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DOE OSTI2022

Geographical variability of summer- and winter-dominant onshore wind

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

17 WIND ENERGY↗
DOE OSTI2014

National Offshore Wind Energy Grid Interconnection Study

The National Offshore Wind Energy Grid Interconnection Study (NOWEGIS) considers the availability and potential impacts of interconnecting large amounts of offshore wind energy into the transmission system of the lower 48 contiguous United States. A total of 54GW of offshore wind was assumed to be the target for the analyses conducted. A variety of issues are considered including: the anticipated staging of offshore wind; the offshore wind resource availability; offshore wind energy power production profiles; offshore wind variability; present and potential technologies for collection and delivery of offshore wind energy to the onshore grid; potential impacts to existing utility systems most likely to receive large amounts of offshore wind; and regulatory influences on offshore wind development. The technologies considered the reliability of various high-voltage ac (HVAC) and high-voltage dc (HVDC) technology options and configurations. The utility system impacts of GW-scale integration of offshore wind are considered from an operational steady-state perspective and from a regional and national production cost perspective.

17 WIND ENERGY↗
DOE OSTI2018

Texturizing Wind Turbine Towers to Reduce Bat Mortality

There is still a great need for the development of mitigation strategies that are economically viable and can be used alone or in combination with other options (e.g., operational minimization, acoustic deterrents) to ensure that bat fatalities are effectively reduced at wind facilities across the U.S. Our goal was to develop a wind turbine tower coating that 1) bats show little or no interest in approaching, 2) can be applied to currently deployed wind turbine towers and to towers as they are constructed, 3) is economically feasible to produce and apply, and 4) ultimately contributes to a reduction in bat mortality at utility-scale wind facilities. Wind turbine tower monopoles are large, smooth surfaces and numerous studies have reported bats making close investigative approaches at turbine tower surfaces that are suggestive of drinking and foraging behavior. We therefore hypothesized that such activity could be contributing to bat-wind turbine fatalities by increasing the amount of time that bats spend in or around the rotor-swept zone. We further hypothesized that a texturizing the tower might minimized bat activity at the towers and therefore reduce fatality risk. To explore this, we designed a series of experiments to characterize how bats interact with smooth and texture-treated surfaces in a controlled environment. We conducted 64 behavioral trials in a flight facility from 10 June to 25 September 2015 and an additional 76 behavioral trials from 9 June to 30 August 2016. The first set of trials showed that while bats did make contact with smooth surfaces, they did not make contact with textured surfaces. We ascertained that bat activity occurred at 0 to 1 m distances from surfaces, suggesting that the bats (used in this study) may need to be this close to a surface to accurately discern its texture. From the results were developed a texture coating. For the second set of trials, we tested the texture coating. Bats showed a >40% reduction in activity within 1 m of the textured surfaces in comparison to the smooth surfaces. Following these controlled experiments, we conducted field tests at Wolf Ridge Wind, LLC. We applied the texture coating to the midsection of turbine monopoles. The texture coating was manually applied to 2 turbine towers, although we noted during the application there was variation in texture coating between turbines. We considered that this variation could potentially impact bat behavior. We then conducted bat activity surveys at both texture-treated towers and 2 nearby control towers, over 55 nights from 24 June to 22 September, 2017. All species of bats known to be present at the site were detected in the acoustic recordings at these turbine towers. We found no significant differences in overall numbers of bats observed and levels of acoustic activity at the smooth and texture-treated towers. When we analyzed the experimental pairs separately, however, we found that a greater proportion of the close range bat activity within Pair 1 occurred at the texture-treated tower and that much of this activity could be attributed to foraging behavior. In contrast at turbine Pair 2, we observed no difference in the proportion of close range bat activity between the smooth and texture-treated tower, with no clear differences in behavior at the two tower types. When we examined acoustic bat activity at the tower pairs separately, we found species-specific differences in activity that differed between the smooth and texture-treated towers within the pairs. For example, hoary bat acoustic activity was significantly higher at the texture-treated tower in Pair 1, but significantly lower at the texture-treated tower in Pair 2. Given the observational nature of the field test, our limited sample size (2 turbine pairs), variation in the texture-coating between the 2 turbine pairs, and different patterns of bat activity observed at the 2 turbine pairs, inferences from this one field test are limited at best. In conclusion, the results from the flight facility experiments were promising (i.e., we observed a significant decrease in bat activity at smooth compared to texture-treated surfaces); however, due to coating installation and application challenges prior to our field test, we were unable to collect sufficient information to understand how these coatings affect bat activity at operational wind turbine towers. Although the results from the field test are inconclusive, the lessons learned will be informative for future research into turbine surface materials that can modify the surface of the turbine tower and therefore alter how bats perceive wind towers. Thus, the outcomes of this research project may have a significant impact on wind energy operators, wind developers, and OEMs by contributing to future impact mitigation strategies. In future research we will test new ideas to improve the development, application, and evaluation of this mitigation technique.

