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At least 73 records · Page 4

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

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

17 WIND ENERGY↗

Quantifying the Impacts of Land Surface Modeling on Hub-Height Wind Speed under Different Soil Conditions

We investigate the impact of three land surface models (LSMs) on hub-height wind speed under three different soil regimes (dry, wet, and frozen) to understand and improve the physics of wind energy forecasts using the Weather Research and Forecasting (WRF) model. A six-day representative period is selected for each soil condition. The simulated wind speed, surface energy budget and soil properties are compared with the observations collected from the second Wind Forecast Improvement Project (WFIP2). For the selected cases, our simulation results suggest that, the impact of LSMs on hub-height wind speed are sensitive to the soil states but not so much to the choice of LSM. The simulated hub-height wind speed is in much better agreement with the observations for the dry soil case than the wet and frozen soil cases. Over the dry soil, there is a strong physical connection between the land surface and hub-height wind speed through near-surface turbulent mixing. Over the wet soil, the simulated hub-height wind speed is less impacted by land surface due to weaker surface fluxes and more dominated by large-scale synoptic disturbances. Over the frozen soil, the land surface model seems to have limited impact on hub-height wind speed variability due to the decoupling of the land surface with the overlying atmosphere. Two main sources of modeling uncertainties are proposed. The first are the insufficient model physics representing the surface energy budget, especially the ground heat flux, and the second are the inaccurate initial soil states such as soil temperature and soil moisture.

Xia, Geng↗

Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

We evaluate the sensitivity of simulated turbine-height winds to 26 parameters applied in a planetary boundary layer (PBL) scheme and a surface layer scheme of the Weather Research and Forecasting (WRF) model over an area of complex terrain during the Columbia Basin Wind Energy Study. An efficient sampling algorithm and a generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of modeled turbine-height winds. The results indicate that most of the variability in the ensemble simulations is contributed by parameters related to the dissipation of the turbulence kinetic energy (TKE), Prandtl number, turbulence length scales, surface roughness, and the von Kármán constant. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability. The parameter associated with the TKE dissipation rate is found to be the most important one, and a larger dissipation rate can produce larger hub-height winds. A larger Prandtl number results in weaker nighttime winds. Increasing surface roughness reduces the frequencies of both extremely weak and strong winds, implying a reduction in the variability of the wind speed. All of the above parameters can significantly affect the vertical profiles of wind speed, the altitude of the low-level jet and the magnitude of the wind shear strength. The wind direction is found to be modulated by the same subset of influential parameters. Remainder of abstract is in attachment.

54 ENVIRONMENTAL SCIENCES↗

Incorporation of the Rotor-Equivalent Wind Speed into the Weather Research and Forecasting Model’s Wind Farm Parameterization

Wind power installations have been increasing in recent years. Because wind turbines can influence local wind speeds, temperatures, and surface fluxes, weather forecasting models should consider their effects. Wind farm parameterizations do currently exist for numerical weather prediction models. They generally consider two turbine impacts: elevated drag in the region of the wind turbine rotor disk and increased turbulent kinetic energy production. The wind farm parameterization available in the Weather Research and Forecasting (WRF) Model calculates this drag and TKE as a function of hub-height wind speed. However, recent work has suggested that integrating momentum over the entire rotor disk [via a rotor-equivalent wind speed (REWS)] is more appropriate, especially for cases with high wind shear. In this study, we implement the REWS in the WRF wind farm parameterization and evaluate its impacts in an idealized environment, with varying amounts of wind speed shear and wind directional veer. Specifically, we evaluate three separate cases: neutral stability with low wind shear, high stability with high wind shear, and high stability with nonlinear wind shear. For most situations, use of the REWS with the wind farm parameterization has marginal impacts on model forecasts. However, for scenarios with highly nonlinear wind shear, the REWS can significantly affect results.

17 WIND ENERGY↗

US East Coast synthetic aperture radar wind atlas for offshore wind energy

We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from -0.8 to 0.6 m s -1 . Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to -0.2 to 0.0 m s -1 , while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s -1 . The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s -1 for SAR and less than 0.2m s -1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

17 WIND ENERGY↗

Wind Loading on Parabolic Trough Collectors: Wind and Structural Loads Measurements at an Operational Powerplant

