A continuously updated, geospatially rectified database of utility-scale wind turbines in the United States

Rand, Joseph; Kramer, Louisa; Garrity, Christopher P.; Hoen, Ben; Diffendorfer, Jay E.; Hunt, Hannah E.; Spears, Michael

doi:10.1038/s41597-020-0353-6

Cited by 38 publications

(41 citation statements)

References 23 publications

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“…Despite the evident utility of location data, spatially explicit national data are only publicly available for a handful of countries [20][21][22][23] . Furthermore, often when they are available they are not open access.…”

Section: Background and Summarymentioning

confidence: 99%

“…In order to justify spatially clustering the remaining renewable energy point data into 'farms' based on their position in space relative to other points, we analysed the spatial characteristics of two large wind and solar databases, the United States Geological Survey (USGS) Wind Turbine Dataset (USWTD) and Wiki Solar 22,23,26 , to check whether they were significantly clustered in space.…”

Section: Determining the Scale For Spatial Clustering: Spatial Distrimentioning

confidence: 99%

“…Using this technique, we can assign more descriptive metadata from national databases to the spatial information gleaned from the processed OSM data. For wind, these validation datasets were the USWTD, the United Kingdom Renewable Energy Planning Database (UK REPD), and data from a German renewable energy study [20][21][22][23] . Solar also utilised the German and UK data (these databases provide more than one type of renewable energy), swapping in data from Wiki Solar 26 in place of the USWTD.…”

Section: Estimating Power Output Of Installations the Vast Majority mentioning

confidence: 99%

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Harmonised global datasets of wind and solar farm locations and power

et al. 2020

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Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet's limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. the majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code to enable users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate these data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.

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Section: Background and Summarymentioning

confidence: 99%

Section: Determining the Scale For Spatial Clustering: Spatial Distrimentioning

confidence: 99%

Section: Estimating Power Output Of Installations the Vast Majority mentioning

confidence: 99%

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Harmonised global datasets of wind and solar farm locations and power

et al. 2020

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“…For academics, I see this paper as providing an important starting-point for discussions around the clustering, size, and growing capacity of wind turbines. Echoing the benefits described by Rand et al [2], this paper may also provide important context for groups interested in: climate change and air quality [4], local health and well-being [5], grid impacts [6], land requirements [7], local surface temperatures [8], sound and noise [9], property values [10] [11], renewable energy potentials [12], and acceptance research [13] [14] [15].…”

Section: Introductionmentioning

confidence: 90%

“…Responding to intersecting problems including global climate change, air pollu-tion, and domestic energy insecurity, wind energy has emerged as a major source of low-carbon electricity generation. In the United States alone, there are now more than 60,000 utility-scale turbines, representing nearly 100 gigawatts of wind energy capacity and 15% of the global total [1] [2]. Much of this has been introduced over the past decade, and yet up until recently, there was no publicly accessible dataset that described wind turbines and their characteristics (e.g.…”

Section: Introductionmentioning

confidence: 99%

Using the United States Wind Turbine Database to Identify Increasing Turbine Size, Capacity and Other Development Trends

Walker¹

2020

EPE

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The purpose of this article was to analyze data associated with advances in wind energy across the United States. While governments, academia, and the private sector generally know patterns of wind turbine development (i.e. turbine size and capacity growing in recent years), there is no known independent, reliable, and/or updated summary of these variables. Using data collected by the Lawrence Berkeley National Laboratory and partners, this study used descriptive statistics to show turbine development and growth patterns from 1981-2019. The newly created United States Wind Turbine Database (USWTDB) represents the most comprehensive account of wind turbine information and was updated in January 2020. Variables I am interested in here are turbine manufacturer, state of project, turbine and project capacity, and turbine size. Findings provide empirical evidence to support the common, yet previously unrefined statements that wind turbines are growing larger in number, size and capacity. This growth is varied over spatial and temporal scales. I also provide evidence to show patterns of turbine manufacturing, with GE Wind dominating much of the US wind energy landscape today. I hope this work provides a timely resource for those interested in a variety of questions surrounding wind energy development in the United States. Perhaps more importantly, this analysis will hopefully inspire others to use what the USWTDB provides and answer larger questions surrounding wind energy futures.

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What can surface wind observations tell us about interannual variation in wind energy output?

2022

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The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are 25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (<25 km) wind plants and compare annual estimated generation, based on a function of surface wind speed observations, to annual recorded generation. Researchers rely on this publicly available surface data because measurements co-located with wind plants are generally considered proprietary. Our analysis addresses a research gap: interannual variation in observed surface wind speeds is rarely compared to observed data at wind plant locations and turbine heights. We find that despite its common use for this purpose, generation estimates based on publicly available surface observational data provide a poor proxy for interannual variability in recorded wind generation. These findings suggest that caution is generally needed when researchers use surface wind speed measurements to investigate longterm wind energy trends.

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A continuously updated, geospatially rectified database of utility-scale wind turbines in the United States

Cited by 38 publications

References 23 publications

Harmonised global datasets of wind and solar farm locations and power

Harmonised global datasets of wind and solar farm locations and power

Using the United States Wind Turbine Database to Identify Increasing Turbine Size, Capacity and Other Development Trends

What can surface wind observations tell us about interannual variation in wind energy output?

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