Appraising the viability of wind energy conversion system in the Peninsular Malaysia

To harvest the wind energy resource for power production, it is crucially important to carry out a preliminary study to understand the site-specific nature of wind at the intended site. Such knowledge is required to estimate the performance of a wind energy project in the area. This study investigates the wind energy potential for production of electric power in the Peninsular Malaysia. Wind speed data of six selected sites across the country collected over a period of 10–20 years are employed for the study. A statistical analysis of the wind speeds is carried out using the Weibull distribution model. Six identified commercially available wind turbines with rated capacity ranging from 20 kW to 1500 kW, with different speed parameters are simulated at the selected locations. Of the six sites evaluated in this paper, it is revealed that Mersing, having the highest monthly average wind speed and consequently the most viable, produces an average power density of 57.58 W/m2 with a capacity factor of only 4.39%. This is equivalent to 378 MW h energy production per annum at a levelised cost of 22 cents per kW h. This study also shows that the standard deviation of the average monthly wind speeds is a better factor than the average annual wind speed for ranking of selected sites in terms of annual energy production. Overall, the results obtained from this investigation show that large-scale wind energy is not viable in Malaysia due to weak wind regimes; however, small-scale wind energy system may be economically viable in a few regions most especially when the recently launched feed-in tariff in the country is extended to wind energy.

[1]  Freyr Sverrisson,et al.  Renewables 2014 : global status report , 2014 .

[2]  D. Fadare The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria , 2010 .

[3]  S. Rehman,et al.  Wind shear coefficients and their effect on energy production , 2005 .

[4]  Nicolas Boccard,et al.  Capacity Factor of Wind Power: Realized Values vs. Estimates , 2009 .

[5]  Ioannis Fyrippis,et al.  Wind energy potential assessment in Naxos Island, Greece , 2010 .

[6]  Tsang-Jung Chang,et al.  Assessment of wind characteristics and wind turbine characteristics in Taiwan , 2003 .

[7]  Olayinka S. Ohunakin,et al.  Assessment of wind energy resources for electricity generation using WECS in North-Central region, Nigeria , 2011 .

[8]  James F. Manwell,et al.  The round robin site assessment method: A new approach to wind energy site assessment , 2008 .

[9]  Shyh-Jier Huang,et al.  Determination of Suitability Between Wind Turbine Generators and Sites Including Power Density and Capacity Factor Considerations , 2012, IEEE Transactions on Sustainable Energy.

[10]  H. Hizam,et al.  Mitigating the anthropogenic global warming in the electric power industry , 2012 .

[11]  R. Exell,et al.  The wind energy potential of Malaysia , 1986 .

[12]  Kamaruzzaman Sopian,et al.  Wind speed analysis in the east coast of Malaysia , 2009 .

[13]  K. Sopian,et al.  The wind energy potential of Malaysia , 1995 .

[14]  Tick Hui Oh,et al.  Energy policy and alternative energy in Malaysia: Issues and challenges for sustainable growth , 2010 .

[15]  Ma Jing,et al.  Piv experimental and numerical study of a 2-D airfoil for wind turbines , 2009, 2009 World Non-Grid-Connected Wind Power and Energy Conference.

[16]  Mohd Talib Latif,et al.  Fitting a mixture of von Mises distributions in order to model data on wind direction in Peninsular Malaysia , 2013 .

[17]  Muyiwa S. Adaramola,et al.  Assessment of electricity generation and energy cost of wind energy conversion systems in north-central Nigeria , 2011 .

[18]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

[19]  Mohan Kolhe,et al.  Generalized feed-forward based method for wind energy prediction , 2013 .

[20]  Jing Liu,et al.  A statistical analysis of wind power density based on the Weibull models for Fujian province in China , 2009, 2009 World Non-Grid-Connected Wind Power and Energy Conference.

[21]  Önder Güler,et al.  Investigation of wind shear coefficients and their effect on electrical energy generation , 2011 .

[22]  M. A. Alsaad,et al.  Wind energy potential in selected areas in Jordan , 2013 .

[23]  I. Aris,et al.  Economic viability of distributed energy resources relative to substation and feeder facilities expansion , 2010, 2010 IEEE International Conference on Power and Energy.

[24]  T. W. Lambert,et al.  Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis , 2000 .

[25]  A. Celik A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey , 2004 .

[26]  Soon-Duck Kwon UNCERTAINTY ANALYSIS OF WIND ENERGY POTENTIAL ASSESSMENT , 2010 .

[27]  Ali Mostafaeipour,et al.  Economic evaluation of small wind turbine utilization in Kerman, Iran , 2013 .

[28]  Nasrudin Abd Rahim,et al.  Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function , 2011 .

[29]  T. Chang Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application , 2011 .

[30]  G. L. Johnson,et al.  Wind energy systems , 1985 .

[31]  Joseph P. Hennessey Some Aspects of Wind Power Statistics , 1977 .