Preprints
https://doi.org/10.5194/wes-2023-110
https://doi.org/10.5194/wes-2023-110
05 Sep 2023
 | 05 Sep 2023
Status: a revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations

Nikolaos Bempedelis, Filippo Gori, Andrew Wynn, Sylvain Laizet, and Luca Magri

Abstract. Maximising the power production of large wind farms is key to the transition towards net zero. The overarching goal of this paper is to propose a computational method to maximise the power production of wind farms with two practical design strategies. First, we propose a gradient-free method to optimise the wind farm power production with high-fidelity surrogate models based on large-eddy simulations and a Bayesian framework. Second, we apply the proposed method to maximise wind farm power production by both micro-siting (layout optimisation) and wake steering (yaw angle optimisation). Third, we compare the optimisation results with the optimisation achieved with low-fidelity wake models. Finally, we propose a simple multi-fidelity strategy by combining the inexpensive wake models with the high-fidelity framework. The proposed gradient-free method can effectively maximise wind farm power production. Performance improvements relative to wake-model optimisation strategies can be attained, particularly in scenarios of increased flow complexity, such as in the wake steering problem, in which some of the assumptions in the simplified flow models become less accurate. The optimisation with high-fidelity methods takes into account nonlinear and unsteady fluid mechanical phenomena, which are leveraged by the proposed framework to increase the farm output. This paper opens up opportunities for wind farm optimisation with high-fidelity methods and without adjoint solvers.

Nikolaos Bempedelis, Filippo Gori, Andrew Wynn, Sylvain Laizet, and Luca Magri

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-110', Anonymous Referee #1, 25 Sep 2023
    • AC1: 'Reply on RC1', Nikolaos Bempedelis, 20 Oct 2023
  • RC2: 'Comment on wes-2023-110', Anonymous Referee #2, 03 Oct 2023
    • AC2: 'Reply on RC2', Nikolaos Bempedelis, 20 Oct 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-110', Anonymous Referee #1, 25 Sep 2023
    • AC1: 'Reply on RC1', Nikolaos Bempedelis, 20 Oct 2023
  • RC2: 'Comment on wes-2023-110', Anonymous Referee #2, 03 Oct 2023
    • AC2: 'Reply on RC2', Nikolaos Bempedelis, 20 Oct 2023
Nikolaos Bempedelis, Filippo Gori, Andrew Wynn, Sylvain Laizet, and Luca Magri
Nikolaos Bempedelis, Filippo Gori, Andrew Wynn, Sylvain Laizet, and Luca Magri

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Short summary
This paper proposes a computational method to maximise the power production of wind farms through two strategies: layout optimisation and yaw angle optimisation. The proposed method relies on high-fidelity computational modelling of wind farm flows and is shown to be able to effectively maximise wind farm power production. Performance improvements relative to conventional optimisation strategies based on low-fidelity models can be attained, particularly in scenarios of increased flow complexity.
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