17 WIND ENERGY↗
DOE OSTI2014

Analyzing Effects of Turbulence on Power Generation Using Wind Plant Monitoring Data: Preprint

In this paper, a methodology is developed to analyze how ambient and wake turbulence affects the power generation of a single wind turbine within an array of turbines. Using monitoring data from a wind power plant, we selected two sets of wind and power data for turbines on the edge of the wind plant that resemble (i) an out-of-wake scenario (i.e., when the turbine directly faces incoming winds) and (ii) an in-wake scenario (i.e., when the turbine is under the wake of other turbines). For each set of data, two surrogate models were then developed to represent the turbine power generation (i) as a function of the wind speed; and (ii) as a function of the wind speed and turbulence intensity. Support vector regression was adopted for the development of the surrogate models. Three types of uncertainties in the turbine power generation were also investigated: (i) the uncertainty in power generation with respect to the published/reported power curve, (ii) the uncertainty in power generation with respect to the estimated power response that accounts for only mean wind speed; and (iii) the uncertainty in power generation with respect to the estimated power response that accounts for both mean wind speed and turbulence intensity. Results show that (i) under the same wind conditions, the turbine generates different power between the in-wake and out-of-wake scenarios, (ii) a turbine generally produces more power under the in-wake scenario than under the out-of-wake scenario, (iii) the power generation is sensitive to turbulence intensity even when the wind speed is greater than the turbine rated speed, and (iv) there is relatively more uncertainty in the power generation under the in-wake scenario than under the out-of-wake scenario.

17 WIND ENERGY↗
DOE OSTI2025

Future Wind Energy Resources and Cost Uncertainties Across the United States

This dataset contains results estimating projections of change of annual capacity factors and levelized cost of energy for several turbine technologies in the 2024 Annual Technology Baseline (ATB). Projections of change are based on downscaled earth system model (ESM) data from Sup3rCC. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run land-based wind energy models with a range of possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States to estimate critical system performance metrics such as annual energy production (AEP) and levelized cost of energy. Where multiple earth system models agree, changes in mean AEP from the time period 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Several additional states in the Midwest that currently have considerable wind generation capacity show the possibility of substantial decreases in AEP by mid-century. Larger turbines and moderate reductions in system costs can offset even the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will actually change into the future and to what extent wind energy systems can drive down future costs. An analysis of variance shows, in several states in the Midwest, the uncertainty in future wind resource can be almost as important for future changes in the cost of wind energy as the uncertainty in future system costs.

