DOI: https://doi.org/10.14710/ijred.2022.43477

Techno-economic Analysis of Wind Turbines Powering Rural of Malaysia

1Department of Mechanical Engineering, University of Kufa Najaf, Iraq

2Department of Mechanical Engineering, University of Thi-Qar, Nassiriya, Iraq

3Department of Mechanical and Aeronautical Engineering, Clarkson University, Potsdam, NY 13699-5725, United States

4 Centre for Nano-Materials and Energy Technology (RCNMET), Sunway University, Kuala Lumpur, Malaysia

5 Power Energy Dedicated Advanced Centre (UMPEDAC), University of Malaya, Kuala Lumpur, Malaysia

View all affiliations
Received: 5 Dec 2021; Revised: 6 Jan 2022; Accepted: 10 Jan 2022; Available online: 22 Jan 2022; Published: 4 May 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 purpose of this study is to evaluate the wind energy potential and energy cost of various types of wind turbines that could be powering rural Areas. The analysis was performed on hourly wind data over three years for five locations measured with a 10 m-high anemometer in Peninsular Malaysia. The performance of wind turbines with varying hub heights and rated power was examined. The economic evaluation of wind energy in all sites was based on an analysis of the annual Levelized cost of energy. Results show that the annual mean wind speeds vary from 1.16 m/s in Sitiswan to 2.9 m/s in Mersing, whereas annual power varies from 3.6 to 51.4 W/m2. Moreover, the results show that the cost of unit energy varies between (4.5-0.38) $/kWh.The most viable site for the use of wind turbines was Mersing, while Sitiawan was the least viable site. A case study examined three wind turbine models operating at Mersing. The study showed that increasing the inflation escalation rate for operating and maintenance from 0-5% led to a decrease in the unit energy cost by about 38%. However, increasing the operating and maintenance escalation rate from 0-10% led to an increase in the unit cost of energy by about 7-8%.  
Fulltext View|Download
Keywords: Renewable Energy; Rural area; wind speed; economic analysis; wind turbine

