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Fast optimisation of tidal stream turbine positions for power generation in small arrays with low blockage based on superposition of self-similar far-wake velocity deficit profiles

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  • Stansby, Peter
  • Stallard, Tim

Abstract

Far wake velocities of a single horizontal axis three-bladed turbine in shallow flow have been measured previously in the laboratory and shown to have self-similar velocity deficit profiles. Wake velocities of arrays of turbines with one, two and three transverse rows have also been measured and simply superimposing the velocity deficits for a single turbine is shown to give accurate prediction of combined wake width and velocity deficit, accounting for variable downstream blockage through volume flux conservation. Array efficiency is defined as the ratio of total power generated to what would be generated by the same turbines in isolation. From prescribed initial turbine positions, generally determined intuitively or by practical considerations, adjusting the turbine positions to increase the power from each turbine, using the chain rule, shows that relatively small movements of 3–4 rotor diameters may increase array efficiency to over 90%.

Suggested Citation

  • Stansby, Peter & Stallard, Tim, 2016. "Fast optimisation of tidal stream turbine positions for power generation in small arrays with low blockage based on superposition of self-similar far-wake velocity deficit profiles," Renewable Energy, Elsevier, vol. 92(C), pages 366-375.
    • Handle: RePEc:eee:renene:v:92:y:2016:i:c:p:366-375
    DOI: 10.1016/j.renene.2016.02.019
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    References listed on IDEAS

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    1. Malki, Rami & Masters, Ian & Williams, Alison J. & Nick Croft, T., 2014. "Planning tidal stream turbine array layouts using a coupled blade element momentum – computational fluid dynamics model," Renewable Energy, Elsevier, vol. 63(C), pages 46-54.
    2. Funke, S.W. & Farrell, P.E. & Piggott, M.D., 2014. "Tidal turbine array optimisation using the adjoint approach," Renewable Energy, Elsevier, vol. 63(C), pages 658-673.
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    5. Lo Brutto, Ottavio A. & Thiébot, Jérôme & Guillou, Sylvain S. & Gualous, Hamid, 2016. "A semi-analytic method to optimize tidal farm layouts – Application to the Alderney Race (Raz Blanchard), France," Applied Energy, Elsevier, vol. 183(C), pages 1168-1180.
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    7. González-Gorbeña, Eduardo & Qassim, Raad Y. & Rosman, Paulo C.C., 2018. "Multi-dimensional optimisation of Tidal Energy Converters array layouts considering geometric, economic and environmental constraints," Renewable Energy, Elsevier, vol. 116(PA), pages 647-658.
    8. Santos, Ivan Felipe Silva dos & Camacho, Ramiro Gustavo Ramirez & Tiago Filho, Geraldo Lúcio & Botan, Antonio Carlos Barkett & Vinent, Barbara Amoeiro, 2019. "Energy potential and economic analysis of hydrokinetic turbines implementation in rivers: An approach using numerical predictions (CFD) and experimental data," Renewable Energy, Elsevier, vol. 143(C), pages 648-662.
    9. Topper, Mathew B.R. & Olson, Sterling S. & Roberts, Jesse D., 2021. "On the benefits of negative hydrodynamic interactions in small tidal energy arrays," Applied Energy, Elsevier, vol. 297(C).
    10. Chen, Long & Yao, Yu & Wang, Zhi-liang, 2020. "Development and validation of a prediction model for the multi-wake of tidal stream turbines," Renewable Energy, Elsevier, vol. 155(C), pages 800-809.
    11. Lo Brutto, Ottavio A. & Nguyen, Van Thinh & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2016. "Tidal farm analysis using an analytical model for the flow velocity prediction in the wake of a tidal turbine with small diameter to depth ratio," Renewable Energy, Elsevier, vol. 99(C), pages 347-359.
    12. Ross, Hannah & Polagye, Brian, 2020. "An experimental assessment of analytical blockage corrections for turbines," Renewable Energy, Elsevier, vol. 152(C), pages 1328-1341.
    13. Hachmann, Christoph & Stallard, Tim & Stansby, Peter & Lin, Binliang, 2021. "Experimentally validated study of the impact of operating strategies on power efficiency of a turbine array in a bi-directional tidal channel," Renewable Energy, Elsevier, vol. 163(C), pages 1408-1426.
    14. Karina Soto-Rivas & David Richter & Cristian Escauriaza, 2019. "A Formulation of the Thrust Coefficient for Representing Finite-Sized Farms of Tidal Energy Converters," Energies, MDPI, vol. 12(20), pages 1-17, October.
    15. Liu, Heng-xu & Tian, Yi-nong & Liu, Wei-qi & Jin, Ye-qing & Kong, Fan-kai & Chen, Hai-long & Zhong, Yu-guang, 2023. "Aerodynamic interference characteristics of multiple unit wind turbine based on vortex filament wake model," Energy, Elsevier, vol. 268(C).
    16. Goss, Z.L. & Coles, D.S. & Kramer, S.C. & Piggott, M.D., 2021. "Efficient economic optimisation of large-scale tidal stream arrays," Applied Energy, Elsevier, vol. 295(C).
    17. Patel, Vimal & Eldho, T.I. & Prabhu, S.V., 2019. "Velocity and performance correction methodology for hydrokinetic turbines experimented with different geometry of the channel," Renewable Energy, Elsevier, vol. 131(C), pages 1300-1317.
    18. Lo Brutto, Ottavio A. & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2017. "Assessing the effectiveness of a global optimum strategy within a tidal farm for power maximization," Applied Energy, Elsevier, vol. 204(C), pages 653-666.

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