Land use and turbine technology influences on wind potential in the United States

Lopez, Anthony; Mai, Trieu; Lantz, Eric; Harrison‐Atlas, Dylan; Williams, Tess; Maclaurin, Galen

doi:10.1016/j.energy.2021.120044

Cited by 70 publications

(101 citation statements)

References 29 publications

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“…One of the key results based on the analysis from [21] was that technological advancement could require larger setbacks from buildings and infrastructure, reducing total capacity potential by 20% compared to estimates using current technology, but that this reduction would be largely offset so that net generation differs by only 1%. A key design concept of the reV model dashboard was to enable users to explore this finding through data-driven interactive uncertainty visualization.…”

Section: Methodology and Approachmentioning

confidence: 99%

“…We consider the following QOIs for each potential wind resource site: mean capacity factor (mean CF), which is defined as the ratio of electricity output to maximum The data is first pre-processed using the Dask and Pandas libraries for Python. The pre-processing involves selecting only the relevant QOIs, partitioning the data into seven NREL Regions as defined in [21], and computing mean and standard deviations for each scenario individually across CONUS, as well as mean and standard deviations across all scenarios for each latitude/longitude pair. The necessary data files are then stored in JSON format and used as selectable inputs to drive the dashboard visualizations.…”

Section: Methodology and Approachmentioning

confidence: 99%

“…In addition, wind potential is useful to a broad set of stakeholders including policy makers, land managers, and government agencies for setting renewable energy targets. The data at the core of this visualization was specifically generated to investigate the combined effects of turbine technology innovation, regulatory restrictions, and land use constraints throughout the contiguous United States [21].…”

Section: Introductionmentioning

confidence: 99%

“…This data set presented in the visualization are from recent research that explores varying land-use constraints and interactions with turbine designs [21], all of which affect wind potential. Specific siting constraint components of the data include: infrastructure, where land use and land cover data are drawn from the National Land Cover Dataset [39], the Global Human Settlement Model was leveraged to capture high-density urban development [30], and airport extents are incorporated via the Homeland Security Infrastructure Program Gold [14]; building footprints data representing structures which may require setbacks for wind turbines was sourced from Microsoft building footprints [5]; roads, railroads, and electric transmission rights-of-way are integrated via the Homeland Infrastructure Foundation-Level Data [8]; radar line-of-sight contraints are incorporated for the NEXRAD short-range and longrange radar network; regulatory constraints regarding hub height, setback requirements from buildings, roads, railroads, transmission, and rivers are included for 271 county and 7 state ordinances [28].…”

Section: Introductionmentioning

confidence: 99%

“…Namely, these are described in the reV dashboard as: open access, limited access, and reference access. Each of these siting regimes are discussed and analyzed in detail in [21]. Open access is meant to represent a "ceiling" on wind potential by being the least restrictive considering restrictions related to water bodies, builds, roads, protected lands, and lands of conservative easement.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Uncertainty Visualization for Renewable Energy Potential

Diaz

Potter

Johnson

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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Figure 1: A visualization of uncertainty using the reV Dashboard. The left pane consists of data selection menus which allow the user to interact with the dashboard. The map component on the right shows site specific information considering all possible scenarios present in the data, in this case the color map corresponds to the standard deviation of land use (area [Km 2 ]) required across all scenarios for each possible wind site. The parallel coordinate plot on the bottom displays statistical information computed for each individual scenario.

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Section: Methodology and Approachmentioning

confidence: 99%

Section: Methodology and Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Uncertainty Visualization for Renewable Energy Potential

Diaz

Potter

Johnson

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

Self Cite

View full text Add to dashboard Cite

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Time, climate, and soil settings set the course for reclamation outcomes following dryland energy development

Lupardus,

Sengsirirak,

Griffen

et al. 2023

Land Degrad Dev

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Soil attributes, climate, and time since reclamation have important implications for oil and gas reclamation success on drylands. It is uncertain if reclaimed well pads, on highly degraded drylands, can successfully regain ecological function or meet indicator benchmarks for reclamation. Here, our goals were to assess patterns in reclamation outcomes relative to (1) soil attributes, climate, and time since reclamation; and (2) plant and soil reference benchmarks. We collected a suite of plant, soil, and landscape characteristics from 134 reclaimed well pads on the Colorado Plateau that spanned climate and soil gradients and ranged from 2 to 20 years post‐reclamation, then compared characteristics to those from 583 reference plots. On the reclaimed pads, less saline soils, more mesic climates, and longer time since reclamation were associated with favorable reclamation outcomes based on metrics of plant species richness and diversity, plant structure, and cover of important plant functional groups. However, most pads had major departures in 1/4 indicators, suggesting unsuccessful reclamation. Arid warm locations with shallow soils had the greatest percentage of major departures, and noxious plant richness had the greatest departures across soil and climate settings. Our assessment indicates reclamation is failing in key metrics for most pads, and the results highlight the role of environmental conditions in driving reclamation outcomes. These new insights are valuable for ascertaining possible reclamation outcomes and success rates following energy related disturbance, including orphaned wells and renewable energy development. Importantly, the approach used here has applicability for setting benchmarks and outcome standards.

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Machine learning enables national assessment of wind plant controls with implications for land use

2021

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Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

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Land use and turbine technology influences on wind potential in the United States

Cited by 70 publications

References 29 publications

Uncertainty Visualization for Renewable Energy Potential

Uncertainty Visualization for Renewable Energy Potential

Time, climate, and soil settings set the course for reclamation outcomes following dryland energy development

Machine learning enables national assessment of wind plant controls with implications for land use

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