Evaluation and validation of wind energy extension (Bahooz) in Manjil region, Iran

Document Type : Original Research Article

Authors

1 Department of Marine Physics, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Iran

2 Department of Environmental Science, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Iran

Abstract

Development that is not environmentally friendly is not sustainable. One of the 
methods of sustainable development is the use of renewable energy such as 
wind. One of the most important sites in Iran with wind energy potential is the 
Manjil region. Four sites in Manjil region (Manjil, Siahpoosh, Rudbar and 
Herzeville) were surveyed. In this paper, wind energy potential measurement in 
onshore and coastal areas evaluates wind energy according to the extensions 
developed by the authors. The results with scientific achievements and similar 
software in 4 stages of wind simulation, simulation of conditions the boundary 
of the range will assess wind power and extractable energy. Summary of spatial 
fit and arrangement of turbines shows that Manjil power plant in world energy 
class has sufficient quality of energy production and can be compared with 
global sites. This site with a nominal capacity of 240 million kilowatt-hours per 
year is one of the largest sites in the Middle East with a capacity factor of 0.25. 
Siahpoosh site with a capacity of 410 million kilowatt-hours per year has a 
limited factor capacity of 8%. This site has a coefficient of variation of 11%, 
which modeling shows that the choice of 660 MW turbines is not very 
appropriate and practical. Therefore, it seems that the use of 500 kW turbines 
has a better capability than 660 turbines on this site. Based on the results, the 
two sites of Rudbar and Herzeville have a very proportionate factor capacity, so 
these two sites can be upgraded according to the installation of Class 4 and 3 
turbines.

