loading page

Collective wind farm operation based on a predictive model increases utility-scale energy production
  • +5
  • Michael F. Howland,
  • Jesús Bas Quesada,
  • Juan José Pena Martínez,
  • Felipe Palou Larrañaga,
  • Neeraj Yadav,
  • Jasvipul S. Chawla,
  • Varun Sivaram,
  • John O. Dabiri
Michael F. Howland
Massachusetts Institute of Technology

Corresponding Author:[email protected]

Author Profile
Jesús Bas Quesada
Siemens Gamesa Renewable Energy Innovation & Technology
Author Profile
Juan José Pena Martínez
Siemens Gamesa Renewable Energy Innovation & Technology
Author Profile
Felipe Palou Larrañaga
Siemens Gamesa Renewable Energy Innovation & Technology
Author Profile
Neeraj Yadav
ReNew Power Private Limited
Author Profile
Jasvipul S. Chawla
ReNew Power Private Limited
Author Profile
Varun Sivaram
ReNew Power Private Limited
Author Profile
John O. Dabiri
California Institute of Technology
Author Profile

Abstract

Wind turbines located in wind farms are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. In this study, we operate a wind turbine array collectively to maximize total array production through wake steering. The selection of the farm control strategy relies on the optimization of computationally efficient flow models. We develop a physics-based, data-assisted flow control model to predict the optimal control strategy. In contrast to previous studies, we first design and implement a multi-month field experiment at a utility-scale wind farm to validate the model over a range of control strategies, most of which are suboptimal. The flow control model is able to predict the optimal yaw misalignment angles for the array within +/-5 degrees for most wind directions (11-32% power gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 2.7% and 1.0%, for the wind directions of interest and for wind speeds between 6 and 8 m/s and all wind speeds, respectively. The developed and validated predictive model can enable a wider adoption of collective wind farm operation.