Articles | Volume 5, issue 1
https://doi.org/10.5194/wes-5-413-2020
© Author(s) 2020. 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-5-413-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Mar 2020
Research article |
| 30 Mar 2020
| 30 Mar 2020
Wake steering optimization under uncertainty
University of Colorado, Boulder, CO, USA
National Renewable Energy Laboratory, Golden, CO, USA
Jennifer King
National Renewable Energy Laboratory, Golden, CO, USA
Ryan N. King
National Renewable Energy Laboratory, Golden, CO, USA
Peter E. Hamlington
University of Colorado, Boulder, CO, USA
Katherine Dykes
National Renewable Energy Laboratory, Golden, CO, USA
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Cited
41 citations as recorded by crossref.
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland https://doi.org/10.1063/5.0051071
- A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha https://doi.org/10.1115/1.4054501
- Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control J. Quick et al. https://doi.org/10.1088/1742-6596/3224/3/032080
- Wake steering under inflow wind direction uncertainty: an LES study E. Hodgson & S. Andersen https://doi.org/10.5194/wes-11-2173-2026
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
- Multicluster Distributed Optimization Strategy for Turbine Wake Environment Z. Yu et al. https://doi.org/10.1002/aisy.202400884
- Wind Farm Yield and Lifetime Optimization by Smart Steering of Wakes J. Schmidt et al. https://doi.org/10.1088/1742-6596/1934/1/012020
- A Machine Learning Method for Modeling Wind Farm Fatigue Load Y. Miao et al. https://doi.org/10.3390/app12157392
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Review of wake management techniques for wind turbines D. Houck https://doi.org/10.1002/we.2668
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. https://doi.org/10.5194/wes-7-2181-2022
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. https://doi.org/10.1063/5.0039325
- Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. https://doi.org/10.5194/wes-6-1427-2021
- An analytical modeling study on yaw-based wake redirection control for large-scale offshore wind farm annual energy power improvement J. Tan et al. https://doi.org/10.1063/5.0207111
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. https://doi.org/10.3390/en14051293
- An improved wake steering optimization method for enhanced wind energy production using realistic yaw behaviour Z. Xie & Z. Lin https://doi.org/10.1016/j.renene.2026.126160
- Multifidelity multiobjective optimization for wake-steering strategies J. Quick et al. https://doi.org/10.5194/wes-7-1941-2022
- Design, Implementation and Software-in-the-Loop Validation of an Online Wake-Steering Control System P. Tona et al. https://doi.org/10.1088/1742-6596/3224/3/032141
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. https://doi.org/10.1088/1742-6596/2265/3/032110
- Reducing the number of wind turbine prototype measurement campaigns for power curve model validation using a model-validity predictor J. Murcia Leon et al. https://doi.org/10.1016/j.renene.2026.126122
- Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. https://doi.org/10.5194/wes-9-1507-2024
- Quantitative assessment on fatigue damage induced by wake effect and yaw misalignment for floating offshore wind turbines T. Tao et al. https://doi.org/10.1016/j.oceaneng.2023.116004
- A novel optimization method for maximizing wind farm performance through turbine positioning and yaw angle estimation S. Al-Rubaye & R. Gil-Pita https://doi.org/10.1016/j.enconman.2025.120546
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al. https://doi.org/10.1007/s00466-023-02272-4
- Evaluating the potential of a wake steering co-design for wind farm layout optimization through a tailored genetic algorithm M. Baricchio et al. https://doi.org/10.5194/wes-9-2113-2024
- Design-friendly wind farm control setpoint estimation via layout-agnostic graph neural networks D. Dirik et al. https://doi.org/10.1088/1742-6596/3224/3/032112
- Time-Accurate Wind Farm Control in Dynamic Flow Conditions using Deep Reinforcement Learning H. Sheehan et al. https://doi.org/10.1088/1742-6596/3016/1/012025
- Impact of Wake Expansion Assumptions on Wind Farm Layout Optimization and Cabling Trade-offs A. Baigarina et al. https://doi.org/10.1088/1742-6596/3224/3/032097
- Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation M. Lejeune et al. https://doi.org/10.1088/1742-6596/2767/9/092075
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. https://doi.org/10.5194/wes-7-345-2022
- A Numerical Investigation of the Geometric Characteristics of Floating Wind Turbine Wakes under Axial and Yawed Rotor Conditions J. Valentin & T. Sant https://doi.org/10.1088/1742-6596/2018/1/012044
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. https://doi.org/10.1063/5.0163896
- Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. https://doi.org/10.5194/wes-7-741-2022
- Experimental results of wake steering using fixed angles P. Fleming et al. https://doi.org/10.5194/wes-6-1521-2021
- Stochastic gradient descent for wind farm optimization J. Quick et al. https://doi.org/10.5194/wes-8-1235-2023
- Study of a dynamic effect-based method for wind farm yaw control optimization L. Li et al. https://doi.org/10.1080/15435075.2023.2297771
- Control-oriented modelling of wind direction variability S. Dallas et al. https://doi.org/10.5194/wes-9-841-2024
- Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm T. Tao et al. https://doi.org/10.1016/j.oceaneng.2024.117706
- Wake steering and individual turbine yaw control in large wind farms with complex inflows E. Hodgson et al. https://doi.org/10.1088/1742-6596/3224/3/032013
- Review of Mesoscale Wind-Farm Parametrizations and Their Applications J. Fischereit et al. https://doi.org/10.1007/s10546-021-00652-y
- Integer programming for optimal yaw control of wind farms F. Bestehorn et al. https://doi.org/10.5194/wes-10-1637-2025
41 citations as recorded by crossref.
