Wind and solar resource data sets

Clifton, Andrew; Draxl, Caroline; Badger, Jake; Habte, Aron

doi:10.1002/wene.276

Cited by 15 publications

(11 citation statements)

References 51 publications

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“…We focus on the techno-economic resource potential for creating utility-scale solar and/or wind power projects in coal mining areas and in top coal mining provinces/states in our focus countries. We do this by assessing a key parameter for setting up solar and/or wind projects-availability of suitable solar and wind power resources in the area (Mahtta et al 2014, Clifton et al 2018, which is a key requirement identified for developers/companies or governments to start a solar or wind power project (Mahtta et al 2014, Clifton et al 2018.…”

Section: Methods and Datamentioning

confidence: 99%

Solar has greater techno-economic resource suitability than wind for replacing coal mining jobs

Pai

Zerriffi

Jewell

et al. 2020

Environ. Res. Lett.

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Coal mining directly employs over 7 million workers and benefits millions more through indirect jobs. However, to meet the 1.5°C global climate target, coal's share in global energy supply should decline between 73% and 97% by 2050. But what will happen to coal miners as coal jobs disappear ? Answering this question is necessary to ensure a just transition and to ensure that politically powerful coal mining interests do not impede energy transitions. Some suggest that coal miners can transition to renewable jobs. However, prior research has not investigated the potential for renewable jobs to replace 'local' coal mining jobs. Historic analyses of coal industry declines show that coal miners do not migrate when they lose their jobs. By focusing on China, India, the US, and Australia, which represent 70% of global coal production, we investigate: (1) the local solar and wind capacity required in each coal mining area to enable all coal miners to transition to solar/wind jobs; (2) whether there are suitable solar and wind power resources in coal mining areas in order to install solar/wind plants and create those jobs; and (3) the scale of renewables deployment required to transition coal miners in areas suitable for solar/wind power. We find that with the exception of the US, several GWs of solar or wind capacity would be required in each coal mining area to transition all coal miners to solar/wind jobs. Moreover, while solar has more resource suitability than wind in coal mining areas, these resources are not available everywhere. In China, the country with the largest coal mining workforce, only 29% of coal mining areas are suitable for solar power. In all four countries, less than 7% of coal mining areas have suitable wind resources. Further, countries would have to scale-up their current solar capacity significantly to transition coal miners who work in areas suitable for solar development.

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Section: Methods and Datamentioning

confidence: 99%

Solar has greater techno-economic resource suitability than wind for replacing coal mining jobs

Pai

Zerriffi

Jewell

et al. 2020

Environ. Res. Lett.

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“…Lateral boundary conditions (LBCs) for these simulations are supplied every 6 h from the ERA-Interim reanalysis data (Dee et al, 2011). The NOAA realtime global sea surface temperature (RTG-SST) data set (Gemmill et al, 2007) is used to provide initial SST and Great Lakes conditions and are updated every 24 h. Data from the 30 arcsec Global Multi-resolution Terrain Elevation Data 2010 (GMTED) (Danielson and Gesch, 2011) are used to describe the topography and, for consistency with our use of the Noah land surface scheme, land cover is described using the Noah-modified 21-category IGBP-MODIS land use data set (Friedl et al, 2010). The time step used for the simulations is 72 s, and there are 41 vertical levels (in sigma hydrostatic pressure coordinate) up to a model top at 50 hPa.…”

Section: Simulationsmentioning

confidence: 99%

Interannual variability of wind climates and wind turbine annual energy production

2018

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Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in over 90 % of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

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“…Wind resource estimates can be developed in several ways (Clifton et al 2018). Historically, static maps have been created to show the wind resource across a region.…”

Section: Approachmentioning

confidence: 99%

Assessing the Wind Energy Potential in Bangladesh: Enabling Wind Energy Development with Data Products

Jacobson

Draxl

Jimenez

et al. 2018

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Wind and solar resource data sets

Cited by 15 publications

References 51 publications

Solar has greater techno-economic resource suitability than wind for replacing coal mining jobs

Solar has greater techno-economic resource suitability than wind for replacing coal mining jobs

Interannual variability of wind climates and wind turbine annual energy production

Assessing the Wind Energy Potential in Bangladesh: Enabling Wind Energy Development with Data Products

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