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Meteorologically defined limits to reduction in the variability of outputs from a coupled wind farm system in the Central US

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  • Huang, Junling
  • Lu, Xi
  • McElroy, Michael B.

Abstract

Studies suggest that onshore wind resources in the contiguous US could readily accommodate present and anticipated future US demand for electricity. The problem with the output from a single wind farm located in any particular region is that it is variable on time scales ranging from minutes to days posing difficulties for incorporating relevant outputs into an integrated power system. The high frequency (shorter than once per day) variability of contributions from individual wind farms is determined mainly by locally generated small scale boundary layer. The low frequency variability (longer than once per day) is associated with the passage of transient waves in the atmosphere with a characteristic time scale of several days. Using 5 years of assimilated wind data, we show that the high frequency variability of wind-generated power can be significantly reduced by coupling outputs from 5 to 10 wind farms distributed uniformly over a ten state region of the Central US in this study. More than 95% of the remaining variability of the coupled system is concentrated at time scales longer than a day, allowing operators to take advantage of multi-day weather forecasts in scheduling projected contributions from wind.

Suggested Citation

  • Huang, Junling & Lu, Xi & McElroy, Michael B., 2014. "Meteorologically defined limits to reduction in the variability of outputs from a coupled wind farm system in the Central US," Renewable Energy, Elsevier, vol. 62(C), pages 331-340.
    • Handle: RePEc:eee:renene:v:62:y:2014:i:c:p:331-340
    DOI: 10.1016/j.renene.2013.07.022
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    References listed on IDEAS

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    2. Rose, Stephen & Apt, Jay, 2015. "What can reanalysis data tell us about wind power?," Renewable Energy, Elsevier, vol. 83(C), pages 963-969.
    3. Dey, Subhashish & Sreenivasulu, Anduri & Veerendra, G.T.N. & Rao, K. Venkateswara & Babu, P.S.S. Anjaneya, 2022. "Renewable energy present status and future potentials in India: An overview," Innovation and Green Development, Elsevier, vol. 1(1).
    4. Shahriari, Mehdi & Blumsack, Seth, 2018. "The capacity value of optimal wind and solar portfolios," Energy, Elsevier, vol. 148(C), pages 992-1005.
    5. Rose, Stephen & Apt, Jay, 2016. "Quantifying sources of uncertainty in reanalysis derived wind speed," Renewable Energy, Elsevier, vol. 94(C), pages 157-165.
    6. Huang, Junling & McElroy, Michael B., 2015. "A 32-year perspective on the origin of wind energy in a warming climate," Renewable Energy, Elsevier, vol. 77(C), pages 482-492.
    7. Qiwei Li & Jiaxuan Zhang & Jiahui Chen & Xi Lu, 2019. "Reflection on opportunities for high penetration of renewable energy in China," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(3), May.
    8. Rezaee Jordehi, Ahmad, 2016. "Allocation of distributed generation units in electric power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 893-905.
    9. Han, Chanok & Vinel, Alexander, 2022. "Reducing forecasting error by optimally pooling wind energy generation sources through portfolio optimization," Energy, Elsevier, vol. 239(PB).
    10. Yongqian Liu & Yanhui Qiao & Shuang Han & Yanping Xu & Tianxiang Geng & Tiandong Ma, 2021. "Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid," Energies, MDPI, vol. 14(16), pages 1-14, August.
    11. Richard Schmalensee, 2016. "The Performance of U.S. Wind and Solar Generators," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    12. Handschy, Mark A. & Rose, Stephen & Apt, Jay, 2017. "Is it always windy somewhere? Occurrence of low-wind-power events over large areas," Renewable Energy, Elsevier, vol. 101(C), pages 1124-1130.
    13. McPherson, Madeleine & Karney, Bryan, 2017. "A scenario based approach to designing electricity grids with high variable renewable energy penetrations in Ontario, Canada: Development and application of the SILVER model," Energy, Elsevier, vol. 138(C), pages 185-196.

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