Articles | Volume 9, issue 4
https://doi.org/10.5194/wes-9-869-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-869-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations
Nikolaos Bempedelis
CORRESPONDING AUTHOR
Department of Aeronautics, Imperial College London, SW7 2AZ London, UK
Filippo Gori
Department of Aeronautics, Imperial College London, SW7 2AZ London, UK
Andrew Wynn
Department of Aeronautics, Imperial College London, SW7 2AZ London, UK
Sylvain Laizet
Department of Aeronautics, Imperial College London, SW7 2AZ London, UK
Luca Magri
CORRESPONDING AUTHOR
Department of Aeronautics, Imperial College London, SW7 2AZ London, UK
The Alan Turing Institute, London NW1 2DB, UK
Politecnico di Torino, DIMEAS, Corso Duca degli Abruzzi, 24 10129 Torino, Italy
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Cited
14 citations as recorded by crossref.
- Techno-economic optimization of a hybrid wind-PV-diesel microgrid for Kasabonika Lake First Nation with Li-ion battery thermal safety assessment S. Roy et al. https://doi.org/10.1177/0309524X261477870
- WAKE-NET: A 3D-wake-aware economic turbine layout and cabling optimization framework for multi-capacity multi-hub-height wind farms serving grid-scale and industrial power systems A. Toms & X. Li https://doi.org/10.1016/j.renene.2026.126078
- Reinforcement learning increases wind farm power production by enabling closed-loop collaborative control A. Mole et al. https://doi.org/10.1038/s44172-026-00667-8
- Resilient maximum-power tracking of offshore wind turbine via two-mass drivetrain modeling and GA-optimized adaptive backstepping control Y. Belkhier et al. https://doi.org/10.1016/j.rico.2026.100737
- Model-free wind farm wake-steering optimization using the cross-entropy method Y. Jiang et al. https://doi.org/10.1088/1742-6596/3306/1/012001
- An extensive framework for preparing dual-Doppler radar measurements for wake model validation: Application to the AWAKEN large-scale field experiment Q. Zheng et al. https://doi.org/10.1063/5.0331761
- An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction Y. Wang et al. https://doi.org/10.1016/j.energy.2025.139116
- Stochastic risk-calibrated geospatial siting of renewable energy systems: multi-objective optimization under environmental and socio-economic uncertainty W. Mbasso et al. https://doi.org/10.1007/s00477-026-03333-9
- Multi-fidelity Bayesian Optimisation of Wind Farm Wake Steering using Wake Models and Large Eddy Simulations A. Mole & S. Laizet https://doi.org/10.1007/s10494-024-00629-0
- A review of artificial intelligence methodologies in marine renewable energy technologies A. Mirzaei et al. https://doi.org/10.1016/j.oceaneng.2026.126810
- Improved wind-power extraction downstream of a 3D hill using a tilted actuator disk K. Steiros et al. https://doi.org/10.1063/5.0280650
- Bayesian Optimisation of a Two‐Turbine Configuration Around a 2D Hill Using Large Eddy Simulations C. Jané‐Ippel et al. https://doi.org/10.1002/we.2946
- An adversarial multitasking evolutionary algorithm with latent feature transfer for wind farm layout optimization Z. Wang et al. https://doi.org/10.1016/j.energy.2026.142374
- Climate-Risk-Aware Offshore Wind Farm Layout Optimization Under Non-Stationary Wind Resources X. Yang https://doi.org/10.3390/wind6040060
14 citations as recorded by crossref.
- Techno-economic optimization of a hybrid wind-PV-diesel microgrid for Kasabonika Lake First Nation with Li-ion battery thermal safety assessment S. Roy et al. https://doi.org/10.1177/0309524X261477870
- WAKE-NET: A 3D-wake-aware economic turbine layout and cabling optimization framework for multi-capacity multi-hub-height wind farms serving grid-scale and industrial power systems A. Toms & X. Li https://doi.org/10.1016/j.renene.2026.126078
- Reinforcement learning increases wind farm power production by enabling closed-loop collaborative control A. Mole et al. https://doi.org/10.1038/s44172-026-00667-8
- Resilient maximum-power tracking of offshore wind turbine via two-mass drivetrain modeling and GA-optimized adaptive backstepping control Y. Belkhier et al. https://doi.org/10.1016/j.rico.2026.100737
- Model-free wind farm wake-steering optimization using the cross-entropy method Y. Jiang et al. https://doi.org/10.1088/1742-6596/3306/1/012001
- An extensive framework for preparing dual-Doppler radar measurements for wake model validation: Application to the AWAKEN large-scale field experiment Q. Zheng et al. https://doi.org/10.1063/5.0331761
- An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction Y. Wang et al. https://doi.org/10.1016/j.energy.2025.139116
- Stochastic risk-calibrated geospatial siting of renewable energy systems: multi-objective optimization under environmental and socio-economic uncertainty W. Mbasso et al. https://doi.org/10.1007/s00477-026-03333-9
- Multi-fidelity Bayesian Optimisation of Wind Farm Wake Steering using Wake Models and Large Eddy Simulations A. Mole & S. Laizet https://doi.org/10.1007/s10494-024-00629-0
- A review of artificial intelligence methodologies in marine renewable energy technologies A. Mirzaei et al. https://doi.org/10.1016/j.oceaneng.2026.126810
- Improved wind-power extraction downstream of a 3D hill using a tilted actuator disk K. Steiros et al. https://doi.org/10.1063/5.0280650
- Bayesian Optimisation of a Two‐Turbine Configuration Around a 2D Hill Using Large Eddy Simulations C. Jané‐Ippel et al. https://doi.org/10.1002/we.2946
- An adversarial multitasking evolutionary algorithm with latent feature transfer for wind farm layout optimization Z. Wang et al. https://doi.org/10.1016/j.energy.2026.142374
- Climate-Risk-Aware Offshore Wind Farm Layout Optimization Under Non-Stationary Wind Resources X. Yang https://doi.org/10.3390/wind6040060
Saved (final revised paper)
Latest update: 25 Oct 2026
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.
This paper proposes a computational method to maximise the power production of wind farms...
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