17 WIND ENERGY↗
DOE OSTI2019

Forest wind regimes and their implications on cross-canopy coupling

An analysis of wind regimes across forests of varying density and height is presented. The hockey stick-like dependence of the friction velocity on the mean advective wind speed is used to determine the threshold between the weak-wind and the strong-wind regime for the entire canopy. Such thresholds are compared across four different sites. The height dependence of thresholds within the canopy is inverted compared to the one above grassland sites. This can be understood by extending the accepted interpretation for weak-wind thresholds from grassland sites to forested sites. Even for large fluctuations above the canopy, the fluctuations of the vertical wind velocity in the subcanopy remain small during weak-wind situations. Correspondingly, in the strong-wind regime, turbulence in the subcanopy remains strong in spite of reduced above-canopy turbulence. This fact suggests that previously used methods for determining the degree of coupling across the canopy layer based upon the ratio of the vertical wind variance between the above-canopy and subcanopy may prove erroneous. Furthermore, it emphasizes the significance of mechanisms generating subcanopy turbulence other than above-canopy turbulent transport and shear. The transport of mass and energy between the subcanopy and above-canopy layers is significantly reduced during the weak-wind regime. In particular, the vertical turbulent transport is reduced by more than one order of magnitude. This suggests a decoupling of the subcanopy layer during weak-wind situations and allows for the accumulation of carbon dioxide originating from soil respiration in the subcanopy layer during the weak-wind regime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

The evolution of 1 AU equatorial solar wind and its association with the morphology of the heliospheric current sheet from solar cycles 23 to 24

The solar wind can be categorized into three types based on its 'freeze-in' temperature (T {sub freeze-in}) in the coronal source: low T {sub freeze-in} wind mostly from coronal holes, high T {sub freeze-in} wind mostly from regions outside of coronal holes, including streamers (helmet streamer and pseudostreamer), active regions, etc., and transient interplanetary coronal mass ejections (ICMEs) usually possessing the hottest T {sub freeze-in}. The global distribution of these three types of wind has been investigated by examining the most effective T {sub freeze-in} indicator, the O{sup 7+}/O{sup 6+} ratio, as measured by the Solar Wind Ion Composition Spectrometer on board the Advanced Composition Explorer (ACE) during 1998-2008 by Zhao et al. In this study, we extend the previous investigation to 2011 June, covering the unusual solar minimum between solar cycles 23 and 24 (2007-2010) and the beginning of solar cycle 24. We find that during the entire solar cycle, from the ascending phase of cycle 23 in 1998 to the ascending phase of cycle 24 in 2011, the average fractions of the low O{sup 7+}/O{sup 6+} ratio (LOR) wind, the high O{sup 7+}/O{sup 6+} ratio (HOR) wind, and ICMEs at 1 AU are 50.3%, 39.4%, and 10.3%, respectively; the contributions of the three types of wind evolve with time in very different ways. In addition, we compare the evolution of the HOR wind with two heliospheric current sheet (HCS) parameters, which indicate the latitudinal standard deviation (SD) and the slope (SL) of the HCS on the synoptic Carrington maps at 2.5 solar radii surface. We find that the fraction of HOR wind correlates with SD and SL very well (slightly better with SL than with SD), especially after 2005. This result verifies the link between the production of HOR wind and the morphology of the HCS, implying that at least one of the major sources of the HOR wind must be associated with the HCS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Regime-Switching Spatio-Temporal GARCH Method for Short-Term Wind Forecasting

The growth of wind energy poses challenges to the integration of wind energy into the power grid. Within a wind farm, the conditions of local wind exhibit sizeable variations in very short term period and temporal wind speed patterns vary from turbine to turbine. Hence, short-term wind forecasting has been adopted to assist power system operations. In this work, we propose a wind plant-level short term wind speed and power forecasting methodology considering turbine contributions. The proposed model utilizes spatio-temporal dependencies and nonstationarity to accommodate the characteristics of wind farm data by using a novel regime-switching spatiotemporal generalized autoregressive conditional heteroscedasticity (RS-stGARCH) model. Case studies based on 2 years of data from a wind farm shows that the proposed RS-stGARCH method outperforms benchmark models by up to 21.10% for wind speed forecasting and up to 58.62% for the wind power forecasting.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Tonopah Test Range Air Monitoring: CY2017 Meteorological, Radiological, and Wind Transported Particulate Observations

