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At least 37 records · Page 2

2022 Small Turbine Certification Awardee: NPS Solutions

Inverters convert the direct current power generated by wind turbines into the alternating current needed to serve local loads and feed onto the electricity grid. Although distributed wind systems typically supply electricity for homes and businesses near the turbines, these systems can also provide clean power to strengthen grid resilience. Certification to UL 1741 SA ensures that inverters will enable distributed wind turbines to provide grid support services and stay online when the grid experiences stability issues. Small companies often find the electrical listing process too complex and expensive to undertake on their own, so few distributed wind turbine inverters are certified to this standard. A U.S. Department of Energy (DOE) Competitiveness Improvement Project (CIP) award is helping NPS Solutions (NPS) secure this critical certification for the inverter in its 100-kilowatt NPS 100C wind turbine, which is optimized for on-site generation at farms, businesses, schools, hospitals, and remote locations.

CIP↗

Valuing wind as a distributed energy resource: A literature review

As more distributed energy resources are deployed on electric grid systems across the world, it is important to identify, characterize, and quantify the value elements of different types of distributed energy resources so that policymakers, developers, and utilities can make informed energy deployment decisions. This paper focuses on the value of wind energy as a distributed energy resource (i.e., “distributed wind”). Because of a lack of distributed wind-specific valuation studies, in this review we document the current state of distributed energy resource valuation, analyze a wide array of distributed energy resource valuation metastudies, and identify several value elements for which we recommend developing more robust and standardized calculation methodologies for their potential inclusion in distributed wind valuation. These value elements are ancillary services and locational, resilience, reliability, and resource diversity benefits. Furthermore, this work lays the foundation for a future comprehensive framework for distributed wind valuation studies.

17 WIND ENERGY↗

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

17 WIND ENERGY↗

An analysis of maximum horizontal wind speeds and associated parameters recorded at NASA's 150-Meter Ground Winds Tower facility at Kennedy Space Center, Florida

Continuous horizontal wind speed measurements were processed and classified as a function of tower level (10, 18, 60, and 150 meters) and period of reference day, month, season: winter (October through March) and summer (April through September), and annual. Tabulations were made of the daily maximum horizontal wind speed, time of ocurrence, and five associated parameters: mean horizontal wind speed, maximum vertical gusts (i.e., updraft and downdraft), and mean and instantaneous directions. Analyses using these data included means, extremes, standard deviations, and frequency distributions. Comparisons of intensity of maximum horizontal wind speeds determined in this year of data are made with maximum values recorded at Kennedy Space Center during another non-hurricane-occurrence year (1967) and with values during 1966 through 1972 when six hurricanes affected the area after the Ground Winds Tower facility became operational. Wind flow in the lowest 150 meters of the atmosphere was measured for the identification of hazards involved in wind shear encounter relative to ascent and descent of the space shuttle and conventional aircraft.

Alexander, M. B.↗

2021 Component Innovation Awardee: Siva Powers America

Siva Powers America earned a Competitiveness Improvement Project (CIP) Component Innovation award that will enable the company to evolve its 250/50 wind turbine design, discontinue its existing 13.4-meter wind turbine blades, and develop new 14.4-meter blades. These longer blades, which have additional extenders at the hub, will increase the rotor diameter to 35 meters, allowing for improved energy production at sites with lower wind resources. A collaboration with the National Renewable Energy Laboratory (NREL) includes a redesign, build, and on-site structural test for the new prototype blades. This will ultimately allow Siva Powers America to pursue recertification of the SIVA 250/50 to meet international standards.

CIP↗

Case Study: Applying the INL Resilience Framework to Iowa Lakes Electric Cooperative Distributed Wind Systems

Traditional metrics and evaluation methods for resiliency are not sufficient to evaluate the effect that distributed wind systems will have, particularly in light of the challenges described above. While the concept of resiliency is not new, its application to the electric grid is neither standardized nor well-defined, and there is little to no guidance on how to evaluate resilience specifically for distributed wind systems. To fill this gap, the Idaho National Laboratory (INL), as part of the multi-laboratory Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project, has developed a resilience framework for electric energy delivery systems (EEDS). The framework provides detailed steps for evaluating resiliency in the planning, operational, and future stages, and encompasses five core functions of resilience. It allows users to evaluate the resilience of distributed wind, taking into consideration the resilience of the wind systems themselves, as well as the effect they have on the resiliency of any systems they are connected to. In this study, we evaluate the resilience of the distributed wind systems at Iowa Lakes Electric Cooperative to cybersecurity hazards. We show that the wind resource can benefit the overall system resilience during some hazards. We show that the practices in place make the wind subsystems resilient against some cybersecurity hazards but that there are still significant risks associated with other cybersecurity hazards

