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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↗
DOE OSTI2014

Lake Michigan Offshore Wind Feasibility Assessment

The purpose of this project was to conduct the first comprehensive offshore wind assessment over Lake Michigan and to advance the body of knowledge needed to support future commercial wind energy development on the Great Lakes. The project involved evaluation and selection of emerging wind measurement technology and the permitting, installation and operation of the first mid-lake wind assessment meteorological (MET) facilities in Michigan’s Great Lakes. In addition, the project provided the first opportunity to deploy and field test floating LIDAR and Laser Wind Sensor (LWS) technology, and important research related equipment key to the sitting and permitting of future offshore wind energy development in accordance with public participation guidelines established by the Michigan Great Lakes Wind Council (GLOW). The project created opportunities for public dialogue and community education about offshore wind resource management and continued the dialogue to foster Great Lake wind resource utilization consistent with the focus of the GLOW Council. The technology proved to be effective, affordable, mobile, and the methods of data measurement accurate. The public benefited from a substantial increase in knowledge of the wind resources over Lake Michigan and gained insights about the potential environmental impacts of offshore wind turbine placements in the future. The unique first ever hub height wind resource assessment using LWS technology over water and development of related research data along with the permitting, sitting, and deployment of the WindSentinel MET buoy has captured public attention and has helped to increase awareness of the potential of future offshore wind energy development on the Great Lakes. Specifically, this project supported the acquisition and operation of a WindSentinel (WS) MET wind assessment buoy, and associated research for 549 days over multiple years at three locations on Lake Michigan. Four research objectives were defined for the project including to: 1) test and validate floating LIDAR technology; 2) collect and access offshore wind data; 3) detect and measure bird and bat activity over Lake Michigan; 4) conduct an over water sound propagation study; 5) prepare and offer a college course on offshore energy, and; 6) collect other environmental, bathometric, and atmospheric data. Desk-top research was performed to select anchorage sites and to secure permits to deploy the buoy. The project also collected and analyzed data essential to wind industry investment decision-making including: deploying highly mobile floating equipment to gather offshore wind data; correlating offshore wind data with conventional on-shore MET tower data; and performing studies that can contribute to the advancement and deployment of offshore wind technologies. Related activities included: • Siting, permitting, and deploying an offshore floating MET facility; • Validating the accuracy of floating LWS using near shoreline cup anemometer MET instruments; • Assessment of laser pulse technology (LIDAR) capability to establish hub height measurement of wind conditions at multiple locations on Lake Michigan; • Utilizing an extended-season (9-10 month) strategy to collect hub height wind data and weather conditions on Lake Michigan; • Investigation of technology best suited for wireless data transmission from distant offshore structures; • Conducting field-validated sound propagation study for a hypothetical offshore wind farm from shoreline locations; • Identifying the presence or absence of bird and bat species near wind assessment facilities; • Identifying the presence or absence of benthic and pelagic species near wind assessment facilities; All proposed project activities were completed with the following major findings: • Floating Laser Wind Sensors are capable of high quality measurement and recordings of wind resources. The WindSentinel presented no significant operational or statistical limitations in recording wind data technology at a at a high confidence level as compared to traditional anemometer cup technology. • During storms, mean Turbulent Kinetic Energy (TKE) increases with height above water; • Sufficient wind resources exist over Lake Michigan to generate 7,684 kWh of power using a 850 kW rated turbine at elevations between 90 - 125 meters, a height lower than originally anticipated for optimum power generation; • Based on initial assessments, wind characteristics are not significantly different at distant (thirty-two mile) offshore locations as compared to near-shore (six mile) locations; • Significant cost savings can be achieved in generation wind energy at lower turbine heights and locating closer to shore. • Siting must be sufficiently distant from shore to minimize visual impact and to address public sentiment about offshore wind development; • Project results show that birds and bats do frequent the middle of Lake Michigan, bats more so than birds; • Based on the wind resource assessment and depths of Lake Michigan encountered during the project, future turbine placement will most likely need to incorporate floating or anchored technology; • The most appropriate siting of offshore wind energy locations will enable direct routing of transmission cables to existing generating and transmission facilities located along the Michigan shoreline; • Wind turbine noise propagation from a wind energy generating facility at a five mile offshore location will not be audible at the shoreline over normal background sound levels.