In 1963, the U.S. Department of Energy (DOE) (formerly the Atomic Energy Commission [AEC]), implemented Operation Roller Coaster on the Tonopah Test Range (TTR) and an adjacent area of the Nevada Test and Training Range (NTTR) (formerly the Nellis Air Force Range). This operation resulted in radionuclide-contaminated soils at the Double Tracks site and Clean Slate I, II, and III sites. This report documents observations made during ongoing monitoring of radiological, meteorological, and dust conditions at stations installed adjacent to the Clean Slate sites and at the TTR Sandia National Laboratories (SNL) Range Operations Center (ROC). The primary objective of the monitoring effort is to support the DOE Environmental Management Nevada Program (EM NV) in the safe cleanup of the environmental legacy of nuclear device development and testing by determining if wind blowing across the Clean Slate sites is transporting particles of radionuclide-contaminated soil beyond the physical and administrative boundaries of the sites. The monitoring program underwent a significant change during 2017 with the decommissioning of one monitoring station and the establishment of three new stations. Station 400, located within TTR Area 3 and near the ROC, monitors conditions near the local workforce center and was in operation the full calendar year. Stations 401 and 403 are located on the northwest and southeast perimeter fence lines, respectively, of the Clean Slate III site. Station 401 operated throughout 2017, whereas Station 403 began operation at the end of April 2017. Stations 404 and 405 are along the northern and eastern boundary fence, respectively, at Clean Slate II and both began data collection at the end of April 2017. Also at the end of April, the Clean Slate I monitoring site, Station 402, was decommissioned. The stations are located to be generally downwind of the contaminated areas during either north-northwesterly or south-southeasterly winds, which are the predominant wind directions. The stations—which are similar in design to the Community Environmental Monitoring Program (CEMP) stations operating at locations surrounding the Nevada National Security Site and TTR—include meteorological instruments and continuous-flow, low-volume air samplers. Three pressurized ionization chambers for measuring gamma energy are in use at Stations 400, 401, and 403 (formerly at 402). Saltation sensors and saltation traps are deployed at the stations adjacent to the Clean Slate sites. Detailed meteorological data are recorded on data loggers, with periodic uploads via a satellite system to the Western Regional Climate Center (WRCC) at Desert Research Institute (DRI), to monitor instrument and site conditions. Air filter samples are collected every two weeks and the deposits in the saltation traps are collected when a sufficient sample mass for analysis has accumulated (generally a six- to eight-month interval). Dust transport by suspension and saltation is strongly dependent on wind speed. Concentrations of PM10 (particulate matter of aerodynamic diameter ≤10 micrometers [μm], a size fraction of small particles that are suspended in the air and can be easily inhaled) remain low until winds exceed approximately 32 km/hr (20 mph). Saltation particle counts also increase above the same general threshold wind speed. Wind speeds in excess of 32 km/hr (20 mph) occur less than three percent of the time. High winds are associated with two predominant directions: north-northwest and south. In 2017, the very highest winds were from the north, but all winds above 40 km/hr (25 mph) were approximately evenly split between south and northwest directions. The low-volume air samplers collect total suspended dust, and saltating particles are captured by the passive saltation traps, both for radiological analysis. Radionuclide monitoring at the ROC in TTR Area 3 (Station 400) found natural background levels of gross alpha and gross beta radiation. Similar background levels were found on filter samples analyzed using gamma and alpha spectroscopy. The gamma exposure rate data from pressurized ionization chambers (PIC) are comparable to those from the regional CEMP stations. Plutonium concentrations above background values were measured during 2017 from soil samples collected in passive saltation traps and on select air filters from the monitoring stations adjacent to the Clean Slate sites. The background for soils in the region is assumed to be 0.014 pCi/g plutonium-239+240 ( 239+240 Pu) as measured by Turner et al. (2003), whereas the background concentration of suspended 239+240Pu is assumed to be 9.5 x 10 -19 μCi/ml, which was determined by continuous air monitoring at the TTR airport in 1996 and 1997 (SNL, 1998). Background concentrations are exceeded by one to four orders of magnitude by the majority of samples from Clean Slate I, II, and III. The gross alpha and gross beta measurements of the air filters are also generally higher than those measured at the regional CEMP stations, although the gamma exposure rates determined by the PICs located at Clean Slate I and III are similar to CEMP measurements. The highest radionuclide concentrations of suspended dust are associated with individual filter samples from Clean Slate II Station 404 and Clean Slate III Station 401. Both of the stations are downwind of their respective sites during southerly winds, but they are also geographically closer to the highly contaminated detonation areas than the other monitoring stations. Considering all the analyzed samples, the 239+240Pu concentrations are generally higher for the Clean Slate II samples compared with those from Clean Slate III. The highest gross alpha and gross beta results are also observed for the Clean Slate II stations. The highest radionuclide concentrations are measured on the smaller (≤63 μm) saltation-sized particles and particulate matter collected downwind of each site. Nonetheless, traps oriented to collect dust moving toward the contamination areas also had plutonium concentrations significantly above background values, which is consistent with observations of low-level contamination dispersed beyond the fenced area. All the observed 239+240 Pu concentrations are below the risk-based action level (25 millirem per year, equivalent to a 239+240 Pu concentration of 4,120 pCi/g for an industrial worker scenario; U.S. DOE, 2014). The meteorological and particulate matter monitoring indicate that conditions for wind-borne contaminant movement exist at the Clean Slate sites, and that transport of radionuclide-contaminated soil by both suspension and saltation is occurring. The Corrective Action Unit (CAU) closure strategy uses a risk-based approach, whereby acceptable contaminant concentrations are determined as a function of anticipated human exposure. This strategy is consistent with the fence lines at the Clean Slate sites encircling areas of higher concentration and the observed presence of lower concentrations of contamination outside the fences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Computational analysis of deployable wind turbine systems in defense operational energy applications.