17 WIND ENERGY↗

Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant

Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thereby increasing net energy production. But research suggests that the effectiveness of wake steering strongly depends on atmospheric conditions such as stability. In this paper, we investigate results from a two-turbine wake-steering experiment at a commercial wind plant to assess the impact of stability and five other atmospheric variables on wake-steering performance. Specifically, for different atmospheric condition bins we compare the ability of the controller to achieve the intended yaw offsets, the power gain from wake steering, and the reduction in wake losses. Further, we analyze wake-steering performance as a function of wind speed to eliminate the confounding impact of different wind speed distributions in different atmospheric conditions. Overall, we find that wind direction standard deviation is the best predictor of wake-steering performance, followed by turbulence intensity and turbulent kinetic energy. The results suggest the importance of adapting wake-steering control strategies to different atmospheric conditions.

17 WIND ENERGY↗

Third and fourth moments of vertical two-point differences of turbulent velocity fluctuations in the atmospheric boundary layer.

Results of recent measurements of the distribution function of wind shear and the associated third and fourth standardized moments. Emphasis is placed on the vertical variation of the longitudinal component of turbulence, i.e., the component of turbulence along the mean wind vector. The data source consists of 11 sets of longitudinal turbulent velocity fluctuation time histories digitized at 0.2-sec intervals with approximately 18,000 data points per history.

Fichtl, G. H.↗

A Global Model for Estimating Atmospheric Phase Scintillation Statistics

Since 2007, the National Aeronautics and Space Administration (NASA) has been collecting atmospheric phase turbulence data from various NASA ground stations throughout the world. The goal of these measurement campaigns has been to generate statistics to characterize the local site turbulence conditions and their impact on widely distributed ground based antenna arrays. This is of critical importance for the situation of uplink arraying, in which a priori knowledge of the fast varying turbulent conditions of water vapor in the troposphere may not be known, and will impact the power combining efficiency of ground based transmitting arrays. Therefore, the design of these type of systems will be dependent on the local climatology of the particular ground station site. Based on the 30+ station years of data collected characterizing atmospheric phase scintillation statistics at various sites, a global model is presented which attempts to predict the average phase statistics of a generic site based on local surface weather data, such as surface pressure, temperature, relative humidity, wind speed, and median wind direction. A model is proposed and based on a standard log power distribution similar to amplitude scintillation models trained on the existing data sets and shows reasonable accuracy against existing data sets.

propagation↗

Aeroelastic Modeling for Distributed Wind Turbines: March 11, 2021 - November 10, 2021

Aeroelastic modeling (AM) is the primary methodology for structural and performance assessment of any wind turbine whereby providing an understanding of the impact of design parameters on its loading and power response before witnessing it in the field. Despite these advantages, the use of AM in the Distributed Wind Technology (DWT) sector is limited, especially within the less established manufacturers. This project represents an in-depth assessment of the status of AM and its role within the Standards for the DWT industry. The study gathered input and feedback from a large number of national and international stakeholders, reviewed technical strengths and weaknesses of the current edition of the design standards, analyzed recent industry workshops' and meetings' minutes, collected publicly available AM templates, and provided an evaluation of the existing AM codes. The study achieved several goals including providing strategies for the load assessment categorization of turbines based on rotor swept area and archetype, and guidance for AM verification and validation (V&V), which includes discussions of measurement requirements and a sample test-plan useful for future V&V campaigns and design standard development. This document summarizes the different tasks conducted in the course of the project and highlights the steps required to improve the AM adoption based on a multifaceted approach that encompasses: 1) augmenting AM software capabilities, 2) publishing AM best-practices and design-basis, 3) creating new model templates, 4) providing guidance for V&V of codes and specific turbine models leveraging field testing best-practice, and 5) addressing weaknesses in the current standards. Many of the future objectives identified in this study could leverage NREL's upcoming testing campaigns of three modern distributed wind turbines. Recommendations within this study will advance the value and the ease-of-use of AM, thereby allowing the industry to better capitalize this underutilized tool resulting in a more efficient design process, an easier path to certification, and overall better and more distributed reliable wind turbine products.

