a significant need to develop a flexible, modular framework that can expand the capacities of existing wave-only laboratories. The project goal is to demonstrate a hydrodynamic real-time hybrid simulation (hydro-RTHS) framework that couples numerical wind and physical waves acting on a FOWT, thus representing simultaneous aero/hydro-dynamic loading. The FOWT is partitioned into a full-scale numerical sub-assembly associated with the aerodynamics and a model-scale physical sub-assembly associated with the hydrodynamics. The numerical-physical partition associated with hydro-RTHS mitigates scaling constraints by supplying different scaling laws to the physical and numerical sub-assemblies. Herein, length, force, and time are scaled and exchanged between the sub-assemblies using Froude scaling to represent the open-channel flow in the physical sub-assembly. Other similitude laws could also be utilized depending on the problem definition. It is envisioned that the ability to model FOWTs under waves and wind, with mitigation of similitude distortions, would result in reduced development costs (currently, FOWT concept development is performed with full-size pro- totypes at enormous expense and risk) and increase the reliability of the FOWT industry (since extreme wave and wind conditions and contingency events can be tested safely in a controlled environment).

16 TIDAL AND WAVE POWER↗

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2023

As the United States' largest source of renewable energy, wind is already playing a vital role in the nation's shift to electricity generated from sustainable resources. Scientists, engineers, and analysts at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) are propelling our country closer to this clean energy future with new efficient, reliable, and cost-competitive wind technologies and deployment strategies. In Fiscal Year 2023 (FY 2023), NREL received national acclaim for recent wind technology breakthroughs, applauded sustained decades of progress in wind energy research, and heralded new initiatives to support the next generation of wind development. This report and NREL's achievements of the last year set the stage for innovative research to come. Two NREL software innovations used to assess wind technology options were recognized with R&D 100 Awards. The Renewable Energy Potential (reV) model helps planners, developers, and researchers calculate wind energy capacity, generation, and cost for specific facilities, fleets and scenarios. NREL's Simulation and Emulation for Advanced Systems validates transmission and distribution solutions to minimize implementation risks. Both of these tools can be applied to a range of renewable solutions to cut costs and improve the reliability of systems in even the most remote locations. The laboratory commemorated landmark anniversaries for wind energy research facilities and programs managed by NREL. The National Wind Technology Center (NWTC) celebrated 30 years as a world-class hub for renewable energy research. An important component of the NWTC on NREL's Flatirons Campus, the one-of-a-kind 7-megawatt (MW) Controllable Grid Interface wind energy research apparatus, turned 10 this year. The Collegiate Wind Competition completed its tenth annual competition in May 2023. Six years after the grand challenges in wind energy science were first compiled, NREL brought together more than 100 wind energy experts from 15 countries to revisit the field's most pressing research needs. Other key new research initiatives examined ways to simultaneously address the needs for skilled wind energy workers and equitable job opportunities. NREL released the first national-level report evaluating offshore wind industry workforce gaps and ways to build a large, diverse pool of job candidates. The lab also co-hosted the International Partnering Forum's Offshore Wind Workforce Summit as part of its ongoing efforts to address the special challenges of that industry sector. NREL research and development (R&D) for the DOE Wind Energy Technologies Office (WETO) continues to build momentum in its efforts to combat climate change, create clean energy jobs, and promote energy justice. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2023 (between Oct. 1, 2022, and Sept. 30, 2023).

accomplishments↗

Field Validation of Wake Steering Control with Wind Direction Variability

Wake steering is a wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes the increase in power production from wake-steering to be less than predicted by steady-state models, but more robust wake-steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two different 2-turbine wake-steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted, assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake-steering controllers that are more robust to variable wind conditions present in the field.

