Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model

Applied Energy

Volumn 174, Issue , 2016, Pages 192-200

  Gao, Xiaoxia ^a   Yang, Hongxing ^a   Lu, Lin ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

2D wake model; Multiple populations genetic algorithm; Turbulence model; Validation; Wake characteristics; Wind turbine layout optimization

Indexed keywords

ELECTRIC POWER SYSTEM INTERCONNECTION; ELECTRIC UTILITIES; ENERGY UTILIZATION; GAUSSIAN DISTRIBUTION; GENETIC ALGORITHMS; OPTIMIZATION; TURBULENCE MODELS; WIND; WIND POWER; WIND TUNNELS; WIND TURBINES;

DEVELOPMENT AND APPLICATIONS; GAUSSIAN FUNCTIONS; MULTIPLE POPULATIONS; TURBINE LAYOUTS; VALIDATION; VELOCITY DEFICITS; WAKE MODEL; WIND TURBINE WAKES;

WAKES;

DATA ACQUISITION; EFFICIENCY MEASUREMENT; EXPERIMENTAL STUDY; GAUSSIAN METHOD; GENETIC ALGORITHM; OPTIMIZATION; PREDICTION; TWO-DIMENSIONAL FLOW; WAKE; WIND FARM; WIND TURBINE;

EID: 84964530039     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.04.098     Document Type: Article

References (50)