17 WIND ENERGY↗
DOE OSTI2014

Coastal Ohio Wind Project

The Coastal Ohio Wind Project intends to address problems that impede deployment of wind turbines in the coastal and offshore regions of Northern Ohio. The project evaluates different wind turbine designs and the potential impact of offshore turbines on migratory and resident birds by developing multidisciplinary research, which involves wildlife biology, electrical and mechanical engineering, and geospatial science. Firstly, the project conducts cost and performance studies of two- and three-blade wind turbines using a turbine design suited for the Great Lakes. The numerical studies comprised an analysis and evaluation of the annual energy production of two- and three-blade wind turbines to determine the levelized cost of energy. This task also involved wind tunnel studies of model wind turbines to quantify the wake flow field of upwind and downwind wind turbine-tower arrangements. The experimental work included a study of a scaled model of an offshore wind turbine platform in a water tunnel. The levelized cost of energy work consisted of the development and application of a cost model to predict the cost of energy produced by a wind turbine system placed offshore. The analysis found that a floating two-blade wind turbine presents the most cost effective alternative for the Great Lakes. The load effects studies showed that the two-blade wind turbine model experiences less torque under all IEC Standard design load cases considered. Other load effects did not show this trend and depending on the design load cases, the two-bladed wind turbine showed higher or lower load effects. The experimental studies of the wake were conducted using smoke flow visualization and hot wire anemometry. Flow visualization studies showed that in the downwind turbine configuration the wake flow was insensitive to the presence of the blade and was very similar to that of the tower alone. On the other hand, in the upwind turbine configuration, increasing the rotor blade angle of attack reduced the wake size and enhanced the vortices in the flow downstream of the turbine-tower compared with the tower alone case. Mean and rms velocity distributions from hot wire anemometer data confirmed that in a downwind configuration, the wake of the tower dominates the flow, thus the flow fields of a tower alone and tower-turbine combinations are nearly the same. For the upwind configuration, the mean velocity shows a narrowing of the wake compared with the tower alone case. The downwind configuration wake persisted longer than that of an upwind configuration; however, it was not possible to quantify this difference because of the size limitation of the wind tunnel downstream of the test section. The water tunnel studies demonstrated that the scale model studies could be used to adequately produce accurate motions to model the motions of a wind turbine platform subject to large waves. It was found that the important factors that affect the platform is whether the platform is submerged or surface piercing. In the former, the loads on the platform will be relatively reduced whereas in the latter case, the structure pierces the wave free surface and gains stiffness and stability. The other important element that affects the movement of the platform is depth of the sea in which the wind turbine will be installed. Furthermore, the wildlife biology component evaluated migratory patterns by different monitoring systems consisting of marine radar, thermal IR camera and acoustic recorders. The types of radar used in the project are weather surveillance radar and marine radar. The weather surveillance radar (1988 Doppler), also known as Next Generation Radar (NEXRAD), provides a network of weather stations in the US. Data generated from this network were used to understand general migratory patterns, migratory stopover habitats, and other patterns caused by the effects of weather conditions. At a local scale our marine radar was used to complement the datasets from NEXRAD and to collect additional monitoring parameters such as passage rates, flight paths, flight directions, and flight altitudes of nocturnal migrating species. Our work focused on the design and development of custom built marine radar that used t-bar and parabolic dish antennas. The marine radar used in the project was Furuno (XANK250) which was coupled with a XIR3000B digitizing card from Russell Technologies for collection of the radar data. The radar data was processed by open source radR processing software using different computational techniques and methods. Additional data from thermal IR imaging cameras were collected to detect heat emitted from objects and provide information on movements of birds and bats, data which we used for different animal flight behavior analysis. Lastly, the data from the acoustic recorders were used to provide the number of bird calls for assessing patterns and peak passage rates during migration. The development of the geospatial database included collection of different data sources that are used to support offshore wind turbine development. Many different data sets were collected and organized using initial version of web-based repository software tools that can accommodate distribution of rectified pertinent data sets such as the lake depth, lake bottom engineering parameters, extent of ice, navigation pathways, wind speed, important bird habitats, fish efforts and other layers that are relevant for supporting robust offshore wind turbine developments. Additional geospatial products developed during the project included few different prototypes for offshore wind farm suitability which can involve different stakeholders and participants for solving complex planning problems and building consensus. Some of the prototypes include spatial decision support system (SDSS) for collaborative decision making, a web-based Participatory Geographic Information System (PGIS) framework for evaluating importance of different decision alternatives using different evaluation criteria, and an Android application for collection of field data using mobile and tablet devices . In summary, the simulations of two- and three-blade wind turbines suggested that two-bladed machines could produce comparable annual energy as the three-blade wind turbines but have a lighter tower top weight, which leads to lower cost of energy. In addition, the two-blade rotor configuration potentially costs 20% less than a three blade configuration that produces the same power at the same site. The cost model analysis predicted a potential cost savings of approximately 15% for offshore two-blade wind turbines. The foundation design for a wind turbine in Lake Erie is likely to be driven by ice loads based on the currently available ice data and ice mechanics models. Hence, for Lake Eire, the cost savings will be somewhat smaller than the other lakes in the Great Lakes. Considering the size of cranes and vessels currently available in the Great Lakes, the cost optimal wind turbine size should be 3 MW, not larger. The surveillance data from different monitoring systems suggested that bird and bat passage rates per hour were comparable during heavy migrations in both spring and fall seasons while passage rates were significantly correlated to wind directions and wind speeds. The altitude of migration was higher during heavy migrations and higher over water relative to over land. Notable portions of migration on some spring nights occurred parallel the shoreline, often moving perpendicular to southern winds. The birds approaching the Western basin have a higher propensity to cross than birds approaching the Central basin of Lake Erie and as such offshore turbine development might be a better option further east towards Cleveland than in the Western basin. The high stopover density was more strongly associated with migration volume the following night rather than the preceding night. The processed mean scalar wind speeds with temporal resolutions as fine as 10-minute intervals near turbine height showed that August is the month with the weakest winds while December is the month, which typically has the strongest winds. The ice data suggests that shallow western basin of Lake Erie has higher ice cover duration many times exceeding 90 days during some winters.

