Advances in Energy Research

  • 제8권2호
  • /
  • Pages.95-110
  • /
  • 2022
  • /
  • 2287-6316(pISSN)
  • /
  • 2287-6324(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

New approach to calculate Weibull parameters and comparison of wind potential of five cities of Pakistan

  • 투고 : 2021.08.01
  • 심사 : 2022.05.17
  • 발행 : 2022.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Wind energy can be utilized for the generation of electricity, due to significant wind potential at different parts of the world, some countries have already been generating of electricity through wind. Pakistan is still well behind and has not yet made any appreciable effort for the same. The objective of this work was to add some new strategies to calculate Weibull parameters and assess wind energy potential. A new approach calculates Weibull parameters; we also developed an alternate formula to calculate shape parameters instead of the gamma function. We obtained k (shape parameter) and c (scale parameter) for two-parameter Weibull distribution using five statistical methods for five different cities in Pakistan. Maximum likelihood method, Modified Maximum likelihood Method, Method of Moment, Energy Pattern Method, Empirical Method, and have been to calculate and differentiate the values of (shape parameter) k and (scale parameter) c. The performance of these five methods is estimated using the Goodness-of-Fit Test, including root mean square error, mean absolute bias error, mean absolute percentage error, and chi-square error. The daily 10-minute average values of wind speed data (obtained from energydata.info) of different cities of Pakistan for the year 2016 are used to estimate the Weibull parameters. The study finds that Hyderabad city has the largest wind potential than Karachi, Quetta, Lahore, and Peshawar. Hyderabad and Karachi are two possible sites where wind turbines can produce reasonable electricity.

