Wind farm power optimization through wake steering

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...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2019-07, Vol.116 (29), p.14495-14500
Main Authors: Howland, Michael F., Lele, Sanjiva K., Dabiri, John O.
Format: Article
Language:English
Subjects:
Ascent
Clean energy
Climate change
Historic buildings & sites
Intergovernmental Panel on Climate Change
Misalignment
Optimization
Physical Sciences
Power efficiency
Renewable energy
Statistical analysis
Statistical significance
Steering
Transmission lines
Turbines
Wakes
Wind farms
Wind power
Wind power generation
Wind speed
Wind turbines
Yaw
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 °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
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1903680116