17 WIND ENERGY↗
DOE OSTI2021

Land-Based Wind Market Report: 2021 Edition

The U.S. Department of Energy's 2021 edition of its land-based wind market report provides an overview of key trends in the U.S. wind power market, with a focus on 2020. You can find a report, data file and presentation on the Files tab, below. Additionally, several data visualizations are available on the Visualizations tab. Highlights of this year’s update include: -Wind comprises a growing share of electricity supply: U.S. wind power capacity grew at a record pace in 2020, with 25 billion dollars invested in 16.8 GW of capacity. Wind energy output rose to account for more than 8% of the entire nation’s electricity supply, and is more than 20% in 10 states. At least 209 GW of wind are seeking transmission interconnection; 61 GW of this capacity are offshore wind and 13 GW are hybrid plants that pair wind with storage or PV. -Wind project performance has increased over time: The average capacity factor among recently built projects was over 40%, considerably higher than projects built earlier. The highest capacity factors are seen in the interior ‘wind belt’ of the country. -Turbines continue to get larger: Improved plant performance has been driven by larger turbines mounted on taller towers and featuring longer blades. In 2010, no turbines employed blades that were 115 meters in diameter or larger, but in 2020, 91% of newly installed turbines featured such rotors. Proposed projects indicate that total turbine height will continue to rise. -Low wind turbine pricing has pushed down installed project costs over the last decade: Wind turbine prices are averaging 775–850 dollars/kW. The average installed cost of wind projects in 2020 was 1,460 dollars/kW, down more than 40% since the peak in 2010, though stable for the last three years. The lowest costs were found in Texas and the (non-ISO) West. -Wind energy prices remain low, around 20 dollars/MWh in the interior of the country: After topping out at 70 dollars/MWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped. In the interior ‘wind belt’ of the country, recent pricing is around 20 dollars/MWh. In the West and East, prices tend to average 30 dollars/MWh or more. These prices, which are possible in part due to federal tax support, fall below the projected future fuel costs of gas-fired generation. -Wind PPA prices are often attractive compared to wind’s grid-system market value: The value of wind in wholesale power markets is affected by the location of wind plants, their hourly output profiles, and how those characteristics correlate with real-time electricity prices and capacity markets. The market value of wind declined in 2020, following natural gas prices lower and averaging under 15 dolalrs/MWh in ERCOT, MISO, NYISO and SPP; higher values were seen in CAISO, ISO-NE and PJM. -The average levelized cost of wind energy is down to 33 dollars/MWh: Levelized costs, which exclude the impacts of federal tax incentives, vary across time and geography, but the national average stood at 33 dollars/MWh in 2020—down substantially historically, though consistent with the previous two years. Levelized costs were lowest in ERCOT, SPP, and the (non-ISO) West. -The health and climate benefits of wind in 2020 were larger than its grid-system value, and the combination of all three far exceeds the current levelized cost of wind: Wind generation reduces power-sector emissions of carbon dioxide, nitrogen oxides, and sulfur dioxide. These reductions, in turn, provide public health and climate benefits that vary regionally, but together are economically valued at an average of 76 dollars/MWh-wind nationwide in 2020. -The domestic supply chain for wind equipment is diverse: For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>85%), towers (60-75%), and blades and hubs (30-50%), but is much lower for most components internal to the nacelle.