Keywords

References

1- Karki R, Billinton R. Cost-effective wind energy utilization for reliable power supply. IEEE Transactions on Energy Conversion. 2004;19(2):435-40.
2- Gude VG, Fthenakis V. Energy efficiency and renewable energy utilization in desalination systems. Progress in Energy. 2020;2(2):022003.
3- Hashemzadegazar M, Azizi G, Karimi M, Khoshakhlagh F, Shamsipour A. performance Evaluation of ERA-ENTRIM in spatio-temporal distribution and wind speed trend in eastern Iran. Physical Geography Research Quarterly. 2020;52(4):515-33.
4- Pryor SC, Barthelmie RJ. A global assessment of extreme wind speeds for wind energy applications. Nature Energy. 2021:1-9.
5- Zhang L. Wind Energy Development: History and Current Status.  Wind, Water and Fire: The Other Renewable Energy Resources: World Scientific; 2021. p. 1-6.
6- Baxter J, Walker C, Ellis G, Devine-Wright P, Adams M, Fullerton RS. Scale, history and justice in community wind energy: An empirical review. Energy Research & Social Science. 2020;68:101532.
7- Nazir MS, Mahdi AJ, Bilal M, Sohail HM, Ali N, Iqbal HM. Environmental impact and pollution-related challenges of renewable wind energy paradigm–a review. Science of the Total Environment. 2019;683:436-44.
8- Roy A, Bandyopadhyay S. Wind Energy Systems.  Wind Power Based Isolated Energy Systems: Springer; 2019. p. 17-32.
9- Kaldellis JK, Zafirakis D. The wind energy (r) evolution: A short review of a long history. Renewable energy. 2011;36(7):1887-901.
10- Cantoni R. Energy and civilization. A history (Vaclav Smil, 2017). Journal of Energy History/Revue d'Histoire de l'Énergie [Online]. (1).
11- Sadorsky P. Wind energy for sustainable development: Driving factors and future outlook. Journal of Cleaner Production. 2021;289:125779.
12- Li L, Lin J, Wu N, Xie S, Meng C, Zheng Y, et al. Review and Outlook on the International Renewable Energy Development. Energy and Built Environment. 2020.
13- groups I. International Renewable Energy Agency (IRENA) 2020 [Available from: https://www.irena.org/wind.
14- El Bassam N. Wind energy.  Distributed Renewable Energies for Off-Grid Communities: Elsevier; 2021. p. 149-63.
15- IRENA. Renewable Energy and Jobs Annual Review 2020. 2020.
16- Erdogan S, Okumus I, Guzel AE. Revisiting the Environmental Kuznets Curve hypothesis in OECD countries: the role of renewable, non-renewable energy, and oil prices. Environmental Science and Pollution Research. 2020;27(19):23655-63.
17-  Manwell JF, McGowan JG, Rogers AL. Wind energy explained: theory, design and application: John Wiley & Sons; 2010.
18- Wadi M, Elmasry W. Statistical analysis of wind energy potential using different estimation methods for Weibull parameters: a case study. Electrical Engineering. 2021:1-22.
19- Kwon S-D. Uncertainty analysis of wind energy potential assessment. Applied Energy. 2010;87(3):856-65.
20- Karsli V, Gecit C. An investigation on wind power potential of Nurdaǧı-Gaziantep, Turkey. Renewable Energy. 2003;28(5):823-30.
21- Ruiz SAG, Barriga JEC, Martínez JA. Wind Power Assessment In The Caribbean Region Of Colombia, Using Ten-Minute Wind Observations And Era5 Data. Renewable Energy. 2021.
22- Palese C, Lässig JL, Cogliati MG, Bastanski MA. Wind regime and wind power in North Patagonia, Argentina. Wind Engineering. 2000;24(5):361-77.
23- Sterns A, Manuel L, Saranyasoontorn K, Nelson L. Analysis of time series data on wind turbine loads. National Science Foundation, Arlington, VA, Tech Rep. 2003.
24- Elliott D, Schwartz M. Wind energy potential in the United States. Pacific Northwest Lab., Richland, WA (United States); 1993.
25- Zheng C-w, Li C-y, Xu J-j. Micro-scale classification of offshore wind energy resource——A case study of the New Zealand. Journal of cleaner production. 2019;226:133-41.
26- Mostafaeipour A, Jadidi M, Mohammadi K, Sedaghat A. An analysis of wind energy potential and economic evaluation in Zahedan, Iran. Renewable and Sustainable Energy Reviews. 2014;30:641-50.
27- Hashemi-Tilehnoee M, Babayani D, Khaleghi M. Evaluating wind energy potential in Gorgan-Iran using two methods of Weibull distribution function. International Journal of Renewable Energy Development. 2016;5(1):43.
28- Keyhani A, Ghasemi-Varnamkhasti M, Khanali M, Abbaszadeh R. An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy. 2010;35(1):188-201.
29- Ashrafi ZN, Ghasemian M, Shahrestani MI, Khodabandeh E, Sedaghat A. Evaluation of hydrogen production from harvesting wind energy at high altitudes in Iran by three extrapolating Weibull methods. International Journal of Hydrogen Energy. 2018;43(6):3110-32.
30- Mostafaeipour A, Abarghooei H. Harnessing wind energy at Manjil area located in north of Iran. Renewable and Sustainable Energy Reviews. 2008;12(6):1758-66.
31- Afiesimama E, Pal J, Abiodun B, Gutowski W, Adedoyin A. Simulation of West African monsoon using the RegCM3. Part I: model validation and interannual variability. Theoretical and Applied Climatology. 2006;86(1):23-37.
32- Ciang CC, Lee J-R, Bang H-J. Structural health monitoring for a wind turbine system: a review of damage detection methods. Measurement science and technology. 2008;19(12):122001.
33- Akdağ SA, Dinler A. A new method to estimate Weibull parameters for wind energy applications. Energy conversion and management. 2009;50(7):1761-6.
34- Ouahabi MH, Elkhachine H, Benabdelouahab F, Khamlichi A. Comparative study of five different methods of adjustment by the Weibull model to determine the most accurate method of analyzing annual variations of wind energy in Tetouan-Morocco. Procedia Manufacturing. 2020;46:698-707.
35- Teyabeen AA, Akkari FR, Jwaid AE, editors. Comparison of seven numerical methods for estimating weibull parameters for wind energy applications. 2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim); 2017: IEEE.
36- Justus C, Hargraves W, Mikhail A, Graber D. Methods for estimating wind speed frequency distributions. Journal of applied meteorology. 1978;17(3):350-3.
37- Hau E. Wind turbines: fundamentals, technologies, application, economics: Springer Science & Business Media; 2013.
38- Porté-Agel F, Wu Y-T, Lu H, Conzemius RJ. Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms. Journal of Wind Engineering and Industrial Aerodynamics. 2011;99(4):154-68.
39- Zhou H, Lu Y, Liu X, Chang R, Wang B. Harvesting wind energy in low-rise residential buildings: Design and optimization of building forms. Journal of Cleaner Production. 2017;167:306-16.
40- Whitcomb RT. A design approach and selected wind tunnel results at high subsonic speeds for wing-tip mounted winglets. 1976.
41- Burton T, Sharpe D, Jenkins N, Bossanyi E. Wind Energy Handbook. 2002:511 - 58.
42- Gasset N, Landry M, Gagnon Y. A comparison of wind flow models for wind resource assessment in wind energy applications. Energies. 2012;5(11):4288-322.
43- Oteri FA, Baranowski RE, Baring-Gould EI, Tegen SI. 2017 State of Wind Development in the United States by Region. National Renewable Energy Lab.(NREL), Golden, CO (United States); 2018.
44- Seguro J, Lambert T. Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. Journal of wind engineering and industrial aerodynamics. 2000;85(1):75-84.
45- Karki R, Hu P, editors. Wind power simulation model for reliability evaluation. Canadian Conference on Electrical and Computer Engineering, 2005; 2005: IEEE.
46- Wen J, Zheng Y, Donghan F. A review on reliability assessment for wind power. Renewable and sustainable energy reviews. 2009;13(9):2485-94.
47- Yu Z, Tuzuner A, editors. Fractional weibull wind speed modeling for wind power production estimation. 2009 IEEE Power & Energy Society General Meeting; 2009: IEEE.
48- Jafarian M, Ranjbar A. Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine. Renewable Energy. 2010;35(9):2008-14.
International Journal Of Coastal, Offshore And Environmental Engineering(ijcoe)
Volume 6, Issue 5 - Serial Number 25
Special issue of “Marine Renewable Energy”
November 2021
Pages 76-88

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