Articles | Volume 3, issue 2
https://doi.org/10.5194/wes-3-651-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-651-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Sep 2018
Research article |
| 24 Sep 2018
| 24 Sep 2018
Interannual variability of wind climates and wind turbine annual energy production
Department of Earth and Atmospheric Sciences, Cornell University,
Ithaca, NY 14853, USA
Tristan J. Shepherd
Department of Earth and Atmospheric Sciences, Cornell University,
Ithaca, NY 14853, USA
Rebecca J. Barthelmie
Sibley School of Mechanical and Aerospace Engineering, Cornell
University, Ithaca, NY 14853, USA
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Cited
35 citations as recorded by crossref.
- Wind Farm Wakes Simulated Using WRF S. Pryor et al. 10.1088/1742-6596/1256/1/012025
- Sensitivity of Wind Turbine Array Downstream Effects to the Parameterization Used in WRF T. Shepherd et al. 10.1175/JAMC-D-19-0135.1
- Inter Annual Variability of wind speed in India J. Bastin et al. 10.1080/01430750.2021.1945492
- 20% of US electricity from wind will have limited impacts on system efficiency and regional climate S. Pryor et al. 10.1038/s41598-019-57371-1
- Uncertainty models for the structural design of floating offshore wind turbines: A review M. Ramezani et al. 10.1016/j.rser.2023.113610
- Wind turbine blade leading-edge erosion: laboratory experiments and image processing under an engaged learning experience R. Barthelmie et al. 10.1088/1742-6596/1452/1/012046
- A global assessment of extreme wind speeds for wind energy applications S. Pryor & R. Barthelmie 10.1038/s41560-020-00773-7
- Exploring ENSO-Induced Anomalies over North America in Historical and Future Climate Simulations That Use HadGEM2-ESM Output to Drive WRF T. Shepherd et al. 10.3390/cli10080117
- Wind resource assessment and techno-economic analysis of wind energy and green hydrogen production in the Republic of Djibouti O. Dabar et al. 10.1016/j.egyr.2022.07.013
- Powering the 21st century by wind energy—Options, facts, figures K. Rohrig et al. 10.1063/1.5089877
- Climate change impacts on wind energy generation in Ireland E. Doddy Clarke et al. 10.1002/we.2673
- Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data A. Basse et al. 10.5194/wes-6-1473-2021
- Can wind and solar replace coal in Texas? R. Morse et al. 10.1186/s40807-022-00069-2
- Radar-derived precipitation climatology for wind turbine blade leading edge erosion F. Letson et al. 10.5194/wes-5-331-2020
- Climate Change Mitigation Potential of Wind Energy R. Barthelmie & S. Pryor 10.3390/cli9090136
- On the Development of Wind Market Values and the Influence of Technology and Weather: a German Case Study T. Engelhorn & T. Möbius 10.1007/s12398-022-00319-2
- Climate change impacts on wind power generation S. Pryor et al. 10.1038/s43017-020-0101-7
- Prediction of hourly wind speed time series at unsampled locations using machine learning F. Houndekindo & T. Ouarda 10.1016/j.energy.2024.131518
- Wind Climate and Wind Power Resource Assessment Based on Gridded Scatterometer Data: A Thracian Sea Case Study N. Kokkos et al. 10.3390/en14123448
- Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts N. Effenberger et al. 10.1088/1748-9326/ad0bd6
- Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting D. Vassallo et al. 10.3390/en13205488
- Atmospheric pressure gradients and Coriolis forces provide geophysical limits to power density of large wind farms E. Antonini & K. Caldeira 10.1016/j.apenergy.2020.116048
- Analysis of wind power potential by the three-parameter Weibull distribution to install a wind turbine K. Sukkiramathi & C. Seshaiah 10.1177/0144598719871628
- Cluster wakes impact on a far-distant offshore wind farm's power J. Schneemann et al. 10.5194/wes-5-29-2020
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
- Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S. Z. Zeng et al. 10.1016/j.apenergy.2024.124698
- From wind conditions to operational strategy: optimal planning of wind turbine damage progression over its lifetime N. Requate et al. 10.5194/wes-8-1727-2023
- On the effects of inter-farm interactions at the offshore wind farm Alpha Ventus V. Pettas et al. 10.5194/wes-6-1455-2021
- Wind power generation: A review and a research agenda S. Vargas et al. 10.1016/j.jclepro.2019.02.015
- Wind resource assessment and economic analysis for electricity generation in three locations of the Republic of Djibouti O. Assowe Dabar et al. 10.1016/j.energy.2019.07.107
- Assessing the stability of wind resource and operating conditions S. Pryor et al. 10.1088/1742-6596/1452/1/012084
- Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
- The Potential Wind Power Resource in Central-South of the Constanta County E. Boboc 10.2478/mmce-2019-0007
- Spatial and temporal variability characteristics of offshore wind energy in the United Kingdom P. Potisomporn & C. Vogel 10.1002/we.2685
- Foreseeing the spatio-temporal offshore wind energy potential of India using a differential weighted ensemble created using CMIP6 datasets D. Basak et al. 10.1016/j.rsma.2023.103066
35 citations as recorded by crossref.