17 WIND ENERGY↗

Competitiveness Improvement Project Informational Workshop

The National Renewable Energy Laboratory (NREL) is hosted an in-person workshop and webinar for the distributed wind Competitiveness Improvement Project (CIP) on Tuesday, December 17, 2019, at NREL's Flatirons Campus. The CIP is a periodic solicitation issued by NREL on behalf of the U.S. Department of Energy's Wind Energy Technologies Office. Through a competitive process, component suppliers and manufacturers of small- to medium-sized wind turbine technology are awarded cost-shared subcontracts to optimize their designs, develop advanced manufacturing processes, and perform turbine testing. The CIP aims to make wind energy cost competitive with other distributed generation technologies and increase the number of wind turbine designs certified to national performance and safety standards.

17 WIND ENERGY↗

Analysis of vector wind change with respect to time for Cape Kennedy, Florida

Multivariate analysis was used to determine the joint distribution of the four variables represented by the components of the wind vector at an initial time and after a specified elapsed time is hypothesized to be quadravariate normal; the fourteen statistics of this distribution, calculated from 15 years of twice-daily rawinsonde data are presented by monthly reference periods for each month from 0 to 27 km. The hypotheses that the wind component changes with respect to time is univariate normal, that the joint distribution of wind component change with respect to time is univariate normal, that the joint distribution of wind component changes is bivariate normal, and that the modulus of vector wind change is Rayleigh are tested by comparison with observed distributions. Statistics of the conditional bivariate normal distributions of vector wind at a future time given the vector wind at an initial time are derived. Wind changes over time periods from 1 to 5 hours, calculated from Jimsphere data, are presented. Extension of the theoretical prediction (based on rawinsonde data) of wind component change standard deviation to time periods of 1 to 5 hours falls (with a few exceptions) within the 95 percentile confidence band of the population estimate obtained from the Jimsphere sample data. The joint distributions of wind change components, conditional wind components, and 1 km vector wind shear change components are illustrated by probability ellipses at the 95 percentile level.

Adelfang, S. I.↗

Analysis of wind perturbation activity for the NSTS launches

A method for statistical analysis of wind perturbation activity is proposed for application in NSTS prelaunch assessment of the wind environment. Methods for comparing day-of-launch wind perturbation standard deviations with historical data at the launch site are described. A statistical model based on the Gumbel extreme value distribution is developed for the largest vector and component perturbations in a wind profile as a function of high-pass filter cutoff wavelength. Application of the model is proposed for assessing extreme perturbations within prelaunch sequential Jimsphere wind profiles.

Adelfang, Stanley I.↗

Implementing Cybersecurity for Distributed Wind: An Exercise in ICS Security Application

Distributed wind is unique and fast-growing component of the energy production mix, but cybersecurity for distributed wind is not well developed. In this presentation, we will introduce distributed wind as an example of difficult-to-secure critical infrastructure. We will first provide background on distributed wind architectures, applications, and stakeholder roles. We will explain the need and challenges for cybersecurity for distributed wind, pointing to both academic studies and real-world events. The key component of the presentation will discuss recommendations for different aspects of security, such as risk assessments, communications, and access control, that specifically call out considerations that apply uniquely to distributed wind. We will discuss what sets distributed wind apart from other ICS and energy applications, and how to account for these features in a comprehensive security plan. This process is demonstrative of the type of analysis needed to adapt traditional security guidelines and standards to narrow and focused applications.

