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Wind Integration National Dataset Toolkit The Wind Integration National Dataset (WIND) Toolkit is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies.
The wind power plant-wide control strategy known as wake steering involves the misalignment of upstream turbines with the wind direction to deflect wakes away from downstream turbines, increasing net wind plant power production. In this paper, we evaluate the potential of wake steering for U.S. land-based wind power plants. First, we outline a method for simulating and optimizing wake steering control for existing wind plants by combining the flow redirection and induction in steady state wake steering engineering model with the U.S. Wind Turbine Database and Wind Integration National Dataset Toolkit wind resource dataset. Next, to better understand the potential benefits of wake steering beyond those for existing wind plants, we evaluate the relative impacts of turbine specific power, turbine spacing, and mean wind speed on energy gain and levelized cost of energy (LCOE) using a model land-based wind power plant. For a subset of 60 existing wind plants, assuming a constant turbulence intensity of 8%, wake steering was found to yield an average annual energy production (AEP) gain of 0.80%, equivalent to recovering 13.85% of baseline wake losses. Further, we present a linear approximation between baseline wake losses and AEP gains that can be used to estimate wake steering gains for other wind power plants. Highlighting additional benefits of wake steering, for the model wind power plant we found that energy gains from wake steering enabled an approximate 30% reduction in turbine spacing while keeping LCOE constant.
Much like their counterparts in utility-scale wind energy, developers of industrial, small-scale and distributed wind turbine deployments need to understand and accurately characterize the wind resource to properly assess the power generation and financial ramifications during siting and planning. National Renewable Energy Laboratory’s WIND (Wind Integration National Dataset) Toolkit (WTK) provides a best-in-class wind resource dataset generated using the Weather Research and Forecasting (WRF) model. This dataset includes parameters such as the wind speed, wind direction, and temperature at various heights, plus atmospheric stability near the surface. This data is available at 2-km spatial resolution and five-minute temporal resolution across 7 years, from 2007 to 2013 through a publicly accessible API interface. The Tools Assessing Performance (TAP) project seeks to extend this dataset to allow long term resource estimates and leverage it to better equip distributed wind equipment manufacturers, owner-operators, and installation professionals with better tools for practical siting applications. In this report, we present the results from our investigation within the TAP project focused on characterization of bias in WTK-based wind speed estimates and evaluation of vertical and horizontal interpolation techniques. We discuss the tradeoffs between different techniques and their combinations, as well as describe the lower bounds we determine for the studied validation errors. While the specific estimates we present are specific to WTK and the validation dataset we have chosen for this investigation (NREL's Wind Resource Meteorological Database), the overall analysis and the studied techniques are general enough to be applied to a broader set of wind datasets, both simulation-based and observational.
This research has led to the development of the 2023 National Offshore Wind data set (NOW-23), which offers the latest wind resource information for offshore regions in the United States. NOW-23 supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published a decade ago and is currently a primary resource for wind resource assessments and grid integration studies in the contiguous United States. By incorporating advancements in the Weather Research and Forecasting (WRF) model, NOW-23 delivers an updated and cutting-edge product to stakeholders. As part of this project, we also developed a summary of the uncertainty quantification in NOW-23, along with NOW-WAKES, a 1-year post-construction data set that quantifies expected offshore wake effects in the US Mid-Atlantic lease areas. Stakeholders can access the NOW-23 data set at https://doi.org/10.25984/1821404.
Abstract. This article introduces the 2023 National Offshore Wind data set (NOW-23), which offers the latest wind resource information for offshore regions in the United States. NOW-23 supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published a decade ago and is currently a primary resource for wind resource assessments and grid integration studies in the contiguous United States. By incorporating advancements in the Weather Research and Forecasting (WRF) model, NOW-23 delivers an updated and cutting-edge product to stakeholders. In this article, we present the new data set which underwent regional tuning and performance validation against available observations and has data available from 2000 through, depending on the region, 2019–2022. We also provide a summary of the uncertainty quantification in NOW-23, along with NOW-WAKES, a 1-year post-construction data set that quantifies expected offshore wake effects in the US Mid-Atlantic lease areas. Stakeholders can access the NOW-23 data set at https://doi.org/10.25984/1821404 (Bodini et al., 2020).