키워드

참고문헌

  1. Abid, M., Karimov, K.S., Wajid, H.A., Farooq, F., Ahmed, H. and Khan, O.H. (2015), "Design, development and testing of a combined Savonius and Darrieus vertical axis wind turbine", Iran. J. Energy Environ., 6(1), 1-4.
  2. Adaramola, M.S., Agelin-Chaab, M. and Paul, S.S. (2014), "Assessment of wind power generation along the coast of Ghana", Energy Convers. Manag., 77, 61-69. https://doi.org/10.1016/j.enconman.2013.09.005
  3. Akdag, S.A. and Dinler, A. (2009), "A new method to estimate Weibull parameters for wind energy applications", Energy Convers. Manag., 50(7), 1761-1766. https://doi.org/10.1016/j.enconman.2009.03.020
  4. Akgul, F.G., Senoglu, B. and Arslan, T. (2016), "An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution", Energy Convers. Manag., 114, 234-240. https://doi.org/10.1016/j.enconman.2016.02.026
  5. Arik, I., Kantar, Y.M. and Usta, I. (2019), "The new odd-burr rayleigh distribution for wind speed characterization", Wind Struct., Int. J., 28(6), 369-380. https://doi.org/10.12989/was.2019.28.6.369
  6. Arslan, T., Bulut, Y.M. and Yavuz, A.A. (2014), "Comparative study of numerical methods for determining Weibull parameters for wind energy potential", Renew. Sustain. Energy Rev., 40, 820-825. https://doi.org/10.1016/j.rser.2014.08.009
  7. Baloch, M.H., Kaloi, G.S. and Memon, Z.A. (2016), "Current scenario of the wind energy in Pakistan challenges and future perspectives: A case study", Energy Reports, 2, 201-210. https://doi.org/10.1016/j.egyr.2016.08.002
  8. Bilir, L., Imir, M., Devrim, Y. and Albostan, A. (2015), "Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function", Int. J. Hydrogen Energy, 40(44), 15301-15310. https://doi.org/10.1016/j.ijhydene.2015.04.140
  9. Carta, J.A., Ramirez, P. and Velazquez, S. (2009), "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands", Renew. Sustain. Energy Rev., 13(5), 933-955. https://doi.org/10.1016/j.rser.2008.05.005
  10. Chai, T. and Draxler, R.R. (2014), "Root mean square error (RMSE) or mean absolute error (MAE)-Arguments against avoiding RMSE in the literature", Geosci. Model Develop., 7(3), 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
  11. Chang, T.P. (2011), "Estimation of wind energy potential using different probability density functions", Appl. Energy, 88(5), 1848-1856. https://doi.org/10.1016/j.apenergy.2010.11.010
  12. Deep, S., Sarkar, A., Ghawat, M. and Rajak, M.K. (2020), "Estimation of the wind energy potential for coastal locations in India using the Weibull model", Renew. Energy, 161, 319-339. https://doi.org/10.1016/j.renene.2020.07.054
  13. Gugliani, G.K., Sarkar, A., Ley, C. and Mandal, S. (2018), "New methods to assess wind resources in terms of wind speed, load, power and direction", Renew. Energy, 129, 168-182. https://doi.org/10.1016/j.renene.2018.05.088
  14. Gugliani, G.K., Sarkar, A., Ley, C. and Matsagar, V. (2021), "Identification of optimum wind turbine parameters for varying wind climates using a novel month-based turbine performance index", Renew. Energy, 171, 902-914. https://doi.org/10.1016/j.renene.2021.02.141
  15. Justus, C.G. and Mikhail, A. (1976), "Height variation of wind speed and wind distributions statistics", Geophys. Res. Lett., 3(5), 261-264. https://doi.org/10.1029/GL003i005p00261
  16. Kadhem, A.A., Abdul Wahab, N.I., Aris, I., Jasni, J. and Abdalla, A.N. (2017), "Advanced wind speed prediction model based on a combination of Weibull distribution and an artificial neural network", Energies, 10(11), 1744. https://doi.org/10.3390/en10111744
  17. Khahro, S.F., Tabbassum, K., Soomro, A.M., Liao, X., Alvi, M.B., Dong, L. and Manzoor, M.F. (2014), "Techno-economical evaluation of wind energy potential and analysis of power generation from wind at Gharo, Sindh Pakistan", Renew. Sustain. Energy Rev., 35, 460-474. https://doi.org/10.1016/j.rser.2014.04.027
  18. Khan, J.K., Ahmed, F., Uddin, Z., Iqbal, S.T., Jilani, S.U., Siddiqui, A.A. and Aijaz, A. (2015), "Determination of Weibull parameter by four numerical methods and prediction of wind speed in Jiwani (Balochistan)", J. Basic Appl. Sci., 11, 62-68. https://doi.org/10.6000/1927-5129.2015.11.30
  19. Khan, M.T.I., Ali, Q. and Ashfaq, M. (2018), "The nexus between greenhouse gas emission, electricity production, renewable energy and agriculture in Pakistan", Renew. Energy, 118, 437-451. https://doi.org/10.1016/j.renene.2017.11.043
  20. Lun, I.Y. and Lam, J.C. (2000), "A study of Weibull parameters using long-term wind observations", Renew. Energy, 20(2), 145-153. https://doi.org/10.1016/S0960-1481(99)00103-2