Concentrated Solar Power (CSP) is a promising method for using solar power for electricity generation with thermal energy storage for industrial applications. Solar collectors constitute almost 1/3 of the total cost of the power plant. One of the primary drivers of unrealiability of these collectors is wind-driven loading of mirrors, support structures, and drives. To date, the design of the solar collector structures has relied on data from wind tunnels that do not adequately capture the dynamic effects observed at scale. NREL initiated a field measurement campaign at the operational Nevada Solar One (NSO) parabolic trough powerplant. At this plant, parabolic trough solar collectors track the sun from east to west in the course of a day and face varying wind loads depending on the wind properties and the angle of the troughs. The aim of the project is a detailed characterization of prevailing wind and turbulence conditions and resulting operational loads on parabolic troughs, providing insights into structural dynamic response, and generating a comprehensive wind-loading dataset for validating simulations of wind loading on collector structures. The measurements at NSO consist of sonic anemometers on masts at different heights to characterize the incoming flow and conditions at four trough rows at the edge of the trough field. In addition, a Doppler Lidar scans the horizontal plane above the troughs. The wind measurements at NSO have been continuously collecting data since October 2021 and are combined with structural load measurements that started in November 2022. The load measurements were installed on the same four outermost trough rows and include support structure bending moments, drive torque moments, dynamic accelerations of the spaceframe, mirror displacement, and tilt angles. Measurements are planned to continue until May 2023, providing a first-of-a-kind, high-resolution multi-month dataset of combined wind and load measurements. In this presentation, we show first results of the measurement campaign. Based on the measurements, we identified three main factors altering the flow over the parabolic troughs: Wind speed, wind direction, and the angle of the parabolic troughs. Most interactions between incoming wind and the trough field are observed when the wind blows perpendicular to the trough rows. In this case, the first rows experience the highest wind speed and block the downwind rows, creating conditions with decreased wind speed and enhanced turbulence within the trough field. Also, turbulent length scales are smaller after the first row. This leads to the highest static loads (bending and torque moments) at the first row but potentially increased dynamic loads within the field. We illustrate our findings with case studies focused on different wind conditions and will present fatigue analysis to highlight the impact of wind-driven loads on collector structures.

CSP collectors↗

The Second Wind Forecast Improvement Project (WFIP 2): General Overview

In 2015 the U.S. Department of Energy initiated a four-year study, the second Wind Forecast Improvement Project (WFIP 2), to improve the representation of boundary-layer physics and related processes in mesoscale models for better wind and wind power forecasts. This goal challenges numerical weather prediction models in complex terrain due to inherent assumptions underlying their boundary-layer parameterizations. The WFIP 2 effort involved the wind industry, universities, NOAA, and DOE's national laboratories in an integrated observational and modeling study. Observations spanned 18-months to assure a full annual cycle of continuously recorded observations from remote-sensing and in situ measurement systems. The study area comprised the Columbia Basin of eastern Washington and Oregon, containing more than 4 GW of installed wind capacity; nests of observational systems captured important atmospheric scales from mesoscale to NWP sub-grid scale. Model improvements targeted NOAA's High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to NWS operational forecast models. This paper describes the general WFIP 2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailed BAMS papers addressing WFIP 2 observational science, modeling challenges and solutions, practical transfer of boundary layer advances to industry use, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.

WFIP2, boundary layer, complex terrain, forecastin↗

Efficient Optimization of Large Wind Farms for Real-Time Control: Preprint

Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Properly coordinating turbines, by operating some turbines suboptimally, within a wind farm has the potential to improve overall wind farm performance. Computing the optimal control strategy under varying atmospheric conditions can be computationally intense for large wind farms. As wind power farms increase in size and related models become more complex, computationally efficient algorithms are needed to perform real-time optimization and control. This study proposes a distributed optimization framework and computationally efficient wake steering wind farm control strategy that uses the yaw angle of a turbine to alter the behavior of a turbine wake and minimize turbine interactions. This computational efficiency allows the strategy to be feasible for real-time control.

17 WIND ENERGY↗

Solar wind magnetic fluctuations and electron non-thermal temperature anisotropy: survey of Wind-SWE-VEIS observations

The solar wind electron velocity distribution function (eVDF) exhibits a variety of non-thermal features that deviate from thermal equilibrium. These deviations from equilibrium provide a local source for electromagnetic fluctuation emissions, including the commonly observed electron whistler-cyclotron and firehose instabilities. We present a systematic analysis of Wind-SWE-VEIS observations of solar wind electron plasma and associated Wind-MFI observed magnetic fluctuations. For the first time using the full solar wind electron distribution and its moments, without separation of the various electron components, we show clear evidence that the temperature anisotropy threshold of the parallel electron cyclotron anisotropic instability bounds solar wind electrons during slow solar wind periods. We also demonstrate that during periods of slow solar wind, collisions—while infrequent—are the dominant mechanism by which solar wind electrons are constrained, leading to isotropization. During fast solar wind periods, magnetic fluctuations and solar wind anisotropies are enhanced above the parallel whistler anisotropic threshold boundary and collisional effects are significantly reduced. Preliminary calculations further show that the oblique electron whistler mirror anisotropic instability bounds both the slow and fast solar wind. Regardless of speed, the solar wind electron thermal anisotropy appears globally bounded by the parallel electron firehose instability for anisotropies T{sub e⊥}/T{sub e||}<1. Our results indicate that collisions, while infrequent, play a necessary role in regulating the solar wind eVDFs. In striking contrast to solar wind ions, solar wind electron plasma, when considered globally as a single eVDF, is only marginally stable with respect to parallel propagating instabilities.