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Planning and Risk Analysis in Projects of Procurement of Agricultural Raw Materials for the Production of Environmentally Friendly Fuel Investigating the Environmental and the Energy Saving Behavior among School Principals through Classification Algorithms Combustion, Physical, and Mechanical Characterization of Composites Fuel Briquettes from Carbonized Banana Stalk and Corncob Enhanced Geothermal Systems – Promises and Challenges Mathematical Model of the Thermal Performance of Double-Pass Solar Collector for Solar Energy Application in Sierra Leone Bioconversion of Industrial Cassava Solid Waste (Onggok) to Bioethanol Using a Saccharification and Fermentation process More recent articles
Most cited articles
Influence of the Rubber Seed Type and Altitude on Characteristic of Seed, Oil and Biodiesel Generating Organic Liquid Products from Catalytic Cracking of Used Cooking Oil over Mechanically Mixed Catalysts Pre-treatment of Used-Cooking Oil as Feed Stocks of Biodiesel Production by Using Activated Carbon and Clay Minerals Optimal Operation of Micro-grids Considering the Uncertainties of Demand and Renewable Energy Resources Generation Renewable Energy in Eastern North Africa in Terms of Patterns of Coupling to Czisch European HVDC Super Grid More cited articles
  1. Adaramola, M. S., Paul, S. S., & Oyedepo, S. O. (2011). Assessment of electricity generation and energy cost of wind energy conversion systems in north-central Nigeria. Energy Conversion and Management, 52(12), 3363-3368; https://doi.org/10.1016/j.enconman.2011.07.007
  2. Aldersey-Williams, J., Rubert, T. (2019). Levelised cost of energy - A theoretical justification and critical assessment, Energy Policy, 124, 169-179; https://doi.org/10.1016/j.enpol.2018.10.004
  3. Ali Kadhem, A., Abdul Wahab, N. I., & N Abdalla, A. (2019). Wind energy generation assessment at specific sites in a Peninsula in Malaysia based on reliability indices. Processes, 7(7), 399.; https://doi.org/10.3390/pr7070399
  4. Al‐Fatlawi, A. W. A., Rahim, N. A., Saidur, R., & Ward, T. A. (2015). Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia. Environmental Progress & Sustainable Energy, 5(34), 1528-1535.‏; https://doi.org/10.1002/ep.12130
  5. Al-Fatlawi, A.W.A, Abdul-Hakim, S. R., Ward, T. A., & Rahim, N. A. (2014). Technical and economic analysis of renewable energy powered stand‐alone pole street lights for remote area. Environmental Progress & Sustainable Energy, 33(1), 283-289. https://doi.org/10.1002/ep.11772
  6. Akpinar, E. K., & Akpinar, S. (2005). An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics. Energy conversion and management, 46(11-12), 1848-1867. https://doi.org/10.1016/j.enconman.2004.08.012
  7. Bardsley, W. E. (1980). Note on the use of the inverse Gaussian distribution for wind energy applications. Journal of Applied Meteorology and Climatology, 19(9), 1126-1130.‏ https://doi.org/10.1175/1520-0450(1980)019<1126:NOTUOT>2.0.CO;2
  8. Borhanazad, H., Mekhilef, S., Saidur, R., & Boroumandjazi, G. (2013). Potential application of renewable energy for rural electrification in Malaysia. Renewable energy, 59, 210-219.‏ https://doi.org/10.1016/j.renene.2013.03.039
  9. Bosch, J., Staffell, I., Hawkes, A.D. (2017). Temporally-explicit and spatially-resolved global onshore wind energy potentials, Energy, 131 207-217. https://doi.org/10.1016/j.energy.2017.05.052
  10. Carlin, J., & Haslett, J. (1982). The probability distribution of wind power from a dispersed array of wind turbine generators. Journal of Applied Meteorology and Climatology, 21(3), 303-313.‏ https://doi.org/10.1175/1520-0450(1982)021<0303:TPDOWP>2.0.CO;2
  11. Carta, J. A., Ramirez, P., & Velazquez, S. (2009). A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands. Renewable and sustainable energy reviews, 13(5), 933-955.‏ https://doi.org/10.1016/j.rser.2008.05.005
  12. ‏Celik, A. N. (2003). Energy output estimation for small-scale wind power generators using Weibull-representative wind data. Journal of wind engineering and industrial aerodynamics, 91(5), 693-707. https://doi.org/10.1016/S0167-6105(02)00471-3
  13. Celik, A. N. (2004). A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable energy, 29(4), 593-604. https://doi.org/10.1016/j.renene.2003.07.002
  14. Conradsen, K., Nielsen, L. B., & Prahm, L. P. (1984). Review of Weibull statistics for estimation of wind speed distributions. Journal of Applied Meteorology and Climatology, 23(8), 1173-1183.‏ https://doi.org/10.1175/1520-0450(1984)023<1173:ROWSFE>2.0.CO;2
  15. Corotis, R. B., Sigl, A. B., & Klein, J. (1978). Probability models of wind velocity magnitude and persistence. Solar energy, 20(6), 483-493. https://doi.org/10.1016/0038-092X(78)90065-8
  16. Exell, R. H. B., & Fook, C. T. (1986). The wind energy potential of Malaysia. Solar Energy, 36(3), 281-289. https://doi.org/10.1016/0038-092X(86)90143-X