17 WIND ENERGY↗

Outlooks for Wind Power in the United States: Drivers and Trends under a 2016 Policy Environment

Over the past decade, wind power has become one of the fastest growing electricity generation sources in the United States. Despite this growth, the U.S. wind industry continues to experience year-to-year fluctuations across the manufacturing and supply chain as a result of dynamic market conditions and changing policy landscapes. Moreover, with advancing wind technologies, ever-changing fossil fuel prices, and evolving energy policies, the long-term future for wind power is highly uncertain. In this report, we present multiple outlooks for wind power in the United States, to explore the possibilities of future wind deployment. The future wind power outlooks presented rely on high-resolution wind resource data and advanced electric sector modeling capabilities to evaluate an array of potential scenarios of the U.S. electricity system. Scenario analysis is used to explore drivers, trends, and implications for wind power deployment over multiple periods through 2050. Specifically, we model 16 scenarios of wind deployment in the contiguous United States. These scenarios span a wide range of wind technology costs, natural gas prices, and future transmission expansion. We identify conditions with more consistent wind deployment after the production tax credit expires as well as drivers for more robust wind growth in the long run. Conversely, we highlight challenges to future wind deployment. We find that the degree to which wind technology costs decline can play an important role in future wind deployment, electric sector CO 2 emissions, and lowering allowance prices for the Clean Power Plan.

17 WIND ENERGY↗

Oregon Offshore Wind Site Feasibility and Cost Study

The work performed in this Oregon study assessed the present and future costs of floating offshore wind technology deployment in the state of Oregon at commercial scale. It is widely recognized that floating offshore wind energy technology will be needed if significant offshore wind deployment in the Pacific region is to occur. The study was performed by the National Renewable Energy Laboratory (NREL) and its subcontractors and funded by the Bureau of Ocean Energy Management (BOEM). It was based, in part, on assumptions and analysis from an NREL report titled 'A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030' (Beiter et al. 2016), which supported the 'National Offshore Wind Strategy' (Gilman et al. 2016). The strategy builds on the previous DOE Wind Vision Study Scenario of 86 gigawatts (GW) of offshore wind deployed by 2050 in the United States. Under the Wind Vision scenario, 20% (17.2 GW installed capacity) of the nation's total offshore wind in 2050 comes from the Pacific coastal states (DOE 2015), which will require the development of floating wind technologies. Although most offshore development to date has been in depths of 50 meters (m) or less, 97% of Oregon's offshore wind resource is in water depths greater than 60 m, where floating wind is assumed to be needed. Floating offshore wind technology is still in its nascent stage of development but is advancing toward commercialization in both Europe and Asia. In this report, we use available floating prototype costs, pilot-scale costs, and commercial-scale costs from fixed-bottom offshore wind projects to model and analyze the cost of floating wind for five hypothetical wind sites in Oregon using specific geographical and utility grid information.

17 WIND ENERGY↗

Benefits of Two Turbine Rotor Diameters and Hub Heights in the Same Wind Farm

Significant turbine-wake interactions greatly reduce power output in a wind farm. If different turbine hub heights and rotor diameters are included in the same wind farm, the wake interference in the farm will be reduced, resulting in a lower cost of energy (COE) than a farm with identical turbines. In this paper, we present a method to model wind farm COE in farms with hub heights and rotor diameters that vary across the wind farm. We also demonstrate how to optimize these wind farms to minimize COE. The results show that COE can be greatly reduced in wind farms with non-homogeneous turbines, especially when the turbines are spaced close together. For a unidirectional wind rose, including different turbine design in the wind farm has a similar decrease in COE to spreading the wind turbines farther apart. When the rotor diameter and hub height of the wind turbines in a farm are optimized uniformly, a COE decrease of 4% to 13% (depending on the grid spacing and wind shear exponent) is achieved compared to the baseline. When the rotor diameter and turbine heights are optimized non-uniformly, with two different diameters and heights throughout the farm, there is a COE decrease of 22% to 41% compared to the baseline. For a more spread wind rose with a dominant probability from the west, there is a COE decrease between 3% and 10% for uniformly optimized rotor diameter and height compared to the baseline. With two optimized rotor diameters and heights through the farm, a COE decrease of 3% to 19% is achieved. For a similar wind rose shifted such that the dominant wind direction is from the northwest, a COE decrease between 3% and 10% results from uniformly optimized wind turbines compared to the baseline. A COE decrease of 3% to 17% compared to the baseline occurs with two different turbines are optimized throughout the wind farm.

