Achieving Power-Noise Balance in Wind Farms by Fine-Tuning the Layout with Reinforcement Learning

Guangxing Guo, Weijun Zhu (), Ziliang Zhang, Wenzhong Shen and Zhe Chen
Additional contact information
Guangxing Guo: College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
Weijun Zhu: College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, China
Ziliang Zhang: Science and Technology Research Institute, China Three Gorges Corporation, Beijing 101199, China
Wenzhong Shen: College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, China
Zhe Chen: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark

Energies, 2025, vol. 18, issue 18, 1-18

Abstract: Wind farms situated in proximity to residential areas present environmental challenges, primarily due to noise emissions. Rectangular and parallelogram layouts are commonly employed in current wind farm designs owing to their simplicity and visual appeal. However, such configurations often experience significant power loss under certain wind directions because of intense wake interactions. This paper proposes a layout fine-tuning strategy for low-noise wind farm design. Within a reinforcement learning framework integrated with an engineering wake model and a noise propagation model, the positions of two turbines (controlled by two variables) are optimized. The noise propagation model was validated for idealized long-range sound propagation over flat terrain with acoustically soft surfaces. A case study was conducted on a 12-turbine wind farm located on a flat plain in China, with a noise threshold of 45 dB(A) used to assess the noise impact area. Optimization results demonstrate that the proposed method achieves a balance between power output and noise reduction compared to the original regular layout: Annual Energy Production (AEP) increased slightly by 0.16%, while the noise impact area was reduced by 6.0%. Although these improvements appear modest, the potential of the proposed methodology warrants further investigation.

Keywords: wind farm layout; wind farm noise; power production; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/18/5019/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/18/5019/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:18:p:5019-:d:1754482

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().