The U.S. military has been exploring pathways to reduce the logistical burden of fuel on virtually all their missions globally. Energy harvesting of local resources such as wind and solar can help increase the resilience and operational effectiveness of military units, especially at the most forward operating bases where the fuel logistics are most challenging. This report considers the potential benefits of wind energy provided by deployable wind turbines as measured by a reduction in fuel consumption and supply convoys to a hypothetical network of Army Infantry Brigade Combat Team bases. Two modeling and simulation tools are used to represent the bases and their operations and quantify the impacts of system design variables that include wind turbine technologies, battery storage, number of turbines, and wind resource quality. The System of Systems Analysis Toolkit Joint Operational Energy Model serves as a baseline scenario for comparison. The Hybrid Optimization of Multiple Energy Resources simulation tool is used to optimize a single base within the larger Joint Operational Energy Model. The results of both tools show that wind turbines can provide significant benefits to contingency bases in terms of reduced fuel use and number of convoy trips to resupply the base. The match between the turbine design and wind resource, which is statistically low across most of the global land area, is a critical design consideration. The addition of battery storage can enhance the benefits of wind turbines, especially in systems with more wind turbines and higher wind resources. Wind turbines may also provide additional benefits to other metrics such as resilience that may be important but not fully considered in the current analysis. ACKNOWLEDGEMENTS The authors would like to thank the following individuals for their helpful support, feedback and review to improve this report: U.S. Department of Energy Wind Energy Technologies Office, Patrick Gilman and Bret Barker; Idaho National Laboratory, Jake Gentle and Bradley Whipple; The National Renewable Energy Laboratory, Robert Preus and Tony Jimenez; Sandia National Laboratories, Alan Nanco, Dennis Anderson, and Hai Le. In addition, numerous discussions with military and industry stakeholders over the year were invaluable in focusing the efforts represented in this report.

