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The Wind Farm Layout Optimization Problem

  • Michele SamoraniEmail author
Chapter
Part of the Energy Systems book series (ENERGY)

Abstract

An important phase of a wind farm design is solving the Wind Farm Layout Optimization Problem (WFLOP), which consists in optimally positioning the turbines within the wind farm so that the wake effects are minimized and therefore the expected power production maximized. Although this problem has been receiving increasing attention from the scientific community, the existing approaches do not completely respond to the needs of a wind farm developer, mainly because they do not address construction and logistical issues. This chapter describes the WFLOP, gives an overview on the existing work, and discusses the challenges that may be overcome by future research.

Keywords

Wind farm layout Wake effects Wind turbine Optimization Genetic algorithms 

Notes

Acknowledgments

This work has been made possible thanks to the generosity of Mr. John Callies and a IBM Shared University Research (SUR) Award.

References

  1. 1.
    Méchali M, Barthelemie R, Frandsen S, Jensen L, Rethoré P et al (2006) Wake effects at horns rev and their influence on energy production. In: European wind energy conference and exhibition, AthensGoogle Scholar
  2. 2.
    Aytun Ozturk U, Norman B (2004) Heuristic methods for wind energy conversion system positioning. Electr Power Syst Res 70:179–185CrossRefGoogle Scholar
  3. 3.
    Grady SA, Hussaini MY, Abdullah MM (2005) Placement of wind turbines using genetic algorithms. Renew Energy 30:259–270CrossRefGoogle Scholar
  4. 4.
    Hou-Sheng H (2007) Distributed Genetic Algorithm for optimization of wind farm annual profits. In: International conference on intelligent systems applications to power systems, Kaohsiung, TaiwanGoogle Scholar
  5. 5.
    Kusiak A, Song Z (2009) Design of wind farm layout for maximum wind energy capture. Renewable Energy 35:685–694CrossRefGoogle Scholar
  6. 6.
    Mosetti G, Poloni C, Diviacco D (1994) Optimization of wind turbine positioning in large wind farms by means of a Genetic algorithm. J Wind Eng Ind Aerody 51:105–116CrossRefGoogle Scholar
  7. 7.
    Rivas RA, Clausen J, Hansen KS et al (2009) Solving the turbine positioning problem for large offshore wind farms by simulated annealing. Wind Eng 33:287–297CrossRefGoogle Scholar
  8. 8.
    Şişbot S, Turgut Ö, Tunç M et al (2010). Optimal positioning of wind turbines on Gökçeada using multi-objective genetic algorithm. Wind Energy 13:297–306Google Scholar
  9. 9.
    Petersen EL, Mortensen NG, Landberg L et al (1998) Wind power meteorology. Part I: climate and turbulence. Wind Energy 1:25–45CrossRefGoogle Scholar
  10. 10.
    Kelley ND, Jonkman BJ, Scott GN, et al (2007) Comparing pulsed doppler LIDAR with SODAR and direct measurements for wind assessment. In: American Wind Energy Association wind power 2007 conference and exhibition. Los Angeles, CaliforniaGoogle Scholar
  11. 11.
    Frehlich R, Kelley N (2010) Applications of scanning Doppler Lidar for the wind energy industry. The 90th American meteorological society annual meeting. Atlanta, GAGoogle Scholar
  12. 12.
    Ainslie JF (1988) Calculating the flow field in the wake of wind turbines. J Wind Eng Ind Aerodyn 27:213–224CrossRefGoogle Scholar
  13. 13.
    Vermeer LJ, Sørensen JN, Crespo A (2003) Wind turbine wake aerodynamics. Prog Aerosp Sci 39:467–510CrossRefGoogle Scholar
  14. 14.
    Wilcox DC (1998) Turbulence Modeling for CFD. La Canada, DCW Industries, CAGoogle Scholar
  15. 15.
    Jensen NO (1983) A note on wind generator interaction. Risø DTU national laboratory for sustainable energyGoogle Scholar
  16. 16.
    Katic I, Højstrup J, Jensen NO (1986) A simple model for cluster efficiency. In: Europe and Wind Energy Association conference and exhibition, Rome, ItalyGoogle Scholar
  17. 17.
    Katic I (1993) Program PARK, calculation of wind turbine park performance. Release 1.3 ++, Risø National Laboratory, RosklideGoogle Scholar
  18. 18.
    Barthelmie R, Larsen G, Pryor H et al (2004) ENDOW (efficient development of offshore wind farms): modelling wake and boundary layer interactions. Wind Energy 7:225–245CrossRefGoogle Scholar
  19. 19.
    Barthelmie R, Folkerts L, Larsen GC et al (2006) Comparison of wake model simulations with offshore wind turbine wake profiles measured by Sodar. J Atmos Oceanic Technol 23:888–901CrossRefGoogle Scholar
  20. 20.
    Lackner MA, Elkinton CN (2007) An analytical framework for offshore wind farm layout optimization. Wind Eng 31:17–31CrossRefGoogle Scholar
  21. 21.
    Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley Longman Publishing Co. Inc, BostonzbMATHGoogle Scholar
  22. 22.
    Brooks SP, Morgan BJ (1995) Optimization using simulated annealing. J R Stat Soc D (The Statistician) 44:241–257 Google Scholar
  23. 23.
    Manwell JF, McGowan JG, Rogers AL (2002) Wind Energy Explained. Wiley, West SussexCrossRefGoogle Scholar
  24. 24.
    Garcia A, Torres JL, Prieto E et al (1998) Fitting wind speed distributions: a case study. Sol Energy 62:139–144CrossRefGoogle Scholar
  25. 25.
    Donovan S (2005) Wind farm optimization. University of Auckland, New ZealandGoogle Scholar
  26. 26.
    Crespo A, Hernandez J (1996) Turbulence characteristics in wind-turbine wakes. J Wind Eng Ind Aerody 61:71–85CrossRefGoogle Scholar
  27. 27.
    Kelley, ND, Sutherland HJ (1997) Damage estimates from long-term structural analysis of a wind turbine in a U.S. wind farm environment. In: Prepared for the 1997 ASME Wind Energy Symposium, Reno, Nevada. NREL/CP-440-21672, Golden, CO: National Renewable Energy Laboratory. pp 12Google Scholar
  28. 28.
    Burton T, Sharpe D, Jenkins N et al (2001) Wind energy handbook. Wiley, New YorkCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Alberta School of BusinessUniversity of AlbertaEdmontonCanada

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