1. Barthelmie R.J., et al. Modelling and measuring flow and wind turbine wakes in large wind farms offshore. Wind Energy 2009, 12(5):431-444.
2. Sanderse B. Aerodynamics of wind turbine wakes. Energy Research Center of the Netherlands (ECN), ECN-E-09-016, Petten, The Netherlands, Tech. Rep; 2009.
3. Park J., Law K.H. Layout optimization for maximizing wind farm power production using sequential convex programming. Appl Energy 2015, 151:320-334.
4. Castellani F., et al. How wind turbines alignment to wind direction affects efficiency? A case study through SCADA data mining. Energy Proc 2015, 75:697-703.
5. Grassi S., Junghans S., Raubal M. Assessment of the wake effect on the energy production of onshore wind farms using GIS. Appl Energy 2014, 136:827-837.
6. Vermeer L.J., Sørensen J.N., Crespo A. Wind turbine wake aerodynamics. Prog Aerosp Sci 2003, 39(6-7):467-510.
7. Barthelmie R.J., Pryor S.C. An overview of data for wake model evaluation in the virtual wakes laboratory. Appl Energy 2013, 104:834-844.
8. Xydis G., Koroneos C., Loizidou M. Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: a case study in Southern Greece. Appl Energy 2009, 86(11):2411-2420.
9. Crespo A., Hernandez J., Frandsen S. Survey of modelling methods for wind turbine wakes and wind farms. Wind Energy 1999, 2(1):1-24.
10. Katic I, Højstrup J, Jensen N. A simple model for cluster efficiency. In: European wind energy association conference and exhibition; 1986.
11. Jensen N.O. A note on wind generator interaction 1983, Risø National Laboratory, Roskilde, Denmark.
12. Frandsen S. On the wind speed reduction in the center of large clusters of wind turbines. J Wind Eng Ind Aerodyn 1992, 39(1-3):251-265.
13. Barthelmie R.J., et al. Comparison of wake model simulations with offshore wind turbine wake profiles measured by Sodar. J Atmos Ocean Technol 2006, 23(7):888-901.
14. Barthelmie R.J., Jensen L. Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm. Wind Energy 2010, 13(6):573-586.
15. Cleve J., et al. Model-based analysis of wake-flow data in the Nysted offshore wind farm. Wind Energy 2009, 12(2):125-135.
16. Barthelmie R.J., et al. Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm. Wind Energy 2007, 10(6):517-528.
17. Porté-Agel F., Wu Y.-T., Chen C.-H. A numerical study of the effects of wind direction on turbine wakes and power losses in a large wind farm. Energies 2013, 6(10):5297-5313.
18. Shakoor R., et al. Wake effect modeling: a review of wind farm layout optimization using Jensen's model. Renew Sustain Energy Rev 2016, 58:1048-1059.
19. Grady S.A., Hussaini M.Y., Abdullah M.M. Placement of wind turbines using genetic algorithms. Renew Energy 2005, 30(2):259-270.
20. Emami A., Noghreh P. New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renew Energy 2010, 35(7):1559-1564.
21. Mittal A. Optimization of the layout of large wind farms using a genetic algorithm 2010, Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland.
22. Rajper S., Amin I.J. Optimization of wind turbine micrositing: a comparative study. Renew Sustain Energy Rev 2012, 16(8):5485-5492.
23. Mosetti G., Poloni C., Diviacco B. Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J Wind Eng Ind Aerodyn 1993, (51).
24. Lackner Matthew A., Elkinton C.N. An analytical framework for offshore wind farm layout optimization. Wind Eng 2007, 31.
25. Marmidis G., Lazarou S., Pyrgioti E. Optimal placement of wind turbines in a wind park using Monte Carlo simulation. Renew Energy 2008, 33(7):1455-1460.
26. Kusiak A., Zhe S. Design of wind farm layout for maximum wind energy capture. Renew Energy 2010, 35(3):685-694.
27. González J.S., et al. Optimization of wind farm turbines layout using an evolutive algorithm. Renew Energy 2010, 35(8):1671-1681.
28. Ituarte-Villarreal C.M., Espiritu J.F. Optimization of wind turbine placement using a viral based optimization algorithm. Proc Comput Sci 2011, 6:469-474.
29. Saavedra-Moreno B., et al. Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms. Renew Energy 2011, 36(11):2838-2844.
30. Eroğlu Y., Seçkiner S.U. Design of wind farm layout using ant colony algorithm. Renew Energy 2012, 44:53-62.
31. Padmanabhan K.K., Saravanan R. Wind park layout design optimization using GA. Int J Eng Res Technol 2012, 5.
32. Pookpunt S., Ongsakul W. Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients. Renew Energy 2013, 55:266-276.
33. Benatiallah L.K., Dakyo B. Modeling and optimization of wind energy systems. Jordan J Mech Ind Eng 2010, 4(1):143-150.
34. Kusiak A., Verma A., Wei X. Wind turbine frontier from SCADA. Wind Syst Mag 2012, 3(9):36-39.
35. Gao X., et al. Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshore. J Wind Eng Ind Aerodyn 2015, 139:89-99.
36. Dufresne N.P., Wosnik M. Velocity deficit and swirl in the turbulent wake of a wind turbine. Mar Technol Soc J 2013, 47(4):193-205.
37. Chamorro L.P., Porté-Agel F. A wind-tunnel investigation of wind-turbine wakes: boundary-layer turbulence effects. Bound-Layer Meteorol 2009, 132(1):129-149.
38. Tian L., et al. Development and validation of a new two-dimensional wake model for wind turbine wakes. J Wind Eng Ind Aerodyn 2015, 137:90-99.
39. Wikipedia. Normal distribution; 2015; Available from: <>. http://en.wikipedia.org/wiki/Normal_distribution.
40. WIKIPEDIA. Offshore wind power; 2015 [cited 2015].
41. Schlez W., Tindal A., Quarton D. GH wind farmer validation report 2003, Garrad Hassan and Partners Ltd., Bristol.
42. Politis E., et al. Modeling wake effects in large wind farms in complex terrain: the problem, the methods and the issues. Wind Energy 2012, 15(1):161-182.
43. Mittal A., et al. Investigation of two analytical wake models using data from wind farms. ASME 2011 international mechanical engineering congress and exposition 2011, American Society of Mechanical Engineers.
44. Ishihara T, Yamaguchi A, Fujino Y. Development of a new wake model based on a wind tunnel experiment. Global wind power; 2004.
45. Crespo A., Hernández J. Turbulence characteristics in wind-turbine wakes. J Wind Eng Ind Aerodyn 1996, 61(1):71-85.
46. Frandsen ST et al. Measurements on and modelling of offshore wind farms; 1996.
47. Taylor G. Wake measurements on the Nibe wind turbines in Denmark 1990, Atomic Energy Research Establishment, Energy Technology Support Unit.
48. Zhang C., Hou G., Wang J. A fast algorithm based on the submodular property for optimization of wind turbine positioning. Renew Energy 2011, 36(11):2951-2958.
49. Wan C., et al. Optimal micro-siting of wind farms by particle swarm optimization. Advances in swarm intelligence 2010, 198-205. Springer.
50. Wan C et al. Optimal siting of wind turbines using real-coded genetic algorithms. In: Proceedings of European wind energy association conference and exhibition; 2009.