The United States is accelerating its shift toward a renewable energy system. However, renewable resources, which harness energy from the Earth system, are susceptible to both present-day climate variability and future climate change. For example, variations in regional climate can alter renewable energy production patterns and site viability. The use of high-resolution climate model projections can therefore facilitate and may be critical to long-term planning of renewable energy investments. However, climate models must first be validated for renewable resource assessment. This research employs multiple high-spatiotemporal-resolution datasets to assess the capability of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 North American Regionally Refined Model (E3SMv2-NARRM) for predicting multi-year climatological values of solar and wind energy capacity factors in the continental U.S., with a focus on regional and seasonal variability. Present-day E3SMv2-NARRM simulations are compared with reported utility-scale production data obtained from the Energy Information Administration (EIA). In addition, E3SMv2-NARRM data are evaluated against non-climate benchmark models from the National Renewable Energy Laboratory, including the Wind Integration National Dataset Toolkit and the National Solar Radiation Database (NSRDB), as well as three wind energy datasets from PLUSWIND. Our analysis indicates that solar capacity factors from E3SM closely match those from the NSRDB dataset. However, both datasets tend to overestimate values by 10% in comparison to EIA data. Furthermore, biases in wind capacity factors within E3SM are notably pronounced in the West Coast regions, where the seasonal cycle diverges from EIA data.
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.
With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).
Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.
This report presents a state-of-the-art wind resource data set produced by the National Renewable Energy Laboratory (NREL) for the California Pacific Outer Continental Shelf (OCS). This data set replaces NREL's Wind Integration National Dataset (WIND) Toolkit for the California OCS, which was produced and released publicly in 2013 and is currently the principal data set used by stakeholders for wind resource assessment in the continental United States.
In this report, we present the latest wind resource data specifically tailored for offshore regions in the United States. The data set, known as the 2023 National Offshore Wind data set (NOW-23), has been developed by the National Renewable Energy Laboratory (NREL) and its partners, and surpasses the previous resource data set, the Wind Integration National Dataset (WIND) Toolkit, which was released approximately ten years ago, in its offshore component. The WIND Toolkit has been widely utilized by stakeholders involved in wind resource assessments across the continental United States. However, with significant advancements in numerical weather prediction modeling over the past decade, the NOW-23 data set incorporates the latest research and development progress to provide stakeholders with an updated and cutting-edge resource for offshore wind analysis.
In this report, we present the latest wind resource data specifically tailored for offshore regions in the United States. The data set, known as the 2023 National Offshore Wind data set (NOW-23), has been developed by the National Renewable Energy Laboratory (NREL) and its partners, and surpasses the previous resource data set, the Wind Integration National Dataset (WIND) Toolkit, which was released approximately ten years ago, in its offshore component. The WIND Toolkit has been widely utilized by stakeholders involved in wind resource assessments across the continental United States. However, with significant advancements in numerical weather prediction modeling over the past decade, the NOW-23 data set incorporates the latest research and development progress to provide stakeholders with an updated and cutting-edge resource for offshore wind analysis. The NOW-23 data set is created using the Weather Research and Forecasting (WRF) model and its output is available, as for its predecessor, at 5-minute time resolution and 2-kilometer horizontal spatial resolution. However, the NOW-23 data set improves upon the WIND Toolkit through: 1. A modeling period of at least 20 years (and as long as 23 years in selected regions), starting in 2000 (compared to the 7-year 2007–2013 modeling period in the WIND Toolkit). 2. For several offshore regions, a region-specific sensitivity analysis, driven by an ensemble of WRF simulations, to assess the most adequate region-specific WRF setup. 3. An updated WRF model, from Version 3.4 used in the WIND Toolkit to Version 4.2.1 used for the NOW-23 data set, which incorporates significant research advancements. 4. The use of the state-of-the-art reanalysis product ERA5 (which supersedes the older ERA-Interim used in the WIND Toolkit) to provide atmospheric forcing at the WRF domain boundaries. Figure 1 shows the NOW-23 mean wind speed at 160 m above sea level (asl), which we use as proxy for hub-height of a commercial offshore wind turbine in this report across all regions.
