Articles | Volume 8, issue 8
https://doi.org/10.5194/wes-8-1341-2023
© Author(s) 2023. 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-8-1341-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2023
Research article |
| 25 Aug 2023
| 25 Aug 2023
Enabling control co-design of the next generation of wind power plants
Andrew P. J. Stanley
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
now at: Shell International Exploration and Production Inc., Houston, TX 77079, USA
Christopher J. Bay
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Paul Fleming
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
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Cited
20 citations as recorded by crossref.
- Optimizing yaw angles for improved power generation in offshore wind farms: A statistical approach I. Formoso https://doi.org/10.1016/j.oceaneng.2024.119830
- An approximation of the optimal combined helix and yaw control for wind farm co-design applications M. Baricchio et al. https://doi.org/10.1088/1742-6596/3224/3/032019
- Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients M. LoCascio et al. https://doi.org/10.1063/5.0237778
- Comparison of helix and wake steering control for varying turbine spacing and wind direction E. Taschner et al. https://doi.org/10.1088/1742-6596/2767/3/032023
- Synchronized Helix wake mixing control A. van Vondelen et al. https://doi.org/10.5194/wes-10-2411-2025
- Control Co-Design of Wind Turbines L. Pao et al. https://doi.org/10.1146/annurev-control-061423-101708
- Wake Mixing Control For Floating Wind Farms: Analysis of the Implementation of the Helix Wake Mixing Strategy on the IEA 15-MW Floating Wind Turbine D. van den Berg et al. https://doi.org/10.1109/MCS.2024.3432341
- 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
- Control co-design of a large offshore wind farm considering the effect of wind extractability M. Pahus et al. https://doi.org/10.1088/1742-6596/2767/9/092026
- 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
- Brief communication: An elliptical parameterisation of the wind direction rose E. Hart https://doi.org/10.5194/wes-10-1821-2025
- Revenue-Focused Wind Farm Control Co-Design for Future Electricity Markets Scenarios D. Dirik et al. https://doi.org/10.1088/1742-6596/3016/1/012024
- Phase controlling the yaw motion of floating wind turbines with the helix method to reduce wake interactions: an experimental investigation D. van den Berg et al. https://doi.org/10.5194/wes-11-679-2026
- Optimal wind farm energy and reserve scheduling incorporating wake interactions M. Mabboux-Fort et al. https://doi.org/10.1016/j.apenergy.2026.127815
- How do wind farm layout design, control and co-design optimization compare in mitigating external and internal wake effects? S. Kainz et al. https://doi.org/10.1088/1742-6596/3224/3/032039
- Machine learning to rapidly predict turbine yaw angles for wake steering A. Stanley et al. https://doi.org/10.1088/1742-6596/2767/8/082011
- Wind turbine control co-design using dynamic system derivative function surrogate model (DFSM) based on OpenFAST linearization Y. Lee et al. https://doi.org/10.1016/j.apenergy.2025.126203
- Advancing wind turbines through control co-design: An integrative review S. Bayat et al. https://doi.org/10.1016/j.apenergy.2026.127951
- Risk-averse wake steering optimization for energy and power maximization under uncertain wind direction changes M. Becker & J. Wingerden https://doi.org/10.1088/1742-6596/3224/3/032124
- Reductions in wind farm main bearing rating lives resulting from wake impingement J. Quick et al. https://doi.org/10.5194/wes-11-493-2026
20 citations as recorded by crossref.
- Optimizing yaw angles for improved power generation in offshore wind farms: A statistical approach I. Formoso https://doi.org/10.1016/j.oceaneng.2024.119830
- An approximation of the optimal combined helix and yaw control for wind farm co-design applications M. Baricchio et al. https://doi.org/10.1088/1742-6596/3224/3/032019
- Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients M. LoCascio et al. https://doi.org/10.1063/5.0237778
- Comparison of helix and wake steering control for varying turbine spacing and wind direction E. Taschner et al. https://doi.org/10.1088/1742-6596/2767/3/032023
- Synchronized Helix wake mixing control A. van Vondelen et al. https://doi.org/10.5194/wes-10-2411-2025
- Control Co-Design of Wind Turbines L. Pao et al. https://doi.org/10.1146/annurev-control-061423-101708
- Wake Mixing Control For Floating Wind Farms: Analysis of the Implementation of the Helix Wake Mixing Strategy on the IEA 15-MW Floating Wind Turbine D. van den Berg et al. https://doi.org/10.1109/MCS.2024.3432341
- 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
- Control co-design of a large offshore wind farm considering the effect of wind extractability M. Pahus et al. https://doi.org/10.1088/1742-6596/2767/9/092026
- 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
- Brief communication: An elliptical parameterisation of the wind direction rose E. Hart https://doi.org/10.5194/wes-10-1821-2025
- Revenue-Focused Wind Farm Control Co-Design for Future Electricity Markets Scenarios D. Dirik et al. https://doi.org/10.1088/1742-6596/3016/1/012024
- Phase controlling the yaw motion of floating wind turbines with the helix method to reduce wake interactions: an experimental investigation D. van den Berg et al. https://doi.org/10.5194/wes-11-679-2026
- Optimal wind farm energy and reserve scheduling incorporating wake interactions M. Mabboux-Fort et al. https://doi.org/10.1016/j.apenergy.2026.127815
- How do wind farm layout design, control and co-design optimization compare in mitigating external and internal wake effects? S. Kainz et al. https://doi.org/10.1088/1742-6596/3224/3/032039
- Machine learning to rapidly predict turbine yaw angles for wake steering A. Stanley et al. https://doi.org/10.1088/1742-6596/2767/8/082011
- Wind turbine control co-design using dynamic system derivative function surrogate model (DFSM) based on OpenFAST linearization Y. Lee et al. https://doi.org/10.1016/j.apenergy.2025.126203
- Advancing wind turbines through control co-design: An integrative review S. Bayat et al. https://doi.org/10.1016/j.apenergy.2026.127951
- Risk-averse wake steering optimization for energy and power maximization under uncertain wind direction changes M. Becker & J. Wingerden https://doi.org/10.1088/1742-6596/3224/3/032124
- Reductions in wind farm main bearing rating lives resulting from wake impingement J. Quick et al. https://doi.org/10.5194/wes-11-493-2026
Saved (final revised paper)
Latest update: 16 Aug 2026
Short summary
Better wind farms can be built by simultaneously optimizing turbine locations and control, which is currently impossible or extremely challenging because of the size of the problem. The authors present a method to determine optimal wind farm control as a function of the turbine locations, which enables turbine layout and control to be optimized together by drastically reducing the size of the problem. In an example, a wind farm's performance improves by 0.8 % when optimized with the new method.
Better wind farms can be built by simultaneously optimizing turbine locations and control, which...
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