17 WIND ENERGY↗
DOE OSTI2022

Land-Based Wind Market Report: 2022 Edition

The U.S. Department of Energy's 2022 edition of its Land-Based Wind Market Report provides an overview of key trends in the U.S. wind power market, with a focus on 2021. You can find a report, data file and presentation on the Files tab, below. Additionally, several data visualizations are available on the Visualizations tab. Despite ongoing supply chain challenges, wind energy in 2021 continued to see strong growth, technology improvements, and low prices in the U.S. Key highlights include: Wind comprises a growing share of electricity supply: U.S. wind power capacity grew at a strong pace in 2021, with the 13.4 GW of new additions representing a $\$20$ billion investment and 32% of all newly added U.S. generation capacity. Wind energy output rose to account for more than 9% of the entire nation’s electricity supply. At least 247 GW of wind are seeking transmission interconnection; 77 GW of this capacity are offshore wind and 19 GW are hybrid plants that pair wind with storage or solar PV. Wind project performance has increased over the decades: The average capacity factor among recently built projects was nearly 40%, considerably higher than projects built earlier. The highest capacity factors are seen in the interior ‘wind belt’ of the country. Turbines continue to get larger: Improved plant performance has been driven by larger turbines mounted on taller towers and featuring longer blades. In 2011, no turbines employed blades that were 115 meters in diameter or larger, but in 2021, 89% of newly installed turbines featured such rotors. Proposed projects indicate that total turbine height will continue to rise. Low wind turbine pricing has pushed down installed project costs over the last decade: Wind turbine prices averaged $\$800$–$\$950$/kW in 2021, a 5% to 10% increase from the year prior but substantially lower than in 2010. The average installed cost of wind projects in 2021 was $\$1,500$/kW, down more than 40% since the peak in 2010, though relatively stable in recent years. The lowest costs were found in Texas and the (non-ISO) West. Wind energy prices are on the rise, but generally remain low, around $\$20$/MWh in the interior of the country with higher prices in the West and East. After topping out above $\$75$/MWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped—though supply-chain pressures have resulted in increased prices in recent years. In the interior ‘wind belt’ of the country, recent pricing is around $\$20$/MWh. In the West and East, prices tend to average above $\$30$/MWh. These prices, which are possible in part due to federal tax support, fall below the projected future fuel costs of gas-fired generation. Wind PPA prices are often attractive compared to wind’s grid-system market value: The value of wind in wholesale power markets is affected by the location of wind plants, their hourly output profiles, and how those characteristics correlate with real-time electricity prices and capacity markets. The market value of wind increased in 2021, averaging $\$16$/MWh in MISO, $\$19$/MWh in SPP, $\$23$/MWh in NYISO, $\$31$/MWh in ERCOT, $\$33$/MWh in PJM, $\$44$/MWh in ISO-NE, and $\$48$/MWh in CAISO. The average levelized cost of wind energy was $\$32$/MWh for plants built in 2021: Levelized costs, which exclude the impacts of federal tax incentives, vary across time and geography. The national average stood at $\$32$/MWh in 2021—down substantially historically, though relatively stable in recent years. Levelized costs were lowest in ERCOT, SPP, and the (non-ISO) West. The health and climate benefits of wind in 2021 were larger than its grid-system value, and the combination of all three far exceeds the current levelized cost of wind: Wind generation reduces power-sector emissions of carbon dioxide, nitrogen oxides, and sulfur dioxide. These reductions, in turn, provide public health and climate benefits that vary regionally, but together are economically valued at an average of over $\$90$/MWh-wind for plants built in 2021.