79 ASTRONOMY AND ASTROPHYSICS↗

Transition Marshall Space Flight Center Wind Profiler Splicing Algorithm to Launch Services Program Upper Winds Tool

NASAs LSP customers and the future SLS program rely on observations of upper-level winds for steering, loads, and trajectory calculations for the launch vehicles flight. On the day of launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds and provide forecasts to the launch team via the AMU-developed LSP Upper Winds tool for launches at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station. This tool displays wind speed and direction profiles from rawinsondes released during launch operations, the 45th Space Wing 915-MHz Doppler Radar Wind Profilers (DRWPs) and KSC 50-MHz DRWP, and output from numerical weather prediction models.The goal of this task was to splice the wind speed and direction profiles from the 45th Space Wing (45 SW) 915-MHz Doppler radar Wind Profilers (DRWPs) and KSC 50-MHz DRWP at altitudes where the wind profiles overlap to create a smooth profile. In the first version of the LSP Upper Winds tool, the top of the 915-MHz DRWP wind profile and the bottom of the 50-MHz DRWP were not spliced, sometimes creating a discontinuity in the profile. The Marshall Space Flight Center (MSFC) Natural Environments Branch (NE) created algorithms to splice the wind profiles from the two sensors to generate an archive of vertically complete wind profiles for the SLS program. The AMU worked with MSFC NE personnel to implement these algorithms in the LSP Upper Winds tool to provide a continuous spliced wind profile.The AMU transitioned the MSFC NE algorithms to interpolate and fill data gaps in the data, implement a Gaussian weighting function to produce 50-m altitude intervals in each sensor, and splice the data together from both DRWPs. They did so by porting the MSFC NE code written with MATLAB software into Microsoft Excel Visual Basic for Applications (VBA). After testing the new algorithms in stand-alone VBA modules, the AMU replaced the existing VBA code in the LSP Upper Winds tool with the new algorithms. They then tested the code in the LSP Upper Winds tool with archived data. The tool will be delivered to the 45 WS after the 50-MHz DRWP upgrade is complete and the tool is tested with real-time data. The 50-MHz DRWP upgrade is expected to be finished in October 2014.

Space launch↗

Dynamic Wind Loading on CSP Collectors Caused by Turbulent Wind Fluctuations: Insights from a 2-Year Field Campaign

Concentrating Solar Power (CSP) is a promising solar technology for electricity generation with thermal energy storage and with the additional benefit of industrial heat production. Wind loading on CSP collector structures, such as parabolic troughs or heliostats, is one of the primary drivers of their structural design costs. In particular, dynamic wind loading is a major source of uncertainty in the collector design process, which heavily relies on wind tunnel testing. In the field, the turbulent nature of the incoming wind creates fluctuating loads (support structure loads and resulting mirror deflections) on the collectors, with impacts on fatigue lifetime and optical performance. As is well known, wind tunnel tests cannot entirely reproduce the complex turbulent wind conditions typically observed at full-scale plants. To shed light on this topic, NREL initiated a field campaign at the operational Nevada Solar One (NSO) powerplant that uses parabolic troughs as solar collectors. The aim of the project is a detailed characterization of prevailing wind and turbulence conditions and resulting operational loads on parabolic troughs. We use the published 2-year dataset of high-resolution combined wind and structural loads measurements [1] to characterize the dynamic structural wind response. For quantifying dynamic wind loading, we apply the concept of admittance functions, which are spectral transfer functions that couple the turbulent wind to resulting structural loads (aerodynamic admittance), and to the structural response (mechanical admittance). In practice, aerodynamic admittance describes which turbulent eddy sizes are effective in creating structural loads. The mechanical admittance describes in which frequency ranges these loads are reinforced or dampened by the structure. While these functions are an established concept in civil engineering, their recent application to a single full-scale heliostat [2] proved their broader applicability to CSP collectors. Here, we present a characterization of admittance functions for full-scale parabolic trough collectors and show how wind characteristics (mean wind speed and direction, turbulent kinetic energy, turbulent length scales), the sun-tracking trough angle, and row position alter the admittance functions. Further, we study to which extent the admittance functions are universal for a specific trough geometry and how our findings compare to reported heliostat results. References [1] https://data.openei.org/submissions/5938. [2] Blume, K., Roger, M., and Pitz-Paal, R. 2023b. "Simplified analytical model to describe wind loads and wind-induced tracking deviations of heliostats." Solar Energy, 256, 96-109. https://doi.org/10.1016/j.solener.2023.03.055.

