期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 116, 期 29, 页码 14495-14500出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1903680116
关键词
wind energy; turbulence; data science
资金
- National Science Foundation Graduate Research Fellowship [DGE-1656518]
- Stanford Graduate Fellowship
Global power production increasingly relies on wind farms to supply low-carbon energy. The recent Intergovernmental Panel on Climate Change (IPCC) Special Report predicted that renewable energy production must leap from 20 % of the global energy mix in 2018 to 67 % by 2050 to keep global temperatures from rising 1.5 degrees C above preindustrial levels. This increase requires reliable, low-cost energy production. However, wind turbines are often placed in close proximity within wind farms due to land and transmission line constraints, which results in wind farm efficiency degradation of up to 40 % for wind directions aligned with columns of turbines. To increase wind farm power production, we developed a wake steering control scheme. This approach maximizes the power of a wind farm through yaw misalignment that deflects wakes away from downstream turbines. Optimization was performed with site-specific analytic gradient ascent relying on historical operational data. The protocol was tested in an operational wind farm in Alberta, Canada, resulting in statistically significant (P < 0.05) power increases of 7-13 % for wind speeds near the site average and wind directions which occur during less than 10 % of nocturnal operation and 28-47 % for low wind speeds in the same wind directions. Wake steering also decreased the variability in the power production of the wind farm by up to 72 %. Although the resulting gains in annual energy production were insignificant at this farm, these statistically significant wake steering results demonstrate the potential to increase the efficiency and predictability of power production through the reduction of wake losses.
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