  21. Mahmood, F.H., Resen, A.K. and Khamees, A.B. (2020), "Wind characteristic analysis based on Weibull distribution of Al-Salman site, Iraq", Energy Reports, 6, 79-87. https://doi.org/10.1016/j.egyr.2019.10.021
  22. Mohammadi, K., Alavi, O., Mostafaeipour, A., Goudarzi, N. and Jalilvand, M. (2016), "Assessing different parameters estimation methods of Weibull distribution to compute wind power density", Energy Convers. Manag., 108, 323-335. https://doi.org/10.1016/j.enconman.2015.11.015
  23. Mohiuddin, O., Mohiuddin, A., Obaidullah, M., Ahmed, H. and Asumadu-Sarkodie, S. (2016), "Electricity production potential and social benefits from rice husk, a case study in Pakistan", Cogent Eng., 3(1), 1177156. https://doi.org/10.1080/23311916.2016.1177156
  24. Rehman, A. and Deyuan, Z. (2018), "Investigating the linkage between economic growth, electricity access, energy use, and population growth in Pakistan", Appl. Sci., 8(12), 2442. https://doi.org/10.3390/app8122442
  25. Rocha, P.A.C., de Sousa, R.C., de Andrade, C.F. and da Silva, M.E.V. (2012), "Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil", Appl. Energy, 89(1), 395-400. https://doi.org/10.1016/j.apenergy.2011.08.003
  26. Saeed, M.K., Salam, A., Rehman, A.U. and Saeed, M.A. (2019), "Comparison of six different methods of Weibull distribution for wind power assessment: A case study for a site in the Northern region of Pakistan", Sustain. Energy Technol. Assess., 36, 100541. https://doi.org/10.1016/j.seta.2019.100541
  27. Safari, B. and Gasore, J. (2010), "A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda", Renew. Energy, 35(12), 2574-2880. https://doi.org/10.1016/j.renene.2010.04.032
  28. Salahaddin, A.A. (2013), "Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq", Int. J. Phys. Sci., 8(5), 186-192. https://doi.org/10.5897/IJPS12.697
  29. Sarkar, A., Gugliani, G. and Deep, S. (2017), "Weibull model for wind speed data analysis of different locations in India", KSCE J. Civil Eng., 21(7), 2764-2776. https://doi.org/10.1007/s12205-017-0538-5
  30. Seguro, J.V. and Lambert, T.W. (2000), "Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis", J. Wind Eng. Indust. Aerodyn., 85(1), 75-84. https://doi.org/10.1016/S0167-6105(99)00122-1
  31. Sukkiramathi, K., Rajkumar, R. and Seshaiah, C.V. (2020), "Mathematical representation to assess the wind resource by three parameter Weibull distribution", Wind Struct., Int. J., 31(5), 419-430. https://doi.org/10.12989/was.2020.31.5.419
  32. Sumair, M., Aized, T., Gardezi, S.A.R., Bhutta, M.M.A., Rehman, S.M.S. and Ur Rehman, S.U. (2020), "Comparison of three probability distributions and techno-economic analysis of wind energy production along the coastal belt of Pakistan", Energy Explorat. Exploitat., 0144598720931587. https://doi.org/10.1177/0144598720931587
  33. Teyabeen, A.A., Akkari, F.R. and Jwaid, A.E. (2018), "Mathematical Modelling of Wind Turbine Power Curve", Int. J. Simul. Syst. Sci. Technol., 19(5), 1-13. https://doi.org/10.5013/IJSSST.a.19.05.15
  34. Tizgui, I., El Guezar, F., Bouzahir, H. and Benaid, B. (2017), "Comparison of methods in estimating Weibull parameters for wind energy applications", Int. J. Energy Sector Manag. https://doi.org/10.1108/IJESM-06-2017-0002
  35. Ullah, K. (2013), "Electricity infrastructure in Pakistan: an overview", Int. J. Energy Inform. Commun., 4(3), 11-26.
  36. Usta, I. (2016), "An innovative estimation method regarding Weibull parameters for wind energy applications", Energy, 106, 301-314. https://doi.org/10.1016/j.energy.2016.03.068
  37. Voinov, V., Pya, N., Makarov, R. and Voinov, Y. (2016), "New invariant and consistent chi-squared type goodness-of-fit tests for multivariate normality and a related comparative simulation study", Commun. Statist. - Theory and Methods, 45(11), 3249-3263. https://doi.org/10.1080/03610926.2014.901370
  38. Wadi, M. and Elmasry, W. (2021), "Statistical analysis of wind energy potential using different estimation methods for Weibull parameters: a case study", Electr. Eng., 103, 2573-2594. https://doi.org/10.1007/s00202-021-01254-0
  39. Wakeel, M., Chen, B. and Jahangir, S. (2016), "Overview of energy portfolio in Pakistan", Energy Procedia, 88, 71-75. https://doi.org/10.1016/j.egypro.2016.06.024
  40. Zameer, H. and Wang, Y. (2018) "Energy production system optimization: Evidence from Pakistan", Renew. Sustain. Energy Rev., 82, 886-893. https://doi.org/10.1016/j.rser.2017.09.089
  41. Zhou, J., Erdem, E., Li, G. and Shi, J. (2010), "Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites", Energy Convers. Manag., 51(7), 1449-1458. https://doi.org/10.1016/j.enconman.2010.01.020