admittance functions↗

Investigations of vertical wind variations at a mountain top in the Himalaya using Doppler Lidar observations and model simulations

Hight-resolved observations of vertical winds remain nearly non-existing over the Himalayas, despite of an­ticipated crucial role of vertical motions in transporting pollution across the Himalayan hms. The present study analyze the vertical wind observations from surface to 1 km above ground level over Manora Peak (29.4° N; 79.5° E; 1958 m ams!) in the Himalaya performed using a Doppler Lidar during the Ganges Valley Aerosol Experiment (GVAX). Vertical wind exhibited a pronounced diurnal variability at Manora Peak comprising of upward motions during the daytime (05-10 UT) and downward motions during nighttime typical of a mountain­valley system. Mean vertical wind speeds are observed to be varying from -0.8 to +0.8 ms- 1 during the study period with a variance of 0.1-1.5 m2s·2, which is attributed to the thermally driven turbulence. Mean vertical winds are observed to be stronger in the Doppler Lidar profiles above Manora Peak (-0_8 to 0.8 ms- 1) as compared to near surface measurements at this station using an ultrasonic anemometer ( -0.4 to 0.4 ms - 1 ), and low altitude stations in India. Daytime vertical wind speeds are observed to be higher during pre-monsoon (0.81 ms- 1), as compared to post-monsoon (0.24 ms- 1) and winter (0.33 ms- 1)_ Average Black Carbon (BC) concentrations are significantly higher during strong upward vertical winds, which indicates efficient transport of polluted air mass from low-altitude regions to the Himalaya. Weather Research and Forecasting (WRF) model reproduces the observed diurnal pattern in the vertical wind at the observation site however the model un­derestimates the variability.

Shukla, K K.↗

Critical wind speeds suggest wind could be an important disturbance agent in Amazonian forests

Abstract Recent research in the central Amazon suggests that wind is a major agent of disturbance, however, a mechanistic understanding of how wind may lead to tree mortality in Amazonian forests remains unclear. Here we estimated wind speeds necessary to topple central Amazon trees by linking both static and dynamic versions of two wind speed estimation methods (four methods total) to field data on tree failure derived from a static winching study. Static versions of these methods assumed invariant wind characteristics as more trees failed, while dynamic versions updated tree spacing, leaf area index and wind profiles progressively after each tree failure. First, we used a profile method which estimates wind force on individual trees by segments. We calculated drag on each segment and converted drag into basal turning moment, and compared the summed turning moments to the critical turning moment measured in the winching study. Estimated critical wind speeds from the static profile method varied greatly, from 10.75 m s−1 to >120.0 m s−1 with a mean of 45.70 m s−1. Critical wind speeds estimated with static approaches decreased with tree size but were not significantly different between two focal genera. Primary drivers of variation in critical wind speed were tree height and crown size. Second, we used the turning moment coefficient method of Hale, S.E., Gardiner, B., Peace, A., Nicoll, B., Taylor, P. and Pizzirani, S. 2015 Comparison and validation of three versions of a forest wind risk model. Environ. Model. Softw.68, 27–41. doi:10.1016/j.envsoft.2015.01.016.; the static version of this method yielded less-variable estimates, ranging from 18.98 to 52.01 m s−1, with a mean of 30.88 m s−1. Notably, the two static methods for estimating critical wind speeds differed in the trees they identified as having the highest and lowest critical wind speeds. Dynamic variants of the above two methods produced greatly reduced ranges in CWS estimates for our study trees, because after the early tree failures, remaining trees were subject to greater wind penetration into the stand and thus greater loading for a given above-canopy wind speed. CWS estimated with dynamic approaches differed significantly between the focal taxa. Nevertheless, both estimates suggest that wind speeds commonly observed during Amazon storms are sufficient to produce widespread tree damage and mortality.