17 WIND ENERGY↗
DOE OSTI2019

The Wind Energy Workforce in the United States: Training, Hiring, and Future Needs

Researchers received 247 completed interviews from wind industry employers and 50 completed interviews from representatives from educational institutions with wind energy courses, to better understand the wind industry workforce, hiring needs, and educational pathways . The interviews revealed that 68% of wind industry firms interviewed had difficulty hiring across most occupations. Twenty-five percent of firms searched outside of the United States to find candidates with appropriate skills and experience, which some employers consider lacking, among domestic candidates. At the same time, interview results showed that people who graduate from wind energy educational programs often do not enter the wind industry upon graduation - the majority find employment outside of the wind industry. In addition, some educational and training institutions with wind or renewable energy-specific programs reported difficulty filling courses. This difficulty of employers finding well qualified workers while wind and renewable energy graduates report difficulty finding jobs in the wind industry is the wind energy workforce gap. Narrowing this workforce gap - decreasing hiring difficulty while increasing graduates' ability to find jobs in the wind industry - could reduce recruiting costs, better satisfy employer needs, and grow the domestic wind workforce. Narrowing the gap would simultaneously help educational institutions more efficiently fill classrooms and communicate wind-industry career options to potential students. Twenty-five percent of firms searched outside of the United States to find candidates with appropriate skills and experience, which some employers consider lacking, among domestic candidates. At the same time, interview results showed that people who graduate from wind energy educational programs often do not enter the wind industry upon graduation - the majority find employment outside of the wind industry. In addition, some educational and training institutions with wind or renewable energy-specific programs reported difficulty filling courses. This difficulty of employers finding well qualified workers while wind and renewable energy graduates report difficulty finding jobs in the wind industry is the wind energy workforce gap. Narrowing this workforce gap - decreasing hiring difficulty while increasing graduates' ability to find jobs in the wind industry - could reduce recruiting costs, better satisfy employer needs, and grow the domestic wind workforce. Narrowing the gap would simultaneously help educational institutions more efficiently fill classrooms and communicate wind-industry career options to potential students.

17 WIND ENERGY↗
NASA NTRS2010

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

Barrett, Joe H., III↗
DOE OSTI2019

2018 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 robust pace in 2018: $11 billion was invested in new wind power plants in 2018. In 2018, wind energy contributed 6.5% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and more than 30% in three of those states—Kansas, Iowa, and Oklahoma. 232 gigawatts of wind are seeking transmission interconnection, 2% of which represents proposed wind plants paired with storage. ~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 are on the horizon. The average 2018 capacity factor among projects built from 2014 through 2017 was 42%, compared to an average of 31% among projects built from 2004 to 2011 and 24% among projects built from 1998 to 2001. Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen to $700–$900/kW. The average installed cost of wind projects in 2018 was $1,470/kW, down 40 percent since the peak in 2009 and 2010. ~Wind energy prices are at historical lows: After topping out at 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to below 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country. These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. Solar PPA prices have declined precipitously, pressuring wind’s competitive position. ~The grid-system value of wind has declined over the last decade, but rebounded over the last two years. The value of wind in wholesale power markets is impacted by the location of wind plants, their hourly output profiles, and how those characteristics correlate with real-time electricity prices and capacity markets. The market value of wind in 2018 was the lowest in the Southwest Power Pool and Texas (average of $17/MWh and $18/MWh, respectively) whereas the highest-value market was New England (at $41/MWh). Wind energy prices are generally competitive with these value estimates. ~The domestic supply chain for wind equipment is diverse: For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (75-90%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the nacelle. Wind sector employment reached a new high of more than 114,000 full-time workers at the end of 2018. ~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 9,000-12,000 MW in 2019 and 11,000-15,000 in 2020, with market contraction anticipated beginning in 2021 as those tax incentives are phased out.

17 WIND ENERGY↗
DOE OSTI2024

The value of wake steering wind farm flow control in US energy markets

Abstract. Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.

