10.1007/s40095-021-00444-7

Estimation of wind energy potential and comparison of six Weibull parameters estimation methods for two potential locations in Nepal

  • Bibek Pandeya*1,
  • Bibek Prajapati1,
  • Aayush Khanal1,
  • Basant Regmi1,
  • Shree Raj Shakya2,
  1. Department of Mechanical and Aerospace Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, 44700, NP
  2. Department of Mechanical and Aerospace Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, 44700, NP Institute for Advanced Sustainability Studies, Potsdam, 14467, DE

Published in Issue 2021-11-02

How to Cite

Pandeya, B., Prajapati, B., Khanal, A., Regmi, B., & Shakya, S. R. (2021). Estimation of wind energy potential and comparison of six Weibull parameters estimation methods for two potential locations in Nepal. International Journal of Energy and Environmental Engineering, 13(3 (September 2022). https://doi.org/10.1007/s40095-021-00444-7
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Abstract

Abstract This study analyzes the wind speed characteristics, compares the six different methods (graphical, method of moment, wind energy pattern factor, empirical method of Justus and Lysen, and maximum likelihood method) of estimating Weibull parameters and calculates wind power density using daily mean wind speed data collected, at a height of 2 m, over a period of seven and eight years for Jumla and Okhaldhunga, respectively. Wind data were estimated at a height of 50 m to calculate average wind speed, Weibull parameters, and wind power density. Based on the results, Jumla has an average monthly maximum wind speed of 9.78 m/s in June and minimum wind speed of 6.71 m/s in December, whereas Okhaldhunga has an average monthly maximum wind speed of 10.95 m/s in April and minimum wind speed of 4.52 m/s in October. Jumla has an average annual wind speed of 8.11 m/s while Okhaldhunga has an average annual wind speed of 6.89 m/s. The accuracy of estimation methods was statistically tested using root mean square error and coefficient of determination. The empirical method of Justus and Lysen was found to be the best performing while the graphical method performed the poorest. By using the best method, an average wind power density has been estimated as 336.07 W/m 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document} and 326.73 W/m 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document} for Jumla and Okhaldhunga, respectively, indicating that both locations belong to wind power class III and have a moderate potential for wind energy harvesting.