Peterson, Chris J.↗

Vector wind and vector wind shear models 0 to 27 km altitude for Cape Kennedy, Florida, and Vandenberg AFB, California

The techniques are presented to derive several statistical wind models. The techniques are from the properties of the multivariate normal probability function. Assuming that the winds can be considered as bivariate normally distributed, then (1) the wind components and conditional wind components are univariate normally distributed, (2) the wind speed is Rayleigh distributed, (3) the conditional distribution of wind speed given a wind direction is Rayleigh distributed, and (4) the frequency of wind direction can be derived. All of these distributions are derived from the 5-sample parameter of wind for the bivariate normal distribution. By further assuming that the winds at two altitudes are quadravariate normally distributed, then the vector wind shear is bivariate normally distributed and the modulus of the vector wind shear is Rayleigh distributed. The conditional probability of wind component shears given a wind component is normally distributed. Examples of these and other properties of the multivariate normal probability distribution function as applied to Cape Kennedy, Florida, and Vandenberg AFB, California, wind data samples are given. A technique to develop a synthetic vector wind profile model of interest to aerospace vehicle applications is presented.

Smith, O. E.↗

SSMI Wind Speed Climatology of the Time of Monsoon Wind Offset in the Western Arabian Sea

Forecasting the time of onset of monsoon wind in the western Arabian Sea, which is believed to precede the onset of rainfall along the west coast of India, is an important unsolved problem. Prior to measurements of the surface wind field by satellite, there was an absence of suitable surface wind observations. NASA scatterometer (NSCAT) surface wind vectors revealed that the time of the 1997 onset of 12 m/s southwest monsoon wind speeds in the western Arabian Sea preceded the onset of monsoon rainfall in Goa, India, by 3 - 4 days. Wind speed and direction data were necessary to establish a dynamical mechanism between times of onset of 12 m/s wind speed off Somalia and rainfall in Goa. Except for NSCAT, no satellite scatterometer wind product recorded adequately sampled 2-day 1deg x 1deg averaged wind vectors, which are the required space and time scales, to examine the wind-rain relationship in other years. However, the greater-than-95% steadiness of summer monsoon winds allows an opportunity to use satellite measurements of surface wind speed. The Special Sensor Microwave Imager (SSMI) recorded surface wind speed with adequate sampling to produce a 1-day, 1deg x 1deg data product during 1988 - 1998. SSMI data had been uniformly processed throughout the period. Times of onset of 12 m/s wind speed off Somalia determined with the SSMI data set were 21 May 1988, 24 May 1989, 17 May 1990, 28 May 1991, 8 June 1992, 28 May 1993, 30 May 1994, 7 June 1995, 29 May 1996, 12 June 1997, and 15 May 1998. Uncertainty of the 1992 and 1996 times of onset were increased because of the absence of SSMI data on 6 and 7 June 1992 and on 30 May 1996. Correlations of timing of monsoon wind onset with El Nino will be described. Variability of the time difference between times of onset of 12 m/s wind speed and Goa rainfall will be discussed. At the time of submission of the abstract, the Goa rainfall data have not arrived from the India Meteorological Department.

Halpern, David↗

Techno-economic analysis of offshore wind PEM water electrolysis for H 2 production

A model for producing hydrogen via offshore wind electrolysis was developed and the levelized costs of both energy and hydrogen were calculated. Here, this model calculated the cost of hydrogen produced by offshore wind and showed that the levelized cost of energy for hydrogen production and transportation to shore could be lower than for electricity transmission from offshore wind farms, using real wind data from a particular location. Therefore, direct coupling of the electrolysis system with offshore wind turbine is more advantageous than transmitting electricity to shore and then producing hydrogen via traditional electrolysis; the cost of hydrogen from offshore wind electrolysis is estimated to be $\$2.09$/kg, vs $\$3.86$/kg from traditional electrolysis using wind power.

08 HYDROGEN↗

On the Low Risk of SSR in Type III Wind Turbines Operating in Grid-Forming Control: Preprint

We have shown in a previous work that the risk of sub-synchronous resonance (SSR) between a wind power plant with Type III wind turbines and series-compensated transmission lines is low when the wind turbines in the plant are operated in grid-forming mode, instead of the standard grid-following mode. The fundamental mechanism behind the improved damping characteristics is explained in this paper by modeling the positive and negative sequence impedances of Type III wind turbines for GFM operation mode. It is discovered that the GFM control naturally acts against the negative resistance of Type III wind turbines that results from an interaction between the proportional gain of the rotor-side converter current controller and negative slip. The developed sequence impedance models and improved damping behavior are verified using PSCAD simulations of a 2.5-MW Type III GFM wind turbine. The modeling predictions are also supported by experimentally measuring the sequence impedance response of a 2.5-MW Type III wind turbine during operation in GFL and GFM modes.

grid-following turbine↗