Abstract
Environmental pollution and scarce resources have led us to think of new alternatives. Even though we have plenty of alternatives like solar energy, hydro energy, bio gas, still wind energy plays an important role. It is said that if all the places in the world with wind energy potential is utilized, it will fulfill the total energy requirements of the world. Keeping it in mind, we focused on the wind energy potential for power generation in the Northeastern cities of India such as Gangtok, Tarey Bhir, Zunheboto, Bomdila, Dibrugarh, Udaipur, Ukhrul, Serchhip, and William Nagar. The wind speed data were collected over the period of 22 years (January 1983 to December 2004) from the RETScreen climate database at an altitude of 10 m from the ground. The wind speed data were subjected to two parameters: Weibull distribution with the scale (c) and shape (k) being the parameters along with other statistical techniques for assessment of wind energy potential. We found that wind speed varies between 1.94 and 7.15 m/s at different places in northeastern cities. The Weibull parameters k and c lie between 4.81–11.95 and 2.53–6.12. Also, the wind power density which is the quantitative measure of the wind energy available at any location varies from 10.2 to 186.76 W/m2. The result based on the Weibull analysis indicates that only Gangtok, Tarey Bhir, Bomdila, and Dibrugarh can be used for electricity generation on a large scale at 10 m height above the ground, whereas other places can be used to extract energy from low-speed wind at a height greater than 10 m.
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Abbreviations
- f(v):
-
Probability density function
- k :
-
Weibull shape parameter (dimensionless)
- c :
-
Weibull scale parameter (m/s)
- F(v):
-
Cumulative density function
- v :
-
Wind speed (m/s)
- ν m :
-
Mean wind speed (m/s)
- σ :
-
Standard deviation
- Γ:
-
Gamma function
- n :
-
Total number of data
- ν mp :
-
Most probable wind speed (m/s)
- ν Emax :
-
Maximum energy carrying wind speed (m/s)
- p(v):
-
Wind power density (W/m2)
- ρ :
-
Air density (kg/m3)
References
Ajayi, O.O., Fagbenle, R.O. and Katende, J. Assessment of Wind Power Potential and Wind Electricity Generation Using WECS of Two Sites in South West, Nigeria. International Journal of Energy Science, 1, 78–92. (2011).
Oztopal, A. Sahin, A.D. Akgun, N. and Sen, Z. On the Regional Wind Energy Potential of Turkey. Energy, 25, 189–200 (2000).
Feretic, D., Tomsic, Z. and Cavlina, N., Feasibility Analysis of Wind-Energy Utilization in Croatia. Energy, 24, 239–246 (1999).
Sulaiman, M.Y., Akaak, A.M., Abd. Wahab, M., Zakaria, A., Sulaiman, Z.A. and Suradi, J. Wind Characteristic of Oman. Energy, 27, 35–46 (2002).
Rehman, S. and Ahmed, A. Assessment of Wind Energy Potential for Coastal Locations of the Kingdom of Saudi Arabia. Energy, 29, 1105–1115 (2005).
Ahmed Shata, A.S. and Hanitsch, R. Evaluation of Wind Energy Potential and Electricity Generation in Egypt. Renewable Energy, 31, 1183–1202 (2006).
Ahmed, S.A. Wind Energy as a Potential Generation Source at Ras Benas, Egypt. Renewable and Sustainable Energy Reviews, 14, 2167–217 (2010).
Albadi, M.H., El-Saadany, E.F. and Albadi, H.A. Wind to Power a New City in Oman. Energy, 34, 1579–1586 (2009).
RETScreen Database, www.retscreen.net.
Mathew, S., Pandey, K.P. and Kumar, A.V. Analysis of Wind Regimes for Energy Estimation. Renewable Energy, 25, 381–399 (2002).
Seguro JV, Lambert TW. Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. J Wind Eng Ind Aerodyn; 85:75–84 (2000).
Pashardes S, Christofides C. Statistical analysis of wind speed and direction in Cyprus. Sol Energy; 55(5):405–14 (1995).
Ilinca, A., McCarthy, E., Chaumel, J.-L. and Retiveau, J.-LWind Potential Assessment of Quebec Province. Renewable Energy, 28, 1881–1897 (2003).
Argungu, G.M., Bala, E.J., Momoh, M., Musa, M., Dabai, K.A., Zangina, U. and Maiyama, B.A. Statistical Analysis of Wind Energy Resource Potentials For Power Generation In Jos, Nigeria, Based on Weibull Distribution Function. The International Journal Of Engineering And Science (IJES), 2, 22–31 (2013).
Lipson, C. and Sheth, N.J. Statistical Design and Analysis of Engineering Experiments. McGraw-Hill, New York (1973).
Keyhani, A., Ghasemi-Varnamkhasti, M., An Assessment of Wind Energy Potential as a Power Generation Source in the Capital of Iran, Tehran. Energy, 35, 188–201 (2010).
Samuel Perkin, Deon Garrett, and Pall Jensson, “Optimal wind turbine selection methodology: A case-study for Búrfell, Iceland”. Renewable Energy 75, pp. 165–172 (2015).
Wei zhou, Hongxing Yang, Zhaohong Fang, “Wind Power Potential and Characteristic Analysis of the Pearl River Delta Region, China” Renewable Energy Vol. 31, pp 739–753 (2006).
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Rahul, S., Prakash, O. (2018). Assessment of Wind Energy Potential in Northeastern Cities of India. In: Bera, R., Sarkar, S., Chakraborty, S. (eds) Advances in Communication, Devices and Networking. Lecture Notes in Electrical Engineering, vol 462. Springer, Singapore. https://doi.org/10.1007/978-981-10-7901-6_23
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