17 WIND ENERGY↗
DOE OSTI2016

Establishment of a National Wind Energy Center at University of Houston

The DOE-supported project objectives are to: establish a national wind energy center (NWEC) at University of Houston and conduct research to address critical science and engineering issues for the development of future large MW-scale wind energy production systems, especially offshore wind turbines. The goals of the project are to: (1) establish a sound scientific/technical knowledge base of solutions to critical science and engineering issues for developing future MW-scale large wind energy production systems, (2) develop a state-of-the-art wind rotor blade research facility at the University of Houston, and (3) through multi-disciplinary research, introducing technology innovations on advanced wind-turbine materials, processing/manufacturing technology, design and simulation, testing and reliability assessment methods related to future wind turbine systems for cost-effective production of offshore wind energy. To achieve the goals of the project, the following technical tasks were planned and executed during the period from April 15, 2010 to October 31, 2014 at the University of Houston: (1) Basic research on large offshore wind turbine systems (2) Applied research on innovative wind turbine rotors for large offshore wind energy systems (3) Integration of offshore wind-turbine design, advanced materials and manufacturing technologies (4) Integrity and reliability of large offshore wind turbine blades and scaled model testing (5) Education and training of graduate and undergraduate students and post- doctoral researchers (6) Development of a national offshore wind turbine blade research facility The research program addresses both basic science and engineering of current and future large wind turbine systems, especially offshore wind turbines, for MW-scale power generation. The results of the research advance current understanding of many important scientific issues and provide technical information for solving future large wind turbines with advanced design, composite materials, integrated manufacturing, and structural reliability and integrity. The educational program have trained many graduate and undergraduate students and post-doctoral level researchers to learn critical science and engineering of wind energy production systems through graduate-level courses and research, and participating in various projects in center’s large multi-disciplinary research. These students and researchers are now employed by the wind industry, national labs and universities to support the US and international wind energy industry. The national offshore wind turbine blade research facility developed in the project has been used to support the technical and training tasks planned in the program to accomplish their goals, and it is a national asset which is available for used by domestic and international researchers in the wind energy arena.

17 WIND ENERGY↗
DOE OSTI2025

Performance of wind assessment datasets in United States coastal areas

The atmospheric dynamics that occur near the intersection of land and water offer exciting and challenging opportunities for wind energy deployment in coastal locations. New models and tools are continually being developed in support of wind resource assessment, and three recent products are explored in this work for their performance in representing characteristics of the wind resource at coastal locations: the Global Wind Atlas 3 (GWA3), the 2023 National Offshore Wind dataset (NOW-23), and the wind climate simulations that are a component of the Wind Integration National Dataset (WIND) Toolkit Long-Term Ensemble Dataset (WTK-LED Climate). These relatively new products are freely available and user-friendly so that anyone – from a utility-scale developer to a resident or business owner – can evaluate the potential for wind energy generation at their location of interest. The validations in this work provide guidance on the accuracy of wind resource assessments for coastal customers interested in installing small or midsize wind turbines (≤ 1 MW in capacity) to support energy needs at the residential, business, or community scale, such as the island and remotely located participants of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. At 23 coastal locations across the United States, dataset performance varies according to different evaluation metrics. All three recent datasets tend to overestimate the observed coastal wind resource. GWA3 produces the smallest annual average wind speed relative errors, whereas WTK-LED Climate is in best agreement in terms of representing diurnal wind speed cycles. NOW-23 is the highest performing of the datasets for representing seasonal and interannual trends in the coastal wind resource. While GWA3 and WTK-LED Climate are relatively insensitive to the dataset output heights selected for wind resource assessment at small and midsize wind turbine hub heights (20–60 m), significant variation in the NOW-23 representation of wind shear across the wind profile in the lowest 100 m of the atmosphere leads to notable differences in wind speed estimates according to the dataset output heights selected for evaluation. GWA3 exhibits challenges in the representation of observed wind speed diurnal cycles at small and midsize turbine hub heights, likely due to the dataset's consistent treatment of hourly wind speed trends regardless of altitude.

17 WIND ENERGY↗
DOE OSTI2025

A Comparison of Pre‐Construction and Operational Wake Loss Estimates for Land‐Based Wind Plants