17 WIND ENERGY↗
DOE OSTI2026

FOCAL Campaign I: Advanced Wind Turbine Control Strategies

Campaign I of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aims to generate a dataset enabling the validation of aerodynamic performance of a scaled turbine mounted on a rigid tower in a fixed condition. The turbine considered in the FOCAL testing campaigns is the IEA-Wind 15MW Reference Wind Turbine. This scaled model is capable of simulating advanced blade-pitch control strategies in a high-quality wind field. The turbine is fully instrumented to record a variety of parameters in real time such as structural loads and dynamics. The test data considered was generated at the University of Maine's Harold Alfond Wind and Wave (W2) testing facility. The Load Cases (LC) considered in this testing campaign are as follows: LC 1.X - Constant wind and blade-pitch with varying rotor speeds LC 2.X - Constant wind and rotor speed with varying blade-pitch LC 3.X - Varying wind with active closed-loop control Detailed properties on the modeled system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaign I: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine. UMaine ASCC Report Number 23-40-1183. Details on the results of the verification and validation are found in the following reference: Mendoza, Nicole et al., "Verification and Validation of Model-Scale Turbine Performance and Control for the IEA Wind 15 MW Reference Wind Turbine," Energies, vol. 15, no. 20, 2022, https://doi.org/10.3390/en15207649.

17 WIND ENERGY↗
DOE OSTI2024

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

17 WIND ENERGY↗
DOE OSTI2016

RECONSTRUCTING THE SOLAR WIND FROM ITS EARLY HISTORY TO CURRENT EPOCH

Stellar winds from active solar-type stars can play a crucial role in removal of stellar angular momentum and erosion of planetary atmospheres. However, major wind properties except for mass-loss rates cannot be directly derived from observations. We employed a three-dimensional magnetohydrodynamic Alfvén wave driven solar wind model, ALF3D, to reconstruct the solar wind parameters including the mass-loss rate, terminal velocity, and wind temperature at 0.7, 2, and 4.65 Gyr. Our model treats the wind thermal electrons, protons, and pickup protons as separate fluids and incorporates turbulence transport, eddy viscosity, turbulent resistivity, and turbulent heating to properly describe proton and electron temperatures of the solar wind. To study the evolution of the solar wind, we specified three input model parameters, the plasma density, Alfvén wave amplitude, and the strength of the dipole magnetic field at the wind base for each of three solar wind evolution models that are consistent with observational constrains. Our model results show that the velocity of the paleo solar wind was twice as fast, ∼50 times denser and 2 times hotter at 1 AU in the Sun's early history at 0.7 Gyr. The theoretical calculations of mass-loss rate appear to be in agreement with the empirically derived values for stars of various ages. These results can provide realistic constraints for wind dynamic pressures on magnetospheres of (exo)planets around the young Sun and other active stars, which is crucial in realistic assessment of the Joule heating of their ionospheres and corresponding effects of atmospheric erosion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast

The US West Coast holds great potential for wind power generation, although its potential varies due to the complex coastal climate. Characterizing and modeling turbine hub-height winds under different weather conditions are vital for wind resource assessment and management. This study uses a two-stage machine learning algorithm to identify five large-scale meteorological patterns (LSMPs): post-trough, post-ridge, pre-ridge, pre-trough, and California high. The LSMPs are linked to offshore wind patterns, specifically at lidar buoy locations within lease areas for future wind farm development off Humboldt and Morro Bay. While each LSMP is associated with characteristic large-scale atmospheric conditions and corresponding differences in wind direction, diurnal variation, and jet features at the two lidar sites, substantial variability in wind speeds can still occur within each LSMP. Wind speeds at Humboldt increase during the post-trough, pre-ridge, and California-high LSMPs and decrease during the remaining LSMPs. Morro Bay has smaller responses in mean speeds, showing increased wind speed during the post-trough and California-high LSMPs. Besides the LSMPs, local factors, including the land–sea thermal contrast and topography, also modify mean winds and diurnal variation. The High-Resolution Rapid Refresh model analysis does a good job of capturing the mean and variation at Humboldt but produces large biases at Morro Bay, particularly during the pre-ridge and California-high LSMPs. The findings are anticipated to guide the selection of cases for studying the influence of specific large-scale and local factors on California offshore winds and to contribute to refining numerical weather prediction models, thereby enhancing the efficiency and reliability of offshore wind energy production.

17 WIND ENERGY↗
DOE OSTI2026

Emerging mobile lidar technology to study boundary layer winds influenced by operating turbines

The development of a microjoule-class pulsed Doppler lidar and deployment of this compact system on mobile platforms such as aircraft, ships, or trucks have opened a new opportunity to characterize the dynamics of complex mesoscale wind flows. The PickUp-based Mobile Atmospheric Sounder (PUMAS) truck-based lidar system was recently used during the American Wake Experiment (AWAKEN) to assess the general structure of boundary layer (BL) wind and turbulence around wind turbines in central Oklahoma. Wind speed profiles averaged over PUMAS transects influenced by the operating turbines (waked flow) show a 1–2 m s −1 reduction compared to mean undisturbed (free flow) wind speed profiles. Spatial variability in wind speed was observed in time–height cross-sections at different distances from turbines. The wind speeds were about 9–12 m s −1 at 6 km distance compared to 5–7 m s −1 at the transects near the turbines. The PUMAS dataset from AWAKEN demonstrated the capability of the mobile Doppler lidar system to document spatial variability in wind flows at different distances from wind turbines and obtain quantitative estimates of wind speed reduction in the waked flow. The high-frequency, simultaneous measurements of the horizontal and vertical winds provide a new approach for characterizing dynamic processes critical for wind farm wake analyses.

17 WIND ENERGY↗
NASA NTRS2014

Configuration and Evaluation of a Dual-Doppler 3-D Wind Field System

Current LSP, GSDO, and SLS space vehicle operations are halted when wind speeds from specific directions exceed defined thresholds and when lightning is a threat. Strong winds and lightning are difficult parameters for the 45th Weather Squadron (45 WS) to forecast, yet are important in the protection of customer vehicle operations and the personnel that conduct them. A display of the low-level horizontal wind field to reveal areas of high winds or convergence would be a valuable tool for forecasters in assessing the timing of high winds, or convection initiation and subsequent lightning occurrence. This is especially important for areas where no weather observation platforms exist. Developing a dual-Doppler radar capability would provide such a display to assist forecasters in predicting high winds and convection initiation. The wind fields can also be used to initialize a local mesoscale numerical weather prediction model to help improve the model forecast winds, convection initiation, and other phenomena. The 45 WS and NWS MLB tasked the Applied Meteorology Unit (AMU) to develop a dual- Doppler wind field display using data from the 45th Space Wing radar, known as the Weather Surveillance Radar (WSR), NWS MLB Weather Surveillance Radar 1988 Doppler (KMLB), and the Orlando International Airport Terminal Doppler Weather Radar (KMCO). They also stipulated that the software used should be freely available. The AMU evaluated two software packages and, with concurrence from NWS MLB and the 45 WS, chose the Warning Decision Support System-Integrated Information (WDSS-II). The AMU collected data from two significant weather cases: a tornadic event on 14 April 2013 and a severe wind and hail event on 12 February 2014. For the 14 April case, the data were from WSR and KMLB. For the 12 February case, the data were from KMCO and KMLB. The AMU installed WDSS-II on a Linux PC, then processed and quality controlled the radar data for display and analysis using WDSS-II tools. Because of issues with de-aliasing the WSR velocity field, the AMU did not use data from this radar in this study and only analyzed the 12 February case. Merging the data to create the dual-Doppler analysis involved several steps. The AMU used instructions from the WDSS-II website and discussion forum to determine the correct tools to use for the analysis, and was successful in creating a merged reflectivity field, which was critical to the success of creating a merged velocity field. However, the AMU was unable to create a merged velocity field. The AMU researched the WDSS-II forum for discussions on similar issues, asked questions on the forum, and tested different options and values in the merger tool with no success. Developing a dual-Doppler wind field was the main goal of this task, but that was not accomplished. It could be an issue of not using the correct options or the correct value for the options used, or there could be issues with the radar data. There is a follow-on AMU task to install the operational version of WDSS-II in the NWS MLB office. This will provide more opportunities to try different options and input values in order to create a merged wind field from KMCO and KMLB.