Offshore wind resource characterization in the United States relies heavily on simulated winds from numerical weather prediction (NWP) models, given the lack of hub-height observations offshore. One such NWP data set used extensively by U.S. stakeholders is the Wind Integration National Dataset (WIND) Toolkit, a 7-year time-series data set produced in 2013 by the National Renewable Energy Laboratory. In this study, we present an update to that data set for offshore California that leverages recent advancements in NWP modeling capabilities and extends the period of record to a full 20 years. The data set predicts a significantly larger wind resource (0.25–1.75 m s-1 stronger), including in three Call Areas that the Bureau of Ocean Energy Management is considering for commercial activity. We conduct a set of yearlong simulations to study factors that contribute to this increase in the modeled wind resource. The largest impact arises from a change in the planetary boundary layer parameterization from the Yonsei University scheme to the Mellor-Yamada-Nakanishi-Niino scheme and their diverging wind profiles under stable stratification. Additionally, we conduct a refined wind resource assessment at the three Call Areas, characterizing distributions of wind speed, shear, veer, stability, frequency of wind droughts, and power production. We find that, depending on the attribute, the new data set can show substantial disagreement with the WIND Toolkit, thereby driving important changes in predicted power.
The atmospheric dynamics that occur near the intersection of land and water offer exciting and challenging opportunities for wind energy deployment in coastal locations. New models and tools are continually being developed in support of wind resource assessment, and three recent products are explored in this work for their performance in representing characteristics of the wind resource at coastal locations: the Global Wind Atlas 3 (GWA3), the 2023 National Offshore Wind dataset (NOW-23), and the wind climate simulations that are a component of the Wind Integration National Dataset (WIND) Toolkit Long-Term Ensemble Dataset (WTK-LED Climate). These relatively new products are freely available and user-friendly so that anyone – from a utility-scale developer to a resident or business owner – can evaluate the potential for wind energy generation at their location of interest. The validations in this work provide guidance on the accuracy of wind resource assessments for coastal customers interested in installing small or midsize wind turbines (≤ 1 MW in capacity) to support energy needs at the residential, business, or community scale, such as the island and remotely located participants of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. At 23 coastal locations across the United States, dataset performance varies according to different evaluation metrics. All three recent datasets tend to overestimate the observed coastal wind resource. GWA3 produces the smallest annual average wind speed relative errors, whereas WTK-LED Climate is in best agreement in terms of representing diurnal wind speed cycles. NOW-23 is the highest performing of the datasets for representing seasonal and interannual trends in the coastal wind resource. While GWA3 and WTK-LED Climate are relatively insensitive to the dataset output heights selected for wind resource assessment at small and midsize wind turbine hub heights (20–60 m), significant variation in the NOW-23 representation of wind shear across the wind profile in the lowest 100 m of the atmosphere leads to notable differences in wind speed estimates according to the dataset output heights selected for evaluation. GWA3 exhibits challenges in the representation of observed wind speed diurnal cycles at small and midsize turbine hub heights, likely due to the dataset's consistent treatment of hourly wind speed trends regardless of altitude.
Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) Model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at a site in Oklahoma, United States, and in a United States Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~-1ms-1) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a limited negative bias (~-0.5ms-1) offshore and a slight positive bias (~+0.5ms-1) at the land-based site. On the other hand, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED's higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle. At the land-based site, this is partially caused by wind plant wake effects not being accurately captured by WTK-LED.
Abstract. Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, MyWindTurbine.com, and Global Wind Atlas 3 (GWA3), which are seen to overestimate actual annual capacity factors by 2.5, 4.2, 11.5, and 7.3 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produces more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.
Many weather years of high-quality wind data are widely accepted in the grid integration community to be important for studying wind energy technical potential, energy system operations, and grid resilience. NREL makes high-quality wind and solar resource data available. NREL's Grid-Atmosphere workshop (March 2024) identified NREL National Solar Radiation Database as widely used in grid integration modeling, but there is less agreement on commonly used wind datasets. One important factor identified by ESIG's 2023 report 'Weather Dataset Needs for Planning and Analyzing Modern Power Systems' for gold standard wind data is regular updates. To address the need for regular updates, NREL's team can now process all currently available and regularly updated High-Resolution Rapid Refresh (HRRR) outputs. HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). One barrier to NREL using HRRR is systematic bias and consistency with NREL's existing wind datasets (e.g. WIND Toolkit, 'WTK') across weather years. To address this barrier, we show that the HRRR can be interpolated and bias-corrected to be consistent with NRE's existing datasets. We call the new dataset BC-HRRR (bias-corrected HRRR). As with historical datasets like the WTK, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling). BC-HRRR's (2015-present) consistency with WTK (2007-2013) allows NREL to extend internal grid integration tooling with 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. The rest of this slide deck documents the BC-HRRR processing methods, validation, and its implications for intended use.
To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023. Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024). BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit.