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The U.S. Department of Energy's Wind Powering America initiative provides high-resolution wind maps and estimates of the wind resource potential that would be possible from development of the available windy land areas after excluding areas unlikely to be developed. This postcard is a marketing piece that stakeholders can provide to interested parties; it will guide them to Wind Powering America's online wind energy resource maps.
This final report describes the comprehensive findings on a project to validate and improve atmospheric models for offshore wind resource mapping and prediction.
USAID Bangladesh and the U.S. Department of Energy’s National Renewable Energy Laboratory partnered with the Government of Bangladesh to develop a national wind resource assessment. The assessment used sophisticated resource modeling that was validated by a ground measurement campaign. Results from the project included a long-term, correlated wind data set; validated high-resolution wind resource maps; and publicly available data accessible through the RE Data Explorer (https://www.re-explorer.org/). The Renewable Energy (RE) Data Explorer allows users to access and download the Bangladesh wind resource assessment data and related geographic information system (GIS) data sets and perform customized technical potential analyses. Figure ES-1 is an example wind resource map for Bangladesh that can be created with the RE Data Explorer. This project improved upon existing global data sets by using best-in-class modeling techniques and analysis. Global models are good first steps in predicting wind resources in various parts of the world, but they do not generate the accuracy needed to reduce project risk and stimulate renewable investment. This project improved the quality of modeled wind resource data for Bangladesh. In addition to the modeling effort, the project team used a multi-year, local data-collection campaign to validate the model and further improve the accuracy of the data sets. The measurement campaign consisted of nine meteorological sites representing all geographical regions of the country. Seven meteorological towers and one remote-sensing, sonic detection and ranging unit (deployed at two sites) collected the data. Site-selection criteria focused on geographic diversity and proximity to existing transmission lines. Figure ES-1 shows measurement locations. The measurement campaign spanned June 2014 through December 2017. The results of the Bangladesh wind resource assessment will help Bangladesh overcome significant energy challenges. Its energy sector suffers from power shortages, increasing demand, decreasing domestic natural gas reserves, and inadequate transmission infrastructure. As part of a comprehensive plan to overcome these challenges, Bangladesh has committed that 10% of the total generation capacity will be renewable energy by 2021 (Power Division 2016). High-quality renewable energy resource data and other GIS data, such as those developed in this assessment, are important if Bangladesh wishes to reach its 10% renewable energy capacity target. These data support informed decision making, ranging from policy and investment decisions to reliable power sector planning. Specifically, the Bangladesh wind resource assessment will help reduce technical risk and encourage private-sector interest in the nascent wind market in Bangladesh. In addition, the Government of Bangladesh may use the results of the wind resource assessment to develop well-designed policies that could encourage investment in wind energy. This report provides a comprehensive description of the Bangladesh wind resource assessment, including details on the modeling approach and methods, instrumentation, data quality-control techniques, and resulting data sets.
Researchers at the National Renewable Energy Laboratory's (NREL's) National Wind Technology Center (NWTC) provide the wind industry, policymakers, and other stakeholders with applied wind resource data, information, maps, and technical assistance. These tools, which emphasize wind resources at ever-increasing heights, help stakeholders evaluate the wind resource and development potential for a specific area.
Recent advancements in utility-scale wind turbine technology and pricing have vastly increased the potential land area where turbines can be deployed in the United States. This presentation quantifies the new developable land potential (e.g., capacity curves), visually identifies new areas for possible development (e.g., new wind resource maps), and begins to address deployment barriers to wind in new areas for modern and future turbine technology.
Recent advancements in utility-scale wind turbine technology and pricing have vastly increased the potential land area where turbines can be deployed in the United States. This presentation quantifies the new developable land potential (e.g., capacity curves), visually identifies new areas for possible development (e.g., new wind resource maps), and begins to address deployment barriers to wind in new areas for modern and future turbine technology.
For nearly 30 years, NREL has helped the wind turbine industry through design and research innovations. The comprehensive capabilities of the National Wind Technology Center (NWTC), ranging from specialized computer simulation tools to unique test facilities, has been used to design, develop, and deploy several generations of advanced wind energy technology.
This presentation provides an overview of wind energy research being conducted at the National Wind Technology Center, market and technology trends in wind energy, and opportunities for wind technology.
The National Renewable Energy Laboratory (NREL) recently released new estimates of the U.S. potential for wind-generated electricity, using advanced wind mapping and validation techniques to triple previous estimates of the size of the nation's wind resources. The new study, conducted by NREL and AWS TruePower, finds that the contiguous 48 states have the potential to generate up to 37 milliongigawatt-hours annually. In comparison, the total U.S. electricity generation from all sources was roughly 4 million gigawatt-hours in 2009.
This presentation describes the current standards for conducting a national-scale wind resource assessment for power generation, along with the risk/benefit considerations to be considered when beginning a wind resource assessment. The presentation describes changes in turbine technology and viable wind deployment due to more modern turbine technology and taller towers and shows how the Philippines national wind resource assessment evolved over time to reflect changes that arise from updated technologies and taller towers.
Uncovering drivers of risk is crucial to understanding interactions between wildlife and wind turbines, and identifying options for impact minimization. These drivers tie to co-variates linked to behavior and movement patterns that allow us to estimate locations and periods of risk. For volant species, atmospheric flow can have significant influence on flight patterns. For obligate soaring birds, like golden eagles, updraft velocities can inform where eagles are likely to travel, at what altitude, and where conditions are not likely sufficient to sustain soaring flight. This has been an active area of study in recent years, using relatively coarse atmospheric data generally at the 20km x 20km scale or larger. Leveraging a 20-year dataset from the Weather Research and Forecasting Model (WRF) (https://www.mmm.ucar.edu/weather-research-and-forecasting-model), we are quantifying vertical velocities across the continental United States at a 2km x 2km resolution. Wind resource data sets originally were static maps showing the mean annual wind speed over an area. However, for these data sets to be optimally used for various applications they must be high-resolution time series, seamlessly span large geographic contexts, and account for uncertainty in wind speed. The National Renewable Energy Laboratory is producing public available datasets that meet these criteria and will be bias corrected to yield the most accurate wind resource data. This effort is an augment to the current WIND Toolkit which houses a high resolution data set. In the new iteration of the WIND Toolkit, a 20-year dataset will be used to improve the accuracy and estimate uncertainty using ensemble and machine-learning techniques. The resulting product will be the most accurate dataset of its size and at a 2km x 2km spatial and 5-minute temporal resolution. Through this work, a mesoscale vertical velocity layer will be produced by calculating the likelihood of orographic updraft and thermal updraft conditions across the continental United States. Specifically, we will use WRF model output combined with digital elevation maps to predict updrafts and then determine if vertical velocities are sufficient to support Golden Eagle soaring and gliding. Ultimately this data layer will be made available as a GIS layer in the Wind Prospector (maps.nrel.gov/wind-prospector/) tool or a similar framework. Data that will be incorporated include wind speed, direction temperature, relative humidity, barometric pressure, air density, precipitation rate, solar radiation, atmospheric stability, skin temperature, and upward heat flux. These products will advance research on interactions between volant species and wind energy by providing open access to highly resolved data with uncertainty quantification not previously available at this scale.
The Cadmus Group, Inc., in collaboration with the National Renewable Energy Laboratory (NREL) and Encraft, was awarded a grant from the Department of Energy (DOE) to develop a site analysis tool for distributed wind technologies. As the principal investigator for this project, Mr. Shawn Shaw was responsible for overall project management, direction, and technical approach. The product resulting from this project is the Distributed Wind Site Analysis Tool (DSAT), a software tool for analyzing proposed sites for distributed wind technology (DWT) systems. This user-friendly tool supports the long-term growth and stability of the DWT market by providing reliable, realistic estimates of site and system energy output and feasibility. DSAT-which is accessible online and requires no purchase or download of software-is available in two account types; Standard: This free account allows the user to analyze a limited number of sites and to produce a system performance report for each; and Professional: For a small annual fee users can analyze an unlimited number of sites, produce system performance reports, and generate other customizable reports containing key information such as visual influence and wind resources. The tool’s interactive maps allow users to create site models that incorporate the obstructions and terrain types present. Users can generate site reports immediately after entering the requisite site information. Ideally, this tool also educates users regarding good site selection and effective evaluation practices.
This EC-LEDS fact sheet describes the NREL Geospatial Toolkit (GsT), an open-source, map-based software application that provide an intuitive, user-friendly interface for visualizing data and renewable energy potential. The GsT is a country-specific tool that maps renewable energy resources (e.g., for solar, wind, and biomass) in relation to enabling infrastructure like roads and transmission lines, providing necessary information for deploying new clean energy generation.
There has been an increasing need for forecasting power generation at the sub-seasonal to seasonal (S2S) timescales to support the operation, management, and planning of the wind-energy system. At the S2S timescales, atmospheric variability is largely related to recurrent and persistent weather patterns, referred to as weather regimes (WRs). In the study "How do the weather regimes drive wind speed and power production at the sub-seasonal to seasonal timescales over the CONUS?", we identify four WRs that influence wind resources over North America using a self-organizing map (SOM) algorithm. These WRs are responsible for large-scale wind and power production anomalies over the CONUS at the S2S timescales. The WR-based reconstruction explains up to 50% of the monthly variance of power production over the western United States, and the explanatory power generally increases with the increase of timescales. The identified relationship between WRs and power production reveals the potential and limitations of the regional WR-based wind resource assessment over different regions of the CONUS across multiple timescales. This repository includes all the data and codes we use for analyses in this study. Users may use them to reproduce the results of this study on their end.
Agua Caliente Band of Cahuilla Indians (ACBCI) was awarded a grant by the Department of Energy (DOE) to study the feasibility of a wind and/or solar renewable energy project at the Whitewater Ranch (WWR) property of ACBCI. Red Mountain Energy Partners (RMEP) was engaged to conduct the study. The ACBCI tribal lands in the Coachella Valley have very rich renewable energy resources. The tribe has undertaken several studies to more fully understand the options available to them if they were to move forward with one or more renewable energy projects. With respect to the resources, the WWR property clearly has excellent wind and solar resources. The DOE National Renewable Energy Laboratory (NREL) has continued to upgrade and refine their library of resource maps. The newer, more precise maps quantify the resources as among the best in the world. The wind and solar technology available for deployment is also being improved. Both are reducing their costs to the point of being at or below the costs of fossil fuels. Technologies for energy storage and microgrids are also improving quickly and present additional ways to increase the wind and/or solar energy retained for later use with the network management flexibility to provide power to the appropriate locations when needed. As a result, renewable resources continue to gain more market share. The transitioning to renewables as the major resources for power will take some time as the conversion is complex and can have negative impacts if not managed well. While the economics for wind and solar systems continue to improve, the robustness of the WWR site was validated by the repeated queries of developers to place wind and/or solar there. The robust resources and improving technologies portends toward WWR land as a renewable energy site. The business case, however, is not so clear, especially when the potential investment portfolio for ACBCI has several very beneficial and profitable alternatives.
Predictability of wind resource conditions is critical for offshore wind design and operations. While many studies of extreme wind conditions focus on specific events such as low-level jets or ramps, these rely on threshold definitions that limit generality. Here we present a data-driven framework that combines principal component analysis (PCA), self-organizing maps (SOM), and k-means clustering to classify wind resource conditions as typical and anomalous from climatological data. Anomalies are defined not by fixed thresholds but by flagging samples located far from SOM node centers inside the baseline SOM structure. This reframes extremes as rare ebents and hence, likely difficult to anticipate by numerical weather prediction models. We applied this approach to 23 years (2000–2022) of hourly profiles from the NOW-23 hindcast model at the Humboldt Wind Energy Area. Classification is conducted on a feature space consisting of 10 m wind speed and direction, bulk shear and veer across 30–270 m, and a low-level jet index. Dimensionality reduction is achieved through PC. A 2 × 3 OM lattice trained on the PCA vectors identified six baseline regimes spanning weak to strong flow states. High quantization-error profiles are identified and re-clustered into four anomalous regimes. The baseline regimes exhibited clear seasonal and diurnal cycles. Meanwhile, the anomalous regimes represented <10 % of all hours but showed distinct combinations of speed, shear, and veer, when compared to the baseline regimes. Anomalous regimes are typically short-lived (~few hours), yet their transitions can lead to hub-height wind changes of −18 to +9 m s -1 . For a representative 15 MW turbine, these shifts imply rapid swings in capacity factor from near-full output to negligible generation. Validation with lidar buoy data showed 51% agreement in SOM labels across ~6,000 overlapping hours, with most mismatches confined to adjacent speed classes. HRRR comparisons further revealed that anomalous regimes were disproportionately associated with forecast biases exceeding 5 m s -1 . Together, these results reframe extremes in offshore wind from absolute maxima or minima to weather states that are difficult to anticipate from models.
This paper introduces a technique for digesting geospatial wind-speed data into areally defined -- country-level, in this case -- wind resource supply curves. We combined gridded wind-vector data for ocean areas with bathymetry maps, country exclusive economic zones, wind turbine power curves, and other datasets and relevant parameters to build supply curves that estimate a country's offshorewind resource defined by resource quality, depth, and distance-from-shore. We include a single set of supply curves -- for a particular assumption set -- and study some implications of including it in a global energy model. We also discuss the importance of downscaling gridded wind vector data to capturing the full resource potential, especially over land areas with complex terrain. This paperincludes motivation and background for a statistical downscaling methodology to account for terrain effects with a low computational burden. Finally, we use this forum to sketch a framework for building synthetic electric networks to estimate transmission accessibility of renewable resource sites in remote areas.
Reasonable and effective use of renewable resources can reduce dependence on traditional fossil-based energy sources and reduce carbon emissions. This study mapped the spatial potentials of renewable resources (i.e., solar radiation, precipitation, wind, and geothermal resources) in China. The results showed that China's most abundant renewable resources are located in the southwestern regions, which are significantly different from the spatial distribution patterns of population and economic development. Four southwestern provinces (Tibet, Qinghai, Sichuan, and Yunnan) make up only 7% of the national gross domestic product (GDP) and 30% of the national land area but possess 58% of the renewable resources. Furthermore, we found a weak to moderate degree of negative correlation between the emergy density of renewable resources and GDP per capita on the administrative levels of the prefecture-level cities for the whole country and in its eastern, central, and western regions. This means that the socioeconomically underdeveloped Midwest has more abundant renewable resources. A distributed energy-economic system may help to bridge the regional gaps of renewable sources in China. These findings can support policy decisions for the better development and use of renewable resources in China.