- Wind Farm Wakes Simulated Using WRF S. Pryor et al. 10.1088/1742-6596/1256/1/012025
- Sensitivity of Wind Turbine Array Downstream Effects to the Parameterization Used in WRF T. Shepherd et al. 10.1175/JAMC-D-19-0135.1
- Inter Annual Variability of wind speed in India J. Bastin et al. 10.1080/01430750.2021.1945492
- 20% of US electricity from wind will have limited impacts on system efficiency and regional climate S. Pryor et al. 10.1038/s41598-019-57371-1
- Uncertainty models for the structural design of floating offshore wind turbines: A review M. Ramezani et al. 10.1016/j.rser.2023.113610
- Wind turbine blade leading-edge erosion: laboratory experiments and image processing under an engaged learning experience R. Barthelmie et al. 10.1088/1742-6596/1452/1/012046
- A global assessment of extreme wind speeds for wind energy applications S. Pryor & R. Barthelmie 10.1038/s41560-020-00773-7
- Exploring ENSO-Induced Anomalies over North America in Historical and Future Climate Simulations That Use HadGEM2-ESM Output to Drive WRF T. Shepherd et al. 10.3390/cli10080117
- Wind resource assessment and techno-economic analysis of wind energy and green hydrogen production in the Republic of Djibouti O. Dabar et al. 10.1016/j.egyr.2022.07.013
- Powering the 21st century by wind energy—Options, facts, figures K. Rohrig et al. 10.1063/1.5089877
- Climate change impacts on wind energy generation in Ireland E. Doddy Clarke et al. 10.1002/we.2673
- Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data A. Basse et al. 10.5194/wes-6-1473-2021
- Can wind and solar replace coal in Texas? R. Morse et al. 10.1186/s40807-022-00069-2
- Radar-derived precipitation climatology for wind turbine blade leading edge erosion F. Letson et al. 10.5194/wes-5-331-2020
- Climate Change Mitigation Potential of Wind Energy R. Barthelmie & S. Pryor 10.3390/cli9090136
- On the Development of Wind Market Values and the Influence of Technology and Weather: a German Case Study T. Engelhorn & T. Möbius 10.1007/s12398-022-00319-2
- Climate change impacts on wind power generation S. Pryor et al. 10.1038/s43017-020-0101-7
- Prediction of hourly wind speed time series at unsampled locations using machine learning F. Houndekindo & T. Ouarda 10.1016/j.energy.2024.131518
- Wind Climate and Wind Power Resource Assessment Based on Gridded Scatterometer Data: A Thracian Sea Case Study N. Kokkos et al. 10.3390/en14123448
- Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts N. Effenberger et al. 10.1088/1748-9326/ad0bd6
- Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting D. Vassallo et al. 10.3390/en13205488
- Atmospheric pressure gradients and Coriolis forces provide geophysical limits to power density of large wind farms E. Antonini & K. Caldeira 10.1016/j.apenergy.2020.116048
- Analysis of wind power potential by the three-parameter Weibull distribution to install a wind turbine K. Sukkiramathi & C. Seshaiah 10.1177/0144598719871628
- Cluster wakes impact on a far-distant offshore wind farm's power J. Schneemann et al. 10.5194/wes-5-29-2020
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
- Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S. Z. Zeng et al. 10.1016/j.apenergy.2024.124698
- From wind conditions to operational strategy: optimal planning of wind turbine damage progression over its lifetime N. Requate et al. 10.5194/wes-8-1727-2023
- On the effects of inter-farm interactions at the offshore wind farm Alpha Ventus V. Pettas et al. 10.5194/wes-6-1455-2021
- Wind power generation: A review and a research agenda S. Vargas et al. 10.1016/j.jclepro.2019.02.015
- Wind resource assessment and economic analysis for electricity generation in three locations of the Republic of Djibouti O. Assowe Dabar et al. 10.1016/j.energy.2019.07.107
- Assessing the stability of wind resource and operating conditions S. Pryor et al. 10.1088/1742-6596/1452/1/012084
- Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
- The Potential Wind Power Resource in Central-South of the Constanta County E. Boboc 10.2478/mmce-2019-0007
- Spatial and temporal variability characteristics of offshore wind energy in the United Kingdom P. Potisomporn & C. Vogel 10.1002/we.2685
- Foreseeing the spatio-temporal offshore wind energy potential of India using a differential weighted ensemble created using CMIP6 datasets D. Basak et al. 10.1016/j.rsma.2023.103066
Latest update: 23 Nov 2024
Short summary
The interannual variability (IAV) of annual energy production (AEP) from wind turbines due to IAV in wind speeds from proposed wind farms plays a key role in dictating project financing but is only poorly constrained. This study provides improved quantification of IAV over eastern N. America using purpose-performed long-term numerical simulations. It may be appropriate to reduce the IAV applied to preconstruction AEP estimates, which would decrease the cost of capital for wind farm developments.
The interannual variability (IAV) of annual energy production (AEP) from wind turbines due to...
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