skip to main content

Improved Evaluation of The Wind Power Potential of a Large Offshore Wind Farm Using Four Analytical Wake Models

1Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco

2Mohammadia School of Engineers, Mohammed Vth University in Rabat, Morocco

3National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco

Received: 5 May 2021; Revised: 19 Jul 2021; Accepted: 30 Aug 2021; Available online: 10 Sep 2021; Published: 1 Feb 2022.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2022 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

The objective of this paper is to investigate the ability of analytical wake models to estimate the wake effects between wind turbines (WTs). The interaction of multiple wakes reduces the total power output produced by a large offshore wind farm (LOFWF). This power loss is due to the effect of turbine spacing (WTS), if the WTs are too close, the power loss is very significant. Therefore, the optimization of turbine positions within the offshore wind farm requires an understanding of the interaction of wakes inside the wind farm. To better understand the wake effect, the Horns Rev 1 offshore wind farm has been studied with four wake models, Jensen, Larsen, Ishihara, and Frandsen. A comparative study of the wake models has been performed in several situations and configurations, single and multiple wakes are taken into consideration. Results from the Horns Rev1 offshore wind farm case have  been evaluated and compared to observational data, and also  with the previous studies. The power output of a row of WTs is sensitive to the wind direction. For example, if a row of ten turbines is aligned with the 270° wind direction, the full wake condition of WTs is reached and the power deficit limit predicted by Jensen model exceeds 70%. When a wind direction changes only of  10° (260° and 280°), the deficit limit reduces to 30%. The obtained results show that a significant power deficit occurs when the turbines are arranged in an aligned manner. The findings also showed that all four models gave acceptable predictions of the total power output. The comparison between the calculated and reported power output of Horns Revs 1 showed that the differences ranged from - 8.27 MW (12.49%) to 15.27 MW (23.06%) for the Larsen and Frandsen models, respectively.

Fulltext View|Download
Keywords: Offshore wind farm; Wind turbine; Wake model; velocity deficit; wind speed; wind direction

Article Metrics:

