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Optimisation of wind farm layout in complex terrain via mixed-installation of different types of turbines

Optimisation of wind farm layout in complex terrain via mixed-installation of different types of turbines

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As wind energy is increasingly exploited worldwide, optimisation of wind farm layout becomes more crucial. To guarantee the economic efficiency and profit of a wind farm, the deployment of wind turbines has to be optimised before operation. Traditional methods usually assume that identical type of wind turbines are utilised in a layout design. In this study, multiple types of turbines are considered in wind farm layout optimisation in complex terrain, namely mixed-installation. By utilising different power generation characteristics, hub heights and rotor diameters, and cost models of different types of turbines, the efficiency of a wind farm can be further improved. A single-objective optimisation problem is firstly established by modelling all aforementioned factors, and the objective is to achieve a minimum cost per unit of energy. Subsequently, after using computational fluid dynamics to simulate the wind flow over complex terrain, a genetic algorithm-particle swarm optimisation optimisation algorithm is then proposed to determine the position and type of every individual turbine simultaneously. Eventually, extensive simulation studies are present to verify the feasibility of this scheme.

References

    1. 1)
      • 1. ‘Wind power capacity reaches 539 gw, 52,6 gw added in 2017’. Available at http://www.wwindea.org/2017-statistics/.
    2. 2)
      • 2. Ghaith, A.F., Epplin, F.M., Frazier, R.S.: ‘Economics of household wind turbine grid-tied systems for five wind resource levels and alternative grid pricing rates’, Renew. Energy, 2017, 109, pp. 155167.
    3. 3)
      • 3. Sagbansua, L., Balo, F.: ‘Decision making model development in increasing wind farm energy efficiency’, Renew. Energy, 2017, 109, pp. 354362.
    4. 4)
      • 4. Wang, H., Chen, Z., Jiang, Q.: ‘Optimal control method for wind farm to support temporary primary frequency control with minimised wind energy cost’, IET Renew. Power Gener., 2014, 9, (4), pp. 350359.
    5. 5)
      • 5. Herbert-Acero, J.F., Probst, O., Réthoré, P.-E., et al: ‘A review of methodological approaches for the design and optimization of wind farms’, Energies, 2014, 7, (11), pp. 69307016.
    6. 6)
      • 6. Jackson, P.S., Hunt, J.C.R.: ‘Turbulent wind flow over a low hill’, Q. J. R. Meteorol. Soc., 2010, 101, (430), pp. 929955.
    7. 7)
      • 7. Hunt, J.C.R., Leibovich, S., Richards, K.J.: ‘Turbulent shear flows over low hills’, Q. J. R. Meteorol. Soc., 2010, 114, (484), pp. 14351470.
    8. 8)
      • 8. Schmidt, J., Stoevesandt, B.: ‘The impact of wake models on wind farm layout optimization’, J. Phys., Conf. Ser., 2015, 625, p. 012040.
    9. 9)
      • 9. Wang, L., Tan, A.C.C., Cholette, M.E., et al: ‘Optimization of wind farm layout with complex land divisions’, Renew. Energy, 2017, 105, pp. 3040.
    10. 10)
      • 10. Palma, J.M.L.M., Castro, F.A., Ribeiro, L.F., et al: ‘Linear and nonlinear models in wind resource assessment and wind turbine micro-siting in complex terrain’, J. Wind Eng. Ind. Aerodyn., 2008, 96, (12), pp. 23082326.
    11. 11)
      • 11. Feng, J., Shen, W.Z.: ‘Wind farm layout optimization in complex terrain: A preliminary study on a Gaussian hill’, J. Phys., Conf. Ser., 2014, 524, p. 012146.
    12. 12)
      • 12. ‘Windsim’. Available at http://www.windsim.com/.
    13. 13)
      • 13. ‘Meteodyn meteorology & dynamics’. Available at http://meteodyn.com/.
    14. 14)
      • 14. Schmidt, J., Stoevesandt, B.: ‘Modelling complex terrain effects for wind farm layout optimization’, J. Phys. Conf. Ser., 2014, 524, p. 012136.
    15. 15)
      • 15. Song, M.X., Chen, K., He, Z.Y., et al: ‘Optimization of wind farm micro-siting for complex terrain using greedy algorithm’, Energy, 2014, 67, (4), pp. 454459.
    16. 16)
      • 16. Yan, B.W., Li, Q.S.: ‘Coupled on-site measurement/CFD based approach for highresolution wind resource assessment over complex terrains’, Energy Convers. Manage., 2016, 117, pp. 351366.
    17. 17)
      • 17. Chowdhury, S., Zhang, J., Messac, A., et al: ‘Unrestricted wind farm layout optimization (UWFLO): investigating key factors influencing the maximum power generation’, Renew. Energy, 2012, 38, (1), pp. 1630.
    18. 18)
      • 18. Ruszczyński, A.P.: ‘Nonlinear optimization’, vol. 13 (Princeton University Press, Princeton, USA, 2006).
    19. 19)
      • 19. 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., 1994, 51, (1), pp. 105116.
    20. 20)
      • 20. Grady, S.A., Hussaini, M.Y., Abdullah, M.M.: ‘Placement of wind turbines using genetic algorithms’, Renew. Energy, 2005, 30, (2), pp. 259270.