The overall bias between pre‐construction energy yield assessment (EYA) estimates of wind plant energy production and the achieved operational production is improving in the wind industry, but uncertainty remains high for individual wind plants. Wake effects within wind plants are one of the largest sources of energy loss considered in the EYA process, and previous work shows wake loss estimates to be a major source of disagreement among wind energy consultants who perform EYAs. To better understand the accuracy of wake loss predictions, we compare overall operational wake loss estimates based on supervisory control and data acquisition data to pre‐construction estimates provided by six wind energy consultants for five land‐based wind plants in North America. By augmenting existing approaches for quantifying operational wake losses, we estimate wake losses during the period of record for which operational data are available as well as the expected long‐term wake losses, based on historical reanalysis weather data, to which the EYA estimates are compared. To account for power variations at different turbine locations caused by terrain‐induced wind resource heterogeneity, we correct the operational wake loss estimates using predicted freestream wind speed variations from the Wind Systems Engineering Reynolds‐averaged Navier–Stokes (RANS) tool. We identify long‐term corrected operational wake losses between 1.9% and 6.4% for the five plants, with a mean loss of 4%. For the project deemed most acceptable for operational wake loss assessment, which is located in the simplest terrain and isolated from neighboring plants, the mean EYA wake loss estimate is within 0.7 percentage points of the operational value of 6.4%. For most of the remaining plants, results suggest that wake losses are generally overpredicted by 2.6–6.3 percentage points. However, operational wake losses may be underestimated for many of these projects because of spatial wind resource variations not captured by the RANS model, external wake effects that are unaccounted for in the estimation process, and wind plant blockage effects. To better understand factors that contribute to the observed wake losses, we investigate operational wake losses as a function of wind direction and wind speed. As expected, wake losses are generally concentrated near wind directions that are aligned with rows of closely spaced turbines and at below‐rated wind speeds; however, for some projects, the energy produced by the wind plant exceeds the estimated potential energy of the plant without wake interactions for certain wind directions and wind speeds, suggesting inaccurate assumptions in the wake loss estimation method for those plants. Lastly, we compare predicted and operational wake losses for individual wind turbines, finding that even when overall wake losses are predicted accurately, large uncertainty exists at the turbine level.

17 WIND ENERGY↗
NASA NTRS2016

Winds Measured by the Rover Environmental Monitoring Station (REMS) During the Mars Science Laboratory (MSL) Rover's Bagnold Dunes Campaign and Comparison with Numerical Modeling Using MarsWRF

A high density of REMS wind measurements were collected in three science investigations during MSL's Bagnold Dunes Campaign, which took place over approx. 80 sols around southern winter solstice (Ls approx. 90deg) and constituted the first in situ analysis of the environmental conditions, morphology, structure, and composition of an active dune field on Mars. The Wind Characterization Investigation was designed to fully characterize the near-surface wind field just outside the dunes and confirmed the primarily upslope/downslope flow expected from theory and modeling of the circulation on the slopes of Aeolis Mons in this season. The basic pattern of winds is 'upslope' (from the northwest, heading up Aeolis Mons) during the daytime (approx. 09:00-17:00 or 18:00) and 'downslope' (from the southeast, heading down Aeolis Mons) at night (approx. 20:00 to some time before 08:00). Between these times the wind rotates largely clockwise, giving generally westerly winds mid-morning and easterly winds in the early evening. The timings of these direction changes are relatively consistent from sol to sol; however, the wind direction and speed at any given time shows considerable intersol variability. This pattern and timing is similar to predictions from the MarsWRF numerical model, run at a resolution of approx. 490 m in this region, although the model predicts the upslope winds to have a stronger component from the E than the W, misses a wind speed peak at approx. 09:00, and under-predicts the strength of daytime wind speeds by approx. 2-4 m/s. The Namib Dune Lee Investigation reveals 'blocking' of northerly winds by the dune, leaving primarily a westerly component to the daytime winds, and also shows a broadening of the 1 Hz wind speed distribution likely associated with lee turbulence. The Namib Dune Side Investigation measured primarily daytime winds at the side of the same dune, in support of aeolian change detection experiments designed to put limits on the saltation threshold, and also appears to show the influence of the dune body on the local flow, though less clearly than in the lee. Using a vertical grid with lower resolution near the surface reduces the relative strength of nighttime winds predicted by MarsWRF and produces a peak in wind speed at approx. 09:00, improving the match to the observed diurnal variation of wind speed, albeit with an offset in magnitude. The annual wind field predicted using this grid also provides a far better match to observations of aeolian dune morphology and motion in the Bagnold Dunes. However, the lower overall wind speeds than observed and disagreement with the observed wind direction at approx. 09:00 suggest that the problem has not been solved and that alternative boundary layer mixing schemes should be explored which may result in more mixing of momentum down to the near-surface from higher layers. These results demonstrate a strong need for in situ wind data to constrain the setup and assumptions used in numerical models, so that they may be used with more confidence to predict the circulation at other times and locations on Mars.

Newman, Claire E.↗