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland https://doi.org/10.1063/5.0051071
- A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha https://doi.org/10.1115/1.4054501
- Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control J. Quick et al. https://doi.org/10.1088/1742-6596/3224/3/032080
- Wake steering under inflow wind direction uncertainty: an LES study E. Hodgson & S. Andersen https://doi.org/10.5194/wes-11-2173-2026
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
- Multicluster Distributed Optimization Strategy for Turbine Wake Environment Z. Yu et al. https://doi.org/10.1002/aisy.202400884
- Wind Farm Yield and Lifetime Optimization by Smart Steering of Wakes J. Schmidt et al. https://doi.org/10.1088/1742-6596/1934/1/012020
- A Machine Learning Method for Modeling Wind Farm Fatigue Load Y. Miao et al. https://doi.org/10.3390/app12157392
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Review of wake management techniques for wind turbines D. Houck https://doi.org/10.1002/we.2668
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. https://doi.org/10.5194/wes-7-2181-2022
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. https://doi.org/10.1063/5.0039325
- Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. https://doi.org/10.5194/wes-6-1427-2021
- An analytical modeling study on yaw-based wake redirection control for large-scale offshore wind farm annual energy power improvement J. Tan et al. https://doi.org/10.1063/5.0207111
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. https://doi.org/10.3390/en14051293
- An improved wake steering optimization method for enhanced wind energy production using realistic yaw behaviour Z. Xie & Z. Lin https://doi.org/10.1016/j.renene.2026.126160
- Multifidelity multiobjective optimization for wake-steering strategies J. Quick et al. https://doi.org/10.5194/wes-7-1941-2022
- Design, Implementation and Software-in-the-Loop Validation of an Online Wake-Steering Control System P. Tona et al. https://doi.org/10.1088/1742-6596/3224/3/032141
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. https://doi.org/10.1088/1742-6596/2265/3/032110
- Reducing the number of wind turbine prototype measurement campaigns for power curve model validation using a model-validity predictor J. Murcia Leon et al. https://doi.org/10.1016/j.renene.2026.126122
- Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. https://doi.org/10.5194/wes-9-1507-2024
- Quantitative assessment on fatigue damage induced by wake effect and yaw misalignment for floating offshore wind turbines T. Tao et al. https://doi.org/10.1016/j.oceaneng.2023.116004
- A novel optimization method for maximizing wind farm performance through turbine positioning and yaw angle estimation S. Al-Rubaye & R. Gil-Pita https://doi.org/10.1016/j.enconman.2025.120546
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al. https://doi.org/10.1007/s00466-023-02272-4
- Evaluating the potential of a wake steering co-design for wind farm layout optimization through a tailored genetic algorithm M. Baricchio et al. https://doi.org/10.5194/wes-9-2113-2024
- Design-friendly wind farm control setpoint estimation via layout-agnostic graph neural networks D. Dirik et al. https://doi.org/10.1088/1742-6596/3224/3/032112
- Time-Accurate Wind Farm Control in Dynamic Flow Conditions using Deep Reinforcement Learning H. Sheehan et al. https://doi.org/10.1088/1742-6596/3016/1/012025
- Impact of Wake Expansion Assumptions on Wind Farm Layout Optimization and Cabling Trade-offs A. Baigarina et al. https://doi.org/10.1088/1742-6596/3224/3/032097
- Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation M. Lejeune et al. https://doi.org/10.1088/1742-6596/2767/9/092075
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. https://doi.org/10.5194/wes-7-345-2022
- A Numerical Investigation of the Geometric Characteristics of Floating Wind Turbine Wakes under Axial and Yawed Rotor Conditions J. Valentin & T. Sant https://doi.org/10.1088/1742-6596/2018/1/012044
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. https://doi.org/10.1063/5.0163896
- Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. https://doi.org/10.5194/wes-7-741-2022
- Experimental results of wake steering using fixed angles P. Fleming et al. https://doi.org/10.5194/wes-6-1521-2021
- Stochastic gradient descent for wind farm optimization J. Quick et al. https://doi.org/10.5194/wes-8-1235-2023
- Study of a dynamic effect-based method for wind farm yaw control optimization L. Li et al. https://doi.org/10.1080/15435075.2023.2297771
- Control-oriented modelling of wind direction variability S. Dallas et al. https://doi.org/10.5194/wes-9-841-2024
- Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm T. Tao et al. https://doi.org/10.1016/j.oceaneng.2024.117706
- Wake steering and individual turbine yaw control in large wind farms with complex inflows E. Hodgson et al. https://doi.org/10.1088/1742-6596/3224/3/032013
- Review of Mesoscale Wind-Farm Parametrizations and Their Applications J. Fischereit et al. https://doi.org/10.1007/s10546-021-00652-y
- Integer programming for optimal yaw control of wind farms F. Bestehorn et al. https://doi.org/10.5194/wes-10-1637-2025
Saved (final revised paper)
Latest update: 06 Aug 2026
Short summary
We investigate the trade-offs in optimization of wake steering strategies, where upstream turbines are positioned to deflect wakes away from downstream turbines, with a probabilistic perspective. We identify inputs that are sensitive to uncertainty and demonstrate a realistic optimization under uncertainty for a wind power plant control strategy. Designing explicitly around uncertainty yielded control strategies that were generally less aggressive and more robust to the uncertain input.
We investigate the trade-offs in optimization of wake steering strategies, where upstream...
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