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Tackling wind theft through interdisciplinary collaboration

As wind farms multiply, wake effects from upwind wind farms reduce downwind output, a phenomenon known as ‘wind theft’ that costs billions and sparks disputes. Interdisciplinary collaboration can address this growing challenge.

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References

  1. Lundquist, J. K. et al. Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development. Nat. Energy 4, 26–34 (2019).

    Article  Google Scholar 

  2. Sanderson, C. RWE, Orsted, and SSE clash in wind theft hearing over huge offshore projects. Recharge https://go.nature.com/4qQ6Ojh (2025).

  3. The University of Manchester. The University of Manchester to lead national review of offshore wind farm projects; https://go.nature.com/40ixSN3 (12 March 2025).

  4. EuroWindWakes: Multiscale modelling of European wind energy wake effects. Fraunhofer IWES https://go.nature.com/4tGXy3u (2025).

  5. Khan, M. A., Javed, A., Shakir, S. & Syed, A. H. Optimization of a wind farm by coupled actuator disk and mesoscale models to mitigate neighboring wind farm wake interference from repowering perspective. Appl. Energy 298, 117229 (2021).

    Article  Google Scholar 

  6. Tao, S. & Feijóo-Lorenzo, A. E. Optimal layout of offshore wind farm cluster: A three-stage game model with priori coalition. IEEE Trans. Ind. Inf. 20, 9225–9234 (2024).

    Article  Google Scholar 

  7. Fischereit, J. et al. Review of mesoscale wind-farm parametrizations and their applications. Bound. Layer Meteorol. 182, 175–224 (2022).

    Article  Google Scholar 

  8. Zhang, J. & Zhao, X. Digital twin of wind farms via physics-informed deep learning. Energy Convers. Manag. 293, 117507 (2023).

    Article  Google Scholar 

  9. Howland, M. F. et al. Collective wind farm operation based on a predictive model increases utility-scale energy production. Nat. Energy 7, 818–827 (2022).

    Article  Google Scholar 

  10. Finserås, E. et al. Gone with the wind? Wind farm-induced wakes and regulatory gaps. Marine Policy 159, 105897 (2024).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant 52477102) and the Excellent Youth Basic Research Fund of Shenzhen (grant RCYX20231211090430053).

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Correspondence to Yunfei Du or Xinwei Shen.

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Du, Y., Ding, X., Hou, P. et al. Tackling wind theft through interdisciplinary collaboration. Nat Rev Electr Eng (2026). https://doi.org/10.1038/s44287-026-00275-w

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