17 WIND ENERGY↗

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.

17 WIND ENERGY↗

Field Validation of Wake Steering Control with Wind Direction Variability: Preprint

Wake steering is a type of wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes wake steering controllers to increase power production less than predicted by steady state models, but more robust wake steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two 2-turbine wake steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake steering controllers that are more robust to variable wind conditions present in the field.

17 WIND ENERGY↗

Incorporating Wind Turbine Choice in High-Resolution Geospatial Supply Curve and Capacity Expansion Models

To achieve national decarbonization goals, U.S. annual deployment of wind energy will need to increase by at least fivefold compared to the recent past. Modeling and analysis frameworks can help inform where and how wind energy deployment might occur and thereby help enable the achievement of decarbonization goals. However, most prior wind energy modeling and analysis studies rely on generalized representations of wind energy technologies. Since wind energy technology advancements are expected to increase the competitiveness of wind energy, it is important to incorporate more detailed representations of turbine technology into wind energy modeling. Here we present a new method that incorporates wind turbine choice into the technology representation of land-based wind energy in long-term planning models. Our method integrates three previously published modeling and analysis capabilities: 1) bottom-up cost modeling to estimate future technology costs, 2) geospatial modeling to represent siting decisions, and 3) power sector modeling to evaluate potential deployment. We refer to this approach as a "customized turbine choice" methodology because it creates a composite turbine scenario by choosing from multiple wind turbine technologies—using site-specific optimized turbine layout and selecting the least-cost technology at each location. We demonstrate the capabilities of this new modeling pipeline by examining how the selection of four different wind turbine configurations might evolve from 2021 through 2040. Our results show that using our customized turbine choice methodology could lead to higher estimates for wind future deployment, which indicates that more simplified modeling might underestimate the role that wind energy could play in meeting decarbonization goals. Future research is needed to further explore the implications of turbine choice and to better inform technology researchers, original equipment manufacturers, and other wind industry stakeholders about the market potential of different wind turbine technologies.

17 WIND ENERGY↗

Analyzing the Effects of Atmospheric Turbulent Fluctuations on the Wake Structure of Wind Turbines and Their Blade Vibrational Dynamics

In recent trends, a rising demand for renewable energy has driven wind turbines to larger proportions, where lighter blade designs are often adopted to reduce the costs associated with logistics and production. This causes modern utility-scale wind turbine blades to be inherently more flexible, and their amplified aeroelastic sensitivity results in complex multi-physics reactions to variant atmospheric conditions, including dynamic patterns of aerodynamic loading at the rotor and vortex structure evolutions within the wake. In this paper, we analyze the influence of inflow variance for wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex wake interactions for a range of flexible NRT blade variations, operating in differing conditions of variant atmospheric flow. CODEF solutions of turbine operation in Steady-In-The-Average (SITA) wind conditions are compared to SITA wind conditions featuring a controlled gust-like pulse overimposed, to isolate the effects of typical wind fluctuations. Finally, simulations of realistic time-varying wind conditions from SWiFT meteorological tower measurements are compared to the solutions of SITA wind conditions. These increasingly complex atmospheric inflow variations are tested to show the differing effects evoked by various patterns of spatiotemporal atmospheric flow fluctuations. An analysis is presented for solutions of wind turbine aeroelastic response and vortex wake evolution, to elucidate the consequences of variant inflow, which pertain to wind turbine dynamics at an individual and farm-collective scale. The comparisons of simulated farm flow for SITA and measured fluctuating wind conditions show that certain regions of the wake contain up to a 12% difference in normalized axial velocity, due to the introduction of wind fluctuations. The findings of this study prove valuable for practical applications in wind farm control and optimization strategies, with particular significance for modern utility-scale wind power plants operating in variant atmospheric conditions.