    21. 21)
      • 21. Wan, C., Wang, J., Yang, G., et al: ‘Optimal micro-siting of wind turbines by genetic algorithms based on improved wind and turbine models’. Proc. 48 h IEEE Conf. Decision and Control (CDC) Held Jointly with 2009 28th Chinese Control Conf., Shanghai, China, 2009, pp. 50925096.
    22. 22)
      • 22. Ozturk, U.A., Norman, B.A.: ‘Heuristic methods for wind energy conversion system positioning’, Electr. Power Syst. Res., 2004, 70, (3), pp. 179185.
    23. 23)
      • 23. Rivas, R.A., Clausen, J., Hansen, K.S., et al: ‘Solving the turbine positioning problem for large offshore wind farms by simulated annealing’, Wind Eng., 2009, 33, (3), pp. 287297.
    24. 24)
      • 24. Mohammadi, K., Mostafaeipour, A.: ‘Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran’, Energy Convers. Manage., 2013, 65, (1), pp. 463470.
    25. 25)
      • 25. Feng, J., Shen, W.Z.: ‘Solving the wind farm layout optimization problem using random search algorithm’, Renew. Energy, 2015, 78, pp. 182192.
    26. 26)
      • 26. Wan, C., Wang, J., Yang, G., et al: ‘Optimal micro-siting of wind farms by particle swarm optimization’, Proc. International Conference in Swarm Intelligence, Springer, Berlin, Heidelberg, 2010, pp. 198205.
    27. 27)
      • 27. Gu, H., Wang, J.: ‘Irregular-shape wind farm micro-siting optimization’, Energy, 2013, 57, (8), pp. 535544.
    28. 28)
      • 28. Hou, P., Hu, W., Chen, Z.: ‘Optimisation for offshore wind farm cable connection layout using adaptive particle swarm optimisation minimum spanning tree method’, IET Renew. Power Gener., 2016, 10, (5), pp. 694702.
    29. 29)
      • 29. Kusiak, A., Song, Z.: ‘Design of wind farm layout for maximum wind energy capture’, Renew. Energy, 2010, 35, (3), pp. 685694.
    30. 30)
      • 30. Chowdhury, S., Zhang, J., Messac, A., et al: ‘Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions’, Renew. Energy, 2013, 52, pp. 273282.
    31. 31)
      • 31. Abudulrahman, M., Wood, D.: ‘Investigation the power-CoE trade-off for wind farm layout optimization considering commercial turbine selection and hub height variation’, Renew. Energy, 2017, 102, pp. 267278.
    32. 32)
      • 32. Chen, K., Song, M., Zhang, X., et al: ‘Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm’, Renew. Energy, 2016, 96, pp. 676686.
    33. 33)
      • 33. Rodrigues, S., Restrepo, C., Katsouris, G., et al: ‘A multi-objective optimization framework for offshore wind farm layouts and electric infrastructures’, Energies, 2016, 9, (3), pp. 142.
    34. 34)
      • 34. Feng, J., Shen, W.Z.: ‘Design optimization of offshore wind farms with multiple types of wind turbines’, Appl. Energy, 2017, 205, pp. 12831297.
    35. 35)
      • 35. Tang, X., Shen, Y., Li, S., et al: ‘Mixed installation to optimize the position and type selection of turbines for wind farms’. Int. Conf. Neural Information Processing, Guangzhou, China, 2017, pp. 307315.
    36. 36)
      • 36. Mortensen, N.G., Landberg, L., Troen, I., et al: ‘Wasp utility programs’. Technical Report, Risø National Laboratory, 2004.
    37. 37)
      • 37. Yu, Y., Li, H., Che, Y., et al: ‘The price evolution of wind turbines in China: a study based on the modified multi-factor learning curve’, Renew. Energy, 2017, 103, pp. 522536.
    38. 38)
      • 38. Katic, I., Højstrup, J., Jensen, N.O.: ‘A simple model for cluster efficiency’. European Wind Energy Association Conf. Exhibition, Rome, Italy, 1986, pp. 407410.
    39. 39)
      • 39. Chen, Y., Li, H., Jin, K., et al: ‘Wind farm layout optimization using genetic algorithm with different hub height wind turbines’, Energy Convers. Manage., 2013, 70, pp. 5665.
    40. 40)
      • 40. Ayotte, K.W., Hughes, D.E.: ‘Observations of boundary-layer wind-tunnel flow over isolated ridges of varying steepness and roughness’, Bound.-Layer Meteorol., 2004, 112, (3), pp. 525556.
    41. 41)
      • 41. Wharton, S., Newman, J.F., Qualley, G., et al: ‘Measuring turbine inflow with vertically-profiling lidar in complex terrain’, J. Wind Eng. Ind. Aerodyn., 2014, 142, pp. 217231.
    42. 42)
      • 42. Launder, B.E., Sharma, B.I.: ‘Application of the energy-dissipation model of turbulence to the calculation of flow near a spinning disc’, Lett. Heat Mass Transf., 1974, 1, (1), pp. 131137.
    43. 43)
      • 43. Miller, C., Davenport, A.: ‘Guidelines for the calculation of wind speed-ups in complex terrain’, J. Wind Eng. Ind. Aerodyn., 1998, 74, pp. 189197.
    44. 44)
      • 44. Mitchell, M.: ‘An introduction to genetic algorithms’ (MIT press, Cambridge, England, 1998).
    45. 45)
      • 45. Schutte, J.F., Groenwold, A.A.: ‘A study of global optimization using particle swarms’, J. Glob. Optim., 2005, 31, (1), pp. 93108.
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