  1. Barthelmie, R., Frandsen, S. T., Hansen, K., Schepers, J., Rados, K., Schlez, W., & Neckelmann, S. (2009 a). Modelling the impact of wakes on power output at Nysted and Horns Rev. In European Wind Energy Conference (pp. 1-10)
  2. Barthelmie, R, Hansen, K., Frandsen, S., Rathmann, O., Schepers, J., & Schlez, W. et al. (2009 b). Modelling and measuring flow and wind turbine wakes in large wind farms offshore. Wind Energy, 12(5), 431-444. https://doi.org/10.1002/we.348
  3. Bastankhah, M., & Porté-Agel, F. (2014). A new analytical model for wind-turbine wakes. Renewable Energy, 70, 116-123. https://doi.org/10.1016/j.renene.2014.01.002
  4. Brun, C., Tenchine, D., & Hopfinger, E. (2004). Role of the shear layer instability in the near wake behavior of two side-by-side circular cylinders. Experiments In Fluids, 36(2), 334-343. https://doi.org/10.1007/s00348-003-0726-6
  5. Chowdhury, S., Zhang, J., Messac, A., & Castillo, L. (2012). Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation. Renewable Energy, 38(1), 16-30. https://doi.org/10.1016/j.renene.2011.06.033
  6. Crasto, G., Gravdahl, A., Castellani, F., & Piccioni, E. (2012). Wake Modeling with the Actuator Disc Concept. Energy Procedia, 24, 385-392. https://doi.org/10.1016/j.egypro.2012.06.122
  7. Deaves, D., & Lines, I. (1997). On the fitting of low mean windspeed data to the Weibull distribution. Journal Of Wind Engineering And Industrial Aerodynamics, 66(3), 169-178. https://doi.org/10.1016/s0167-6105(97)00013-5
  8. Feng, J., & Shen, W. (2015). Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction. Energies, 8(4), 3075-3092. https://doi.org/10.3390/en8043075.‏
  9. Frandsen, S., Barthelmie, R., Pryor, S., Rathmann, O., Larsen, S., Højstrup, J., & Thøgersen, M. (2006). Analytical modelling of wind speed deficit in large offshore wind farms. Wind Energy, 9(1-2), 39-53. https://doi.org/10.1002/we.189
  10. Frandsen, S. (1992). On the wind speed reduction in the center of large clusters of wind turbines. Journal Of Wind Engineering And Industrial Aerodynamics, 39(1-3), 251-265. https://doi.org/10.1016/0167-6105(92)90551-k
  11. Gao, X., Yang, H., & Lu, L. (2016). Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model. Applied Energy, 174, 192-200. https://doi.org/10.1016/j.apenergy.2016.04.098
  12. García, L., Vatn, M., Mühle, F., & Sætran, L. (2017). Experiments in the wind turbine far wake for the evaluation of an analytical wake model. Journal Of Physics: Conference Series, 854, 012015. https://doi.org/10.1088/1742-6596/854/1/012015
  13. Gaumond, M., Réthoré, P., Ott, S., Peña, A., Bechmann, A., & Hansen, K. (2013). Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm. Wind Energy, 17(8), 1169-1178. https://doi.org/10.1002/we.1625
  14. GES DISC. Disc.sci.gsfc.nasa.gov. (2020). Retrieved 15 December 2020, from https://disc.sci.gsfc.nasa.gov/datasets?keywords=%22MERRA-2%22&page=1&source=Models%2FAnalyses%20MERRA-2
  15. Göçmen, T., Laan, P., Réthoré, P., Diaz, A., Larsen, G., & Ott, S. (2016). Wind turbine wake models developed at the technical university of Denmark: A review. Renewable And Sustainable Energy Reviews, 60, 752-769. https://doi.org/10.1016/j.rser.2016.01.113
  16. Hamilton, N., Bay, C., Fleming, P., King, J., & Martínez-Tossas, L. (2020). Comparison of modular analytical wake models to the Lillgrund wind plant. Journal Of Renewable And Sustainable Energy, 12(5), 053311. https://doi.org/10.1063/5.0018695.‏
  17. Hasager, C. B., Giebel, G. (2015). EERA DTOC final summary report. EERA DTOC - European Energy Research Alliance - Design Tool for Offshore Wind Farm Cluster.
  18. Hassoine, M., Lahlou, F., Addaim, A., & Madi, A. (2019). A Novel Evaluation of Wind Energy Potential in Essaouira Offshore Wind Farm, using Genetic Algorithm and MERRA-2 Reanalysis Data. 2019 5Th International Conference On Optimization And Applications (ICOA). https://doi.org/10.1109/icoa.2019.8727669
  19. Hou, P., Hu, W., Soltani, M., Chen, C., & Chen, Z. (2017). Combined optimization for offshore wind turbine micro siting. Applied Energy, 189, 271-282. https://doi.org/10.1016/j.apenergy.2016.11.083
  20. Ishihara, T., Yamaguchi, A., and Fujino, Y., 2004. Development of a New Wake Model Based on a Wind Tunnel Experiment. Global Wind Power
  21. Jensen, L., Mørch, C., Sørensen, P., Svendsen, K. H. 2004. Wake measurements from the Horns Rev wind farm. EWEC 2004, 22-25 November 2004. London, United Kingdom
  22. Jensen, N. O. 1983. A note on wind generator interaction. Risø National Laboratory. Risø-M, No. 2411
  23. Kang, D., Ko, K., & Huh, J. (2018). Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea. Energies, 11(2), 356. https://doi.org/10.3390/en11020356
  24. Katic, I., Højstrup, J., & Jensen, N. O. (1987). A Simple Model for Cluster Efficiency. In W. Palz, & E. Sesto (Eds.), EWEC'86. Proceedings. Vol. 1 (pp. 407-410). A. Raguzzi
  25. Larsen, G. C. 1988. A Simple Wake Calculation Procedure. Risø National Laboratory. Risø-M, No. 2760
  26. Larsen, G. C. 2009. A simple stationary semi-analytical wake model. Risø National Laboratory for Sustainable Energy, Technical University of Denmark. Denmark. Forskningscenter Risoe. Risoe-R, No. 1713(EN)
  27. Machefaux, E. 2015. Multiple Turbine Wakes. DTU Wind Energy. DTU Wind Energy PhD, No. 0043(EN)
  28. MERRA-2. Gmao.gsfc.nasa.gov. (2020). Retrieved 15 December 2020, from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/
  29. Mittal, P., Kulkarni, K., & Mitra, K. (2016). A novel hybrid optimization methodology to optimize the total number and placement of wind turbines. Renewable Energy, 86, 133-147. https://doi.org/10.1016/j.renene.2015.07.100
  30. Peña, A., Réthoré, P-E., Hasager, C. B., & Hansen, K. S. (2013). Results of wake simulations at the Horns Rev I and Lillgrund wind farms using the modified Park model. DTU Wind Energy. DTU Wind Energy E, No. 0026(EN)
  31. Power plants: Horns Rev 1 - Vattenfall. Powerplants.vattenfall.com. (2020). Retrieved 30 May 2020, from https://powerplants.vattenfall.com/horns-rev
  32. Renkema, D.J. 2007. Validation of wind turbine wake models: Using wind farm data and wind tunnel measurements. Master of Science Thesis. Delft University of Technology
  33. Richmond, M., Antoniadis, A., Wang, L., Kolios, A., Al-Sanad, S., & Parol, J. (2019). Evaluation of an offshore wind farm computational fluid dynamics model against operational site data. Ocean Engineering, 193, 106579. https://doi.org/10.1016/j.oceaneng.2019.106579
  34. Shao, Z., Wu, Y., Li, L., Han, S., & Liu, Y. (2019). Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes. Energies, 12(4), 680. https://doi.org/10.3390/en12040680
  35. Sørensen, J., & Larsen, G. (2021). A Minimalistic Prediction Model to Determine Energy Production and Costs of Offshore Wind Farms. Energies, 14(2), 448. https://doi.org/10.3390/en14020448
  36. Stevens, Richard J. A. M., Dennice F. Gayme, and Charles Meneveau. 2016. "Generalized Coupled Wake Boundary Layer Model: Applications And Comparisons With Field And LES Data For Two Wind Farms". Wind Energy 19 (11): 2023-2040. doi: 10.1002/we.1966
  37. Sun, H., & Yang, H. (2018). Study on three wake models’ effect on wind energy estimation in Hong Kong. Energy Procedia, 145, 271-276. https://doi.org/10.1016/j.egypro.2018.04.050.‏
  38. Tian, L., Zhu, W., Shen, W., Zhao, N., & Shen, Z. (2015). Development and validation of a new two-dimensional wake model for wind turbine wakes. Journal Of Wind Engineering And Industrial Aerodynamics, 137, 90-99. https://doi.org/10.1016/j.jweia.2014.12.001
  39. Tong, W., Chowdhury, S., Zhang, J., & Messac, A. (2012, September). Impact of different wake models on the estimation of wind farm power generation. In 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO. Multidisciplinary Analysis and Optimization Conference (p. 5430).‏
  40. Van Ackere, S., Van Eetvelde, G., Schillebeeckx, D., Papa, E., Van Wyngene, K., & Vandevelde, L. (2015). Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods. Energies, 8(8), 8682-8703. https://doi.org/10.3390/en8088682
  41. Vermeer, L., Sørensen, J., & Crespo, A. (2003). Wind turbine wake aerodynamics. Progress In Aerospace Sciences, 39(6-7), 467-510. https://doi.org/10.1016/s0376-0421(03)00078-2
  42. Vestas V80 Offshore - 2,00 MW - Éolienne. Fr.wind-turbine-models.com. Retrieved 13 September 2020, from https://fr.wind-turbine-models.com/turbines/668-vestas-v80-offshore
  43. Wade, B., Pereira, R., & Wade, C. (2019). Investigation of offshore wind farm layouts regarding wake effects and cable topology. Journal Of Physics: Conference Series, 1222, 012007. https://doi.org/10.1088/1742-6596/1222/1/012007
  44. Ye, W. (2013) Spatial Variation and Interpolation of Wind Speed Statistics and Its Implication in Design Wind Load. Electronic Thesis and Dissertation Repository.1254. https://ir.lib.uwo.ca/etd/1254

