Preprints
https://doi.org/10.5194/wes-2021-85
https://doi.org/10.5194/wes-2021-85

  17 Aug 2021

17 Aug 2021

Review status: this preprint is currently under review for the journal WES.

Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions

Michael F. Howland1,2, Aditya S. Ghate3,4, Jesus Bas Quesada5, Juan Jose Pena Martinez5, Wei Zhong5, Felipe Palou Larranaga5, Sanjiva K. Lele3, and John O. Dabiri2,6 Michael F. Howland et al.
  • 1Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
  • 2Graduate Aerospace Laboratories (GALCIT), California Institute of Technology, Pasadena, CA 91125
  • 3Department of Astronautics and Aeronautics, Stanford University, Stanford, CA 94305
  • 4NASA Ames Research Center, Moffet Field, CA 94035
  • 5Siemens Gamesa Renewable Energy Innovation & Technology, Sarriguren, Navarra, Spain, 31621
  • 6Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA 91125

Abstract. The magnitude of wake interactions between individual wind turbines depends on the atmospheric stability. We investigate strategies for wake loss mitigation through the use of closed-loop wake steering using large eddy simulations of the diurnal cycle, where variations in the surface heat flux in time modify the atmospheric stability, wind speed and direction, shear, turbulence, and other atmospheric boundary layer flow (ABL) features. The closed-loop wake steering control methodology developed in Part 1 (Howland et al., Wind Energy Science, 2020, 5, 1315–1338) is implemented in an eight turbine wind farm in large eddy simulations of the diurnal cycle. The optimal yaw misalignment set-points depend on the wind direction, which varies in time during the diurnal cycle. To improve the application of wake steering control in transient ABL conditions with an evolving mean flow state, we develop a regression-based wind direction forecast method. We compare the closed-loop wake steering control methodology to baseline yaw aligned control and open-loop lookup table control for various selections of the yaw misalignment set-point update frequency, which dictates the balance between wind direction tracking and yaw activity. Closed-loop wake steering with set-point optimization under uncertainty results in higher collective energy production than both baseline yaw aligned control and open-loop lookup table control. The increase in wind farm energy production for closed- and open-loop wake steering control compared to baseline yaw aligned control, is 4.0–4.1 % and 3.4–3.8 %, respectively, with the range indicating variations in the energy increase results depending on the set-point update frequency. The primary energy increases through wake steering occur during stable ABL conditions. Open-loop lookup table control decreases energy production in the convective ABL conditions simulated, compared to baseline yaw aligned control, while closed-loop control increases energy production in convective conditions.

Michael F. Howland et al.

Status: open (until 02 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-85', Bart M. Doekemeijer, 03 Sep 2021 reply
  • RC2: 'Comment on wes-2021-85', Paul van der Laan, 14 Sep 2021 reply

Michael F. Howland et al.

Data sets

Supporting data for Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions Michael F. Howland https://doi.org/10.5281/zenodo.5160943

Model code and software

PadeOps Aditya S. Ghate, Akshay Subramaniam, and Michael F. Howland https://github.com/FPAL-Stanford-University/PadeOps

Michael F. Howland et al.

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Short summary
Wake steering control, where turbines are intentionally misaligned with the incident wind, has demonstrated potential to increase wind farm energy. We investigate wake steering control methods in simulations of a wind farm operating in the terrestrial diurnal cycle. We develop a statistical wind direction forecast to improve wake steering in flows with time-varying states. Closed-loop wake steering control increases wind farm energy production, compared to baseline and open-loop control.