  17. Gökçek, M. and M.S. Genç (2009). Evaluation of electricity generation and energy cost of wind energy conversion systems (WECSs) in Central Turkey. Applied Energy, 86(12): p. 2731-2739. https://doi.org/10.1016/j.apenergy.2009.03.025
  18. Hashemi-Tilehnoee, M., Babayani, D., & Khaleghi, M. (2016). Evaluating wind energy potential in Gorgan-Iran using two methods of Weibull distribution function. International Journal of Renewable Energy Development, 5(1), 43. https://doi.org/10.14710/ijred.5.1.43-48
  19. Hiendro, A., Kurnianto, R., Rajagukguk, M., & Simanjuntak, Y. M. (2013). Techno-economic analysis of photovoltaic/wind hybrid system for onshore/remote area in Indonesia. Energy, 59, 652-657.‏ https://doi.org/10.1016/j.energy.2013.06.005
  20. Islam, M.R., Saidur, R. and Rahim, N.A. (2011). Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function. Energy, 36(2): p. 985-992. https://doi.org/10.1016/j.energy.2010.12.011
  21. Islam, M. R. (2011). Assessment of wind energy potential mapping for peninsular Malaysia (Doctoral dissertation, Jabatan Kejuruteraan Mekanik, Fakulti Kejuruteraan, Universiti Malaya).‏‏
  22. Jamil, M., Parsa, S., & Majidi, M. (1995). Wind power statistics and an evaluation of wind energy density. Renewable energy, 6(5-6), 623-628.‏ https://doi.org/10.1016/0960-1481(95)00041-H
  23. Justus, C. G., & Mikhail, A. (1976). Height variation of wind speed and wind distributions statistics. Geophysical Research Letters, 3(5), 261-264.‏ https://doi.org/10.1029/GL003i005p00261
  24. Justus, C. G., Hargraves, W. R., Mikhail, A., & Graber, D. (1978). Methods for estimating wind speed frequency distributions. Journal of applied meteorology, 17(3), 350-353.‏ https://doi.org/10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2
  25. Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M., & Abbaszadeh, R. (2010). An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy, 35(1), 188-201.‏ https://doi.org/10.1016/j.energy.2009.09.009
  26. Luna, R. E., & Church, H. W. (1974). Estimation of long-term concentrations using a "universal" wind speed distribution. Journal of Applied Meteorology and Climatology, 13(8), 910-916.‏ https://doi.org/10.1175/1520-0450(1974)013<0910:EOLTCU>2.0.CO;2
  27. Masseran, N., Razali, A. M., Ibrahim, K., & Zin, W. W. (2012). Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia. Energy, 37(1), 649-656. https://doi.org/10.1016/j.energy.2011.10.035
  28. ‏Martínez, E., Sanz, F., Pellegrini, S., Jiménez, E., & Blanco, J. (2009). Life cycle assessment of a multi-megawatt wind turbine. Renewable energy, 34(3), 667-673.‏ https://doi.org/10.1016/j.renene.2008.05.020
  29. Mohammed, D., Abdelaziz, M., Sidi, A., Mohammed, E., & Elmostapha, E. (2019). Wind speed data and wind energy potential using Weibull distribution in Zagora, Morocco. International Journal of Renewable Energy Development, 8(3), 267-273. https://doi.org/10.14710/ijred.8.3.267-273
  30. ‏Polaris American. < http://www.polaris america.com/> [accessed 03.09.21]
  31. Rieradevall, J., Domènech, X., & Fullana, P. (1997). Application of life cycle assessment to landfilling. The International Journal of Life Cycle Assessment, 2(3), 141-144.‏ https://doi.org/10.1007/BF02978806
  32. ‏‏Şen, Z., Altunkaynak, A., & Erdik, T. (2012). Wind velocity vertical extrapolation by extended power law. Advances in Meteorology, 2012.‏ https://doi.org/10.1155/2012/178623
  33. Sherlock, R. H. (1951). Analyzing winds for frequency and duration. In On Atmospheric Pollution (pp. 42-49). American Meteorological Society, Boston, MA.‏ https://doi.org/10.1007/978-1-940033-03-79
  34. Sopian, K., Othman, M. H., & Wirsat, A. (1995). The wind energy potential of Malaysia. Renewable Energy, 6(8), 1005-1016.‏ https://doi.org/10.1016/0960-1481(95)00004-8
  35. Tchinda, R., & Kaptouom, E. (2003). Wind energy in Adamaoua and North Cameroon provinces. Energy Conversion and Management, 44(6), 845-857. https://doi.org/10.1016/S0196-8904(02)00092-4
  36. Torres, J. L., Prieto, E., Garcia, A., De Blas, M., Ramirez, F., & De Francisco, A. (2003). Effects of the model selected for the power curve on the site effectiveness and the capacity factor of a pitch regulated wind turbine. Solar Energy, 74(2), 93-102; https://doi.org/10.1016/S0038-092X(03)00144-0
  37. Weisser, D. (2003). A wind energy analysis of Grenada: an estimation using the 'Weibull' density function. Renewable energy, 28(11), 1803-1812; https://doi.org/10.1016/S0960-1481(03)00016-8

Last update:

  1. Investigation and statistical analysis of electrical energy production from the wind farm

    Mohammed I. Alghamdi, Oriza Candra, Arif Sari, Iskandar Muda, Mujtaba Zuhair Ali, Karrar Shareef Mohsen, Reza Morovati, Behnam Bagheri. AIP Advances, 13 (6), 2023. doi: 10.1063/5.0137760

  2. Assessment of wind energy resource in the western region of Somaliland

    Shaima Batran, Mohamed Jama, Jawad Yousaf, Taimur Hassan, Mohammed Ghazal, Hazlie Bin Mokhlis. Heliyon, 10 (12), 2024. doi: 10.1016/j.heliyon.2024.e32500

Last update: 2024-12-21 20:13:50

No citation recorded.