17 WIND ENERGY↗

Evidence for Natural Variability in Marine Stratocumulus Cloud Properties Due to Cloud-Aerosol

In this study, aircraft observations from the Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter are used to characterize the variability in drizzle, cloud, and aerosol properties associated with cloud rifts and the surrounding solid clouds observed off the coast of California. A flight made on 16 July 1999 provided measurements directly across an interface between solid and rift cloud conditions. Aircraft instrumentation allowed for measurements of aerosol, cloud droplet, and drizzle spectra. CCN concentrations were measured in addition to standard thermodynamic variables and the winds. A Forward Scatter Spectrometer Probe (FSSP) measured size distribution of cloud-sized droplets. A Cloud Imaging Probe (CIP) was used to measure distributions of drizzle-sized droplets. Aerosol distributions were obtained from a Cloud Aerosol Scatterprobe (CAS). The CAS probe measured aerosols, cloud droplets and drizzle-sized drops; for this study. The CAS probe was used to measure aerosols in the size range of 0.5 micron - 1 micron. Smaller aerosols were characterized using an Ultrafine Condensation Particle Counter (CPC) sensor. The CPC was used to measure particles with diameters greater than 0.003 micron. By subtracting different count concentrations measured with the CPC, this probe was capable of identifying ultrafine particles those falling in the size range of 3 nanometers - 7 nanometers that are believed to be associated with new particle production.

Albrecht, Bruce↗

Assessment and Coordination of DER Cybersecurity Standards

Distributed energy resources (DERs) are becoming increasingly important to the electric grid, including solar energy systems. However, DERs also introduce new cybersecurity risks, including those posed by cloud computing. Standards harmonization is essential for ensuring that DERs are secure and can be safely integrated into the grid. This panel will discuss cyber standards harmonization for solar security. The panel will feature experts from the S2G Program, National Labs and Industry.

14 SOLAR ENERGY↗

Sensitivity of fatigue reliability in wind turbines: effects of design turbulence and the Wöhler exponent

Fatigue assessment of wind turbines involves three main sources of uncertainty: material resistance, load, and the damage accumulation model. Many studies focus on increasing the accuracy of fatigue load assessment to improve the fatigue reliability. Probabilistic modeling of the wind's turbulence standard deviation is an example of an approach used for this purpose. Editions 3 and 4 of the IEC standard for the design of wind energy generation systems (IEC 61400-1) suggest different probability distributions as alternatives for the representative turbulence in the normal turbulence model (NTM) of edition 1. There are debates on whether the suggested distributions provide conservative reliability levels, as the established design safety factors are calibrated based on the representative turbulence approach. The current study addresses the debate by comparing annual reliability based on different scenarios of NTM using a probabilistic approach. More importantly, it elaborates on the relative importance of load assessment accuracy in defining the fatigue reliability. Using the DTU 10 MW reference wind turbine and the first-order reliability method (FORM), we study the changes in the annual reliability level and its sensitivity to the three main random inputs. We perform the study considering the blade root flapwise and the tower base fore–aft moments, assuming different fatigue exponents in each load channel. The results show that integration over distributions of turbulence in each mean wind speed results in less conservative annual reliability levels than representative turbulence. The difference in the reliability levels varies according to turbulence distribution and the fatigue exponent. In the case of the tower base, the difference in the annual reliability index after 20 years can be up to 50 %. However, the model and material uncertainty have much higher effects on the reliability levels compared to load uncertainty. Knowledge about such differences in the reliability levels due to the choice of turbulence distribution is especially important, as it impacts the extent of lifetime extension through reliability reassessments.

17 WIND ENERGY↗

Low altitude wind shear statistics derived from measured and FAA proposed standard wind profiles

Wind shear statistics were calculated for a simulated data set using wind profiles proposed as a standard and compared to statistics derived from measured wind profile data. Wind shear values were grouped in altitude bands of 100 ft between 100 and 1400 ft, and in wind shear increments of 0.025 kt/ft between + or - 0.600 kt/ft for the simulated data set and between + or - 0.200 kt/ft for the measured set. No values existed outside the + or - 0.200 kt/ft boundaries for the measured data. Frequency distributions, means, and standard deviations were derived for each altitude band for both data sets, and compared. Also, frequency distributions were derived for the total sample for both data sets and compared. Frequency of occurrence of a given wind shear was about the same for both data sets for wind shears, but less than + or 0.10 kt/ft, but the simulated data set had larger values outside these boundaries. Neglecting the vertical wind component did not significantly affect the statistics for these data sets. The frequency of occurrence of wind shears for the flight measured data was essentially the same for each altitude band and the total sample, but the simulated data distributions were different for each altitude band. The larger wind shears for the flight measured data were found to have short durations.

Dunham, R. E., Jr.↗