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Wind farm power optimization including flow variability

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  • Herp, Jürgen
  • Poulsen, Uffe V.
  • Greiner, Martin

Abstract

A model-based optimisation approach is used to investigate the potential gain of wind-farm power with a cooperative control strategy between the wind turbines. Based on the Jensen wake model with the Katic wake superposition rule, the potential gain for the Nysted offshore wind farm is calculated to be 1.4–5.4% for standard choices 0.4 ≥ k ≥ 0.25 of the wake expansion parameter. Wake model fits based on short time intervals of length 15sec ≤ T ≤ 10 min within three months of data reveal a strong wake flow variability, resulting in rather broad distributions for the wake expansion parameter. When an optimized wind-farm control strategy, derived from a fixed wake parameter, is facing this flow variability, the potential gain reduces to 0.3–0.5%. An omnipotent control strategy, which has real-time knowledge of the actual wake flow, would be able to increase the gain in wind-farm power to 4.9%.

Suggested Citation

  • Herp, Jürgen & Poulsen, Uffe V. & Greiner, Martin, 2015. "Wind farm power optimization including flow variability," Renewable Energy, Elsevier, vol. 81(C), pages 173-181.
    • Handle: RePEc:eee:renene:v:81:y:2015:i:c:p:173-181
    DOI: 10.1016/j.renene.2015.03.034
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    References listed on IDEAS

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    2. Huang, Yu & Zhang, Bingzhe & Pang, Huizhen & Wang, Biao & Lee, Kwang Y. & Xie, Jiale & Jin, Yupeng, 2022. "Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion," Renewable Energy, Elsevier, vol. 192(C), pages 526-536.
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    6. Chen, C. & Li, Y.P. & Huang, G.H., 2016. "Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management – A case study of Tianjin, China," Renewable Energy, Elsevier, vol. 86(C), pages 1161-1177.

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