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Oklahoma's future wind energy resources and their relationship with the Central Plains low-level jet

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  • Wimhurst, Joshua J.
  • Greene, J. Scott

Abstract

Previous work has projected increases in wind speed/wind power density (WPD) over the United States' South-Central Plains, which could be linked to future increases in frequency of the Central Plains Low-Level Jet (LLJ). This work serves to both update projections of this region's wind energy resources, and to assess the LLJ's importance for influencing these resources. Projected changes in several wind energy metrics (including ramp event frequency and cut-off frequency) were assessed specifically over the Oklahoma Panhandle. These projections were computed by using a climatological delta method to statistically downscale changes in near-surface wind speed from multiple climate models onto observations from the Oklahoma Mesonet. The results suggest a pattern of overall reductions in wind speed and WPD, slight reductions in ramp event frequency, and little change in cut-off frequency, with projected changes often being larger and of statistical significance at night and during summer and autumn. Highly distinguishable diurnal and seasonal variability in ramp event and cut-off frequency was found in this work, with these frequencies most notably maximizing during the day. This climatological variability is consistent with that seen in observations of these wind energy metrics. Regression of LLJ characteristics against wind energy metrics identified consistent and conceptually understandable links between increased LLJ frequency and decreased ramp event frequency in future decades. Such a result attests to a more stable and easier to manage future wind energy resource over the Oklahoma Panhandle.

Suggested Citation

  • Wimhurst, Joshua J. & Greene, J. Scott, 2019. "Oklahoma's future wind energy resources and their relationship with the Central Plains low-level jet," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
  • Handle: RePEc:eee:rensus:v:115:y:2019:i:c:s1364032119305829
    DOI: 10.1016/j.rser.2019.109374
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    Cited by:

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    3. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    4. Daniel Vassallo & Raghavendra Krishnamurthy & Thomas Sherman & Harindra J. S. Fernando, 2020. "Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting," Energies, MDPI, vol. 13(20), pages 1-19, October.
    5. Ayman Al-Quraan & Bashar Al-Mhairat & Ahmad M. A. Malkawi & Ashraf Radaideh & Hussein M. K. Al-Masri, 2023. "Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    6. Chen, Liang, 2020. "Impacts of climate change on wind resources over North America based on NA-CORDEX," Renewable Energy, Elsevier, vol. 153(C), pages 1428-1438.
    7. Wimhurst, Joshua J. & Greene, J. Scott & Koch, Jennifer, 2023. "Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model," Applied Energy, Elsevier, vol. 352(C).
    8. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).

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