Farrell, Alayna (ORCID:000000023555720X)↗

Observation-based estimates of land availability for wind power: a case study for Czechia

Background: The availability of land for the installation of wind power turbines is restricted by numerous factors. Besides climatic conditions, the deployment of wind energy is limited by technical, social, economic, and environmental factors. Typically, assessments of land availability for wind power use legal and technical criteria to estimate the potential for wind power expansion. In contrast, we use observed characteristics of wind power generation sites existing in Austria and Denmark to estimate its potential expansion in Czechia. We combined data on wind turbine locations with data on land use, wind speeds, human impact on land, and nature conservation areas. Results: Our analysis shows that the density of wind power in Austria is variable, but higher on average (4.79 MW km-2 ) than in Denmark (1.76 MW km-2 ). Austrian wind turbines have been installed in areas where the human impact on land is mostly higher than the Austrian average, while in Denmark, no difference is observed. Regarding the land use composite, the share of agricultural land on sites with wind turbines is on average much higher (86%), while the share of forest is much lower (7%) in both countries. We identified a maximum potential area in Czechia of 543 km2 with Austrian and 421 km2 with Danish characteristics. When conservatively assuming observed historical power densities, this area translates to 2295 MW and 741 MW of installed wind power capacity, respectively. These results are a magnitude of order lower than the potentials found in existing studies. In a sensitivity analysis, we have examined that the availability of potential sites depends mainly on the population density, the human impact on land, prevailing wind speeds, and the height above sea level. Conclusions: We estimated available land area for potential wind turbine installations in Czechia using our newly developed methodology based on observed site characteristics of today’s wind power infrastructure in Austria and Denmark. Available land area indicated possible overestimation of wind power capacities proposed in the recent studies on the renewable energy transition. Hence, more rigorous consideration of land availability is required for assessments of potential wind power expansion.

17 WIND ENERGY↗

Evaluating the Grid Impact of Oregon Offshore Wind [Slides]

This analysis used high-resolution offshore wind data and a detailed production cost model (PCM) of the Western Interconnection to explore the value and operational impact of integrating offshore wind along Oregon's coastline. Leveraging local technical stakeholder expertise and input, we determined a set of scenarios to explore. These scenarios vary both offshore wind capacities and the Western Interconnection generation and transmission infrastructure. From the scenario modeling and analysis, we identified the following key findings. In addition, we simulated a subset of the scenarios for a range of historical weather years (2007-2013), to understand the robustness of our findings to different weather conditions. Trans-coastal transmission constraints and congestion are the key drivers to the curtailment of Oregon offshore wind. Once power can be delivered into the Willamette Valley, there are few system constraints that lead to a significant curtailment of offshore wind off the coast of Oregon. Approximately 2.6 GW of installed offshore wind capacity can be integrated into Oregon's power system without major upgrades to trans-coastal transmission while avoiding significant curtailment. The system value provided by offshore wind ranges between $\$65$ /MWh and $\$85$ /MWh across the various scenarios considered. Offshore wind heavily influences the flow of the cross Cascade transmission. Across all scenarios, we found a robust relationship of approximately 500-550 MW decrease in the hourly flow of the cross Cascade transmission for every 1,000 MW of hourly offshore wind generation. However, we also found there was not a strong relationship between the highest cross-Cascade transmission flow hours and high offshore wind generation, limiting the extent to which offshore wind can be considered a non-wires alternative to cross cascade transmission. Depending on the meteorological year, 880-1,580 MW and 1,650-3,100 MW can be counted on to serve coastal loads with 2.6 GW and 5 GW of offshore wind capacity, respectively. Offshore wind allows for more optimal daily and hourly scheduling of hydropower, while still complying with various technical and regulatory constraints on the water resource. Oregon offshore wind has the potential to contribute to the evening net load peak in California (i.e., mitigate duck curve challenges), however transmission congestion between California and Oregon limits this contribution. Co-located storage at the point of interconnection for offshore wind reduces curtailment when trans-coastal transmission is not upgraded, providing a non-wires alternative to increase offshore wind capacity beyond 2.6 GW.

17 WIND ENERGY↗

Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics

Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors. To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s -1 at the turbine hub-height of 80 m. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30% of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: cross-stream component errors are on the order of 15% of the hub-height inflow wind speed (1.0 m s &minus;1 ) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 s -1 (or 15% of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8% while vertical velocity estimates are compromised. Furthermore, measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.

17 WIND ENERGY↗