Last update:

  1. Optimisation-based system designs for deep offshore wind farms including power to gas technologies

    Francesco Baldi, Andrea Coraddu, Miltiadis Kalikatzarakis, Diana Jeleňová, Maurizio Collu, Julia Race, François Maréchal. Applied Energy, 310 , 2022. doi: 10.1016/j.apenergy.2022.118540
  2. Simulation and experimental study of refuse-derived fuel gasification in an updraft gasifier

    Thanh Xuan Nguyen-Thi, Thi Minh Tu Bui, Van Ga Bui. International Journal of Renewable Energy Development, 12 (3), 2023. doi: 10.14710/ijred.2023.53994
  3. Approaches in performance and structural analysis of wind turbines – A review

    Sakthivel Rajamohan, Abhiram Vinod, Mantri Pragada Venkata Sesha Aditya, Harshini Gopalakrishnan Vadivudaiyanayaki, Van Nhanh Nguyen, Müslüm Arıcı, Sandro Nižetić, Thi Thai Le, Rahmat Hidayat, Dinh Tuyen Nguyen. Sustainable Energy Technologies and Assessments, 53 , 2022. doi: 10.1016/j.seta.2022.102570
  4. Numerical and experimental investigations on a bladeless turbine: Tesla's cohesion-type innovation

    Malayathi Sivaramakrishnaiah, Dhanajan Savary Nasan, Prabhakar Sharma, Thanh Tuan Le, Minh Ho Tran, Thi Bich Ngoc Nguyen, Phuoc Quy Phong Nguyen, Viet Dung Tran. International Journal of Renewable Energy Development, 13 (1), 2024. doi: 10.14710/ijred.2024.55455

Last update: 2024-03-28 03:58:50

No citation recorded.