Doppler radar↗
DOE OSTI2013

Investigating Short Circuit Models for Wind Turbine Generators

Short circuit study needs to be performed at the planning stage before connecting large gen erators with the electric grid. This study will reveal the impact of new generation on the fault levels of the grid. The fault levels will determine the rating of nearby equipment and setting of relays. However, a short circuit model of the new generation is required for this study. This report investigates and quantifies the short circuit models of different wind turbine generators (WTGs) and of wind farms, and using these models, explores the effects of the wind farms on the nearby switchgear. WTGs, commonly classified as Type 1, Type 2, Type 3, and Type 4, exhibit different short circuit behavior depending upon their architecture and controls. The short circuit model (synonymous to Thevenin model) of Type 1 WTG is well known; it comprises of a voltage source behind a transient reactance, X'. The transient reactance X' is derived using the reactance of stator, rotor and the magnetizing reactance. In case of Type 2 WTG, an external rotor resistance is used to increase the range of operating slips. The value of the external rotor resistance increases at higher operating slips. The effect of the external rotor resistance on the fault current response is analyzed by simulating a validated model of Type 2 WTG. The validation is performed using the field data from an actual 67-machine Type 2 wind farm located in Wyoming, USA. The results shows that the external rotor resistance should be considered in its Thevenin impedance for better accuracy. A wind farm contains a large number of WTGs, pad mounted transformers, and cables typically connected in a ‘daisy chain” configuration. The effect of these cable impedances on the short circuit model of Type 2 wind farm is examined using the validated models. It is found that the collector circuit cables inside the farm do not have a significant effect on the short circuit model of the farm. Based on this finding, a simple equivalencing procedure for the wind farm is proposed and validated. Faults inside and outside the wind farm are analyzed and recommendations to select main circuit breakers and generator circuit breakers are made. Type 3 WTG, also known as Doubly Fed Asynchronous Generator (DFAG), uses back to back converters in the rotor circuit to inject slip frequency voltage into the rotor circuit. Until recently, these machines used crowbar to short the rotor circuit during faults to protect the converter com ponents form high rotor currents resulting from faults. This type of WTG is simulated and the model is validated using field data from a 66-machine Type 3 wind farm in Wyoming, USA. The simulation study reveals that the crowbar feature is activated almost immediately after the fault takes place, and remains activated throughout the fault-period. This confirms that the short circuit model for Type 3 WTG with crowbar is similar to the model for Type 2 WTG. In the past, wind farms were allowed to disconnect during faults. However, recent grid codes around the world and in the United States demand that the wind farms be kept connected to the grid during faults, and provide certain reactive power quantified based on the voltage dip at the point of interconnection. This requirement, also called low voltage ride through (LVRT), can be accomplished by using Type 4 WTGs that employ full back to back converter interface between generators (permanent magnet synchronous generators are typically used) and the grid. However, the controls are proprietary, and hence the short circuit models are not well defined. This report proposes controls that enable the WTG to comply with the strictest grid codes, and based on this model, proposes a short circuit model of the Type 4 WTG. This model is not validated due to lack of validation data, but the use of grid code requirements as control objectives lends good credibility to the model. PSCAD/EMTDC ® , a powerful and time-tested time-domain simulation software is used to implement all models. This software is capable of modeling in detail all components of wind farms, and all components and controls of converters in time domain.

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DOE OSTI2022

2021 Prototype Manufacture and Installation Awardee: Pecos Wind Power, Inc.

Through the 2021 Competitiveness Improvement Project (CIP), Pecos Wind Power will manufacture a prototype of its 85-kilowatt (kW) horizontal-axis distributed wind turbine, the PW85, a new wind turbine that began development in 2017 when the company was founded. The PW85 wind turbine includes an industry-leading rotor diameter (30 meters) and full-span variable pitch blades to target a levelized cost of energy (LCOE) of $0.103/kilowatt-hour in low annual wind speeds (6 meters per second). This is 55% lower than the average small wind turbine project installed in 2018. The goal of this project is to spur the development of increasingly lower-cost, high-capacity-factor distributed wind turbines. As a result, Pecos Wind Power will manufacture and install wind turbines that increase the geographic area in which distributed wind power is cost competitive with retail-priced electricity and other distributed energy resources - primarily solar energy.

CIP↗