Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

Article, Other literature type OPEN
Ju Feng ; Wen Zhong Shen (2015)
  • Publisher: Multidisciplinary Digital Publishing Institute
  • Journal: Energies, volume 8, issue 4 4, pages 1-18 (issn: 1996-1073)
  • Related identifiers: doi: 10.3390/en8043075
  • Subject: SEARCH ALGORITHM | joint distribution | ENERGY ANALYSIS | DESIGN | ENERGY | TURBINES | wind modelling | wind speed | wind direction | Technology | layout optimization | T | layout optimization; wind modelling; wind speed; wind direction; joint distribution; sector-wise Weibull distribution | sector-wise Weibull distribution | PLACEMENT
    • jel: jel:Q0 | jel:Q | jel:Q4 | jel:Q47 | jel:Q49 | jel:Q48 | jel:Q43 | jel:Q42 | jel:Q41 | jel:Q40

Reliable wind modelling is of crucial importance for wind farm development. The common practice of using sector-wise Weibull distributions has been found inappropriate for wind farm layout optimization. In this study, we propose a simple and easily implementable method to construct joint distributions of wind speed and wind direction, which is based on the parameters of sector-wise Weibull distributions and interpolations between direction sectors. It is applied to the wind measurement data at Horns Rev and three different joint distributions are obtained, which all fit the measurement data quite well in terms of the coefficient of determination . Then, the best of these joint distributions is used in the layout optimization of the Horns Rev 1 wind farm and the choice of bin sizes for wind speed and wind direction is also investigated. It is found that the choice of bin size for wind direction is especially critical for layout optimization and the recommended choice of bin sizes for wind speed and wind direction is finally presented.
  • References (19)
    19 references, page 1 of 2

    Energy Rev. 2009, 13, 1288-1300.

    Herbert-Acero, J.F.; Probst, O.; Réthoré, P.E.; Larsen, G.C.; Castillo-Villar, K.K. A review of methodological approaches for the design and optimization of wind farms. Energies 2014, 7, 6930-7016.

    Mosetti, S.G.; Poloni, C.; Diviacco, B. Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J. Wind Eng. Ind. Aerodyn. 1994, 51, 105-116.

    Grady, S.A.; Hussaini, M.Y.; Abdullah, M.M. Placement of wind turbines using genetic algorithms. Renew. Energy 2005, 30, 259-270.

    Wan, C.; Wang, J.; Yang, G.; Gu, H.; Zhang, X. Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy. Renew. Energy 2012, 48, 276-286.

    J. Mech. Des. 2012, 134, 081002.

    Carta, J.A.; Ramirez, P.; Velazquez, S. A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands. Renew. Sustain. Energy Rev. 2009, 13, 933-955.

    Rivas, R.A.; Clausen, J.; Hansen, K.S.; Jensen, L.E. Solving the turbine positioning problem for large offshore wind farms by simulated annealing. Wind Eng. 2009, 33, 287-297.

    Dobrić, G.; Đurišić, Ž. Double-stage genetic algorithm for wind farm layout optimization on complex terrains. J. Renew. Sustain. Energy 2014, 6, 033127.

    10. Kusiak, A.; Song, Z. Design of wind farm layout for maximum wind energy capture. Renew. Energy 2010, 35, 685-694.

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