Keywords

  • Wind energy,
  • Wind speed,
  • Weibull distribution,
  • Nepal

References

  1. Ritchie, H., Roser, M.: Energy. Our World in Data. Https://ourworldindata.org/energy (2020)
  2. IEA, Global Energy Review 2021. Tech. rep., Paris (2021).
  3. https://www.iea.org/reports/global-energy-review-2021
  4. (2021)
  5. International Renewable Energy Agency (IRENA), Renewable Energy Capacity Statistics 2021. Tech. rep., Abu Dhabi (2021)
  6. Global Wind Energy Council (GWEC), Global Wind Report 2021. Tech. rep., Brussels (2021).
  7. https://gwec.net/global-wind-report-2021/
  8. Parajuli (2016) A statistical analysis of wind speed and power density based on Weibull and Rayleigh models of Jumla, Nepal 8(7) https://doi.org/10.4236/epe.2016.87026
  9. Fyrippis et al. (2010) Wind energy potential assessment in Naxos Island, Greece 87(2) https://doi.org/10.1016/j.apenergy.2009.05.031
  10. Bidaoui et al. (2019) Wind speed data analysis using Weibull and Rayleigh distribution functions, case study: five cities northern Morocco
  11. Hoxha et al. (2018) An experimental study of Weibull and Rayleigh distribution functions of wind speeds in Kosovo 16(5) https://doi.org/10.12928/telkomnika.v16i5.10260
  12. Ucar and Balo (2011) An investigation of wind turbine characteristics and the wind potential assessment of Ankara, Turkey 33(13) https://doi.org/10.1080/15567030903397842
  13. Arefi et al. (2014) The wind energy potential of Kurdistan, Iran
  14. Soulouknga et al. (2018) Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution https://doi.org/10.1016/j.renene.2018.01.002
  15. Ucar and Balo (2009) Investigation of wind energy potential in Kartalkaya-Bolu, Turkey https://doi.org/10.1080/15435070903107098
  16. Chang (2011) Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application 88(1) https://doi.org/10.1016/j.apenergy.2010.06.018
  17. Rocha et al. (2012) Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil 89(1) https://doi.org/10.1016/j.apenergy.2011.08.003
  18. Ahmed (2013) Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq 8(5)
  19. Werapun et al. (2015) Comparative study of five methods to estimate Weibull parameters for wind speed on Phangan Island, Thailand https://doi.org/10.1016/j.egypro.2015.11.596
  20. Shaban et al. (2020) Weibull parameters evaluation by different methods for windmills farms https://doi.org/10.1016/j.egyr.2019.10.037
  21. Ben et al. (2021) Integrated technical analysis of wind speed data for wind energy potential assessment in parts of southern and central Nigeria https://doi.org/10.1016/j.clet.2021.100049
  22. IRENA, Future of Wind: Renewable Power Generation Costs in 2020. Deployment, investment, technology, grid integration and socio-economic aspects. Tech. rep., Abu Dhabi (2019).
  23. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf
  24. IRENA, Renewable Power Generation Costs in 2020. Tech. rep., Abu Dhabi (2021).
  25. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Jan/IRENA_2017_Power_Costs_2018.pdf
  26. Nepal’s largest wind–solar hybrid power system switched on to connect a small village to the world.
  27. https://www.adb.org/news/nepals-largest-wind-solar-hybrid-power-system-switched-connect-small-village-world#:~:text=KATHMANDU
  28. (2017). Accessed May 3, 2021
  29. Dhakal et al. (2020) Feasibility study of distributed wind energy generation in Jumla Nepal 10(3)
  30. Rahman and Chattopadhyay (2019) Statistical assessment of wind energy potential for power generation at Imphal, Manipur (India) https://doi.org/10.1080/15567036.2019.1675814
  31. Prescott and Van Kooten (2009) Economic costs of managing of an electricity grid with increasing wind power penetration 9(2) https://doi.org/10.3763/cpol.2007.0425
  32. Azad et al. (2014) Statistical diagnosis of the best Weibull methods for wind power assessment for agricultural applications 7(5) https://doi.org/10.3390/en7053056
  33. Justus et al. (1978) Methods for estimating wind speed frequency distributions 17(3) https://doi.org/10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2
  34. Mohammadi et al. (2016) Assessing different parameters estimation methods of Weibull distribution to compute wind power density https://doi.org/10.1016/j.enconman.2015.11.015
  35. Akdağ and Dinler (2009) A new method to estimate Weibull parameters for wind energy applications 50(7) https://doi.org/10.1016/j.enconman.2009.03.020
  36. Kaoga et al. (2014) Comparison of five numerical methods for estimating Weibull parameters for wind energy applications in the district of Kousseri, Cameroon 3(1) (pp. 73-88)
  37. Hyndman and Koehler (2006) Another look at measures of forecast accuracy 22(4) https://doi.org/10.1016/j.ijforecast.2006.03.001
  38. Zhang, M.H.: Wind resource assessment and micro-siting: science and engineering. Wiley, Hoboken (2015)
  39. Ulazia et al. (2019) Global estimations of wind energy potential considering seasonal air density changes https://doi.org/10.1016/j.energy.2019.115938
  40. Rehman and Ahmad (2004) Assessment of wind energy potential for coastal locations of the Kingdom of Saudi Arabia 29(8) https://doi.org/10.1016/j.energy.2004.02.026
  41. Teyabeen, A.A., Akkari, F.R., Jwaid, A.E.: Comparison of seven numerical methods for estimating Weibull parameters for wind energy applications. In:
  42. 2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim)
  43. (IEEE), pp. 173–178 (2017)
  44. Boopathi et al. (2021) Assessment of wind power potential in the coastal region of Tamil Nadu, India https://doi.org/10.1016/j.oceaneng.2020.108356
  45. Hayter, S.J., Kandt, A.: Renewable energy applications for existing buildings. Tech. rep., National Renewable Energy Lab.(NREL), Golden, CO (United States) (2011)