The Evaluation of Wind Energy Potential in Peninsular Malaysia

Transcription

1 August 2, Volume 2, No. International Journal of Chemical and Environmental Engineering The Evaluation of Wind Energy Potential in Peninsular Malaysia M.R.S Siti a, b M. Norizah a, M. Syafrudin a a Electrical Engineering Group, School of Electrical and Electronic Engineering Universiti Sains Malaysia, Engineering Campus, 3 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia. b Corresponding Author milia_86@yahoo.com Abstract This paper presents an assessment of the wind energy potential in Peninsular Malaysia. The five selected sites in Peninsular Malaysia which are Langkawi, Penang, Kuala Terengganu, Kota Bharu and Mersing. The data used are real- time wind data obtained from the Malaysian Meteorological Services (MMS) from year 25 until year 29. The statistical analysis was performed by computing the Weibull distribution using maximum likelihood method. The results reveal that Mersing is the most potential site for installing wind turbine. The average annually wind speed within five years is 2.65m/s in this region. According to the international system wind classification, this average annually wind speed can be classified as Class. Moreover, the results of mean wind power density indicates that Mersing experiencing peak mean wind speed during the northeast monsoon with approximately as much as 62W/m 2. Other regions having Wind Power Density (WPD) close to W/m 2 during the stronger wind speed seasonal. From the results, it is revealing that most of the regions in Peninsular Malaysia are having limited wind energy potential except Mersing. Hence, there is a high potential on applying the small-scale wind turbine system at Mersing for power generation purposes. Keywords: Wind energy, wind speed, Weibull distribution, power density, probability density function, wind power generation.. Introduction Wind energy is the fastest growing energy technology in the 99s in terms of percentage of yearly growth of installed capacity per technology source. The growth of wind energy, however, is not evenly distributed around the world. By the end of 2, the total operational wind power capacity worldwide was 23,27 MW. Of this, 7.3% was installed in Europe, followed by 9.% in North America, 9.3% in Asia and the Pacific,.9% in the Middle East and Africa and.% in South and Central America []. The importance of clean energy sources was realized rapidly after the negative effects of the pollution caused by generators on the environment became clear. Wind energy is a clean and renewable energy source whose applications exist worldwide. There are several applications of wind energy particularly in generating electricity. This has been attempted by converting this energy into rotating energy using a wind turbine to drive the electrical generators. The advantages of this type of energy are cheap source and no damage to the environment [2]. Many countries worldwide recognize that the current energy trends are not sustainable and that a better balance must be found between energy security, economic development and protection of the environment including in Malaysia. One of these sources is wind energy. In Malaysia, the potential energy has been quite widely researched. The potential for wind energy generation in Malaysia depends on the availability of the wind resource that varies with location. Understanding the site-specific nature of wind is a crucial step in planning a wind energy project. Malaysia has tropical weather, influenced by monsoonal climate because of its latitude and longitude. Tropical climate here gives hot summer that is accompanied with high humidity level. But the weather in general in Malaysia is without extremities. Malaysia's climate is hot and humid with relative humidity ranging from 8-9 percent, except in the highlands. The temperature averages from (2-3 O C) throughout the year [3]. Monsoon comes twice a year. Due to the country s locations, winds over the area are generally light. The strongest wind only occurs on the East coast of Peninsular Malaysia during the Northeast monsoon []. Hence, the assessment of wind energy potential in Peninsular Malaysia will be performed. 2. Data Collection of Wind Speed Distribution Located in Southeastern Asia, Malaysia is an island nation that forms a part of the Malaysian Peninsular. Bordered by Thailand, Indonesia and Brunei, the geography of Malaysia is divided into two major parts

2 Peninsular Malaysia (latitude N and longitude 2 ' E) and East Malaysia [5]. The South China Sea and the Straits of Malacca are the other two prominent features of Malaysian geography. The wind speed data variations from Meteorological Station of year 25 until year 29 were obtained at five selected regions in Peninsular Malaysia. The regions are Langkawi, Penang, Kuala Terengganu, Kota Bharu and Mersing These wind speed data were recorded every minute using anemometer meanwhile the wind direction were measured using wind vane. The data comprise monthly, average hourly wind speed, wind direction, temperature, and humidity. The elevations of anemometer for each region are different which is depending on the geographical aspect. The wind speed will be measured in meter per second unit. Table present the description of the selected regions in Peninsular Malaysia which consist of latitude, longitudes elevation of anemometer at population. Table. Description of the selected regions in Peninsular Malaysia Region Latitude Longitude Elevation (m) Langkawi 6 25'N 99 5'E 7 Penang 5 2'N 23'E Kuala Terengganu 5 2'N 3 8'E 32 Kota Bharu 6 7'N 2 'E 5 Mersing 2 25'N 3 5'E 5 employ the maximum likelihood method in estimating the Weibull distribution analysis. Hence, the Weibull distribution with the maximum likelihood method will be employed in this research. There are two parameters that need to be computed which are shape factor, k and scale factor, c. The results of shape factor and scale factor for each region were presented in the table form. Next, the probability density function (PDF) and cumulative density function (CDF) will be determined The PDF is given by [], [], [2], [3], [], [5], [6], [7], [8], [9] and [2]. f V k V k V c e c c k Where k is the shape factor and c is the scale factor. This expression is valid for k >, x, and c>. For a given mean wind speed, a lower shape factor indicates a relatively wide distribution of wind speeds around the average while a higher shape factor indicates a relatively narrow distribution of wind speeds around the average. A lower shape factor will normally lead to a higher energy production for a given average wind speed. The cumulative distribution function (CDF) is given by [], [], [2], [3], [], [5], [6], [7], [8], [9] and [2]. k V F( V ) exp c () (2) 3. Statistical Analysis of Weibull Distribution Generally, there are two statistical analysis methods to analyze the wind speed data which are Weibull distribution and Raleigh distribution. Weibull distribution is the common method that was used in implementing the statistical analysis. The Weibull distribution appeared to represent the actual data better than Raleigh distribution [6]. On other hand, there are many methods that can used in order to implement the Weibull distribution. The common methods for determining parameters k and c are graphical method, standard deviation method, moment method and maximum likelihood method as well as energy pattern factor method [7]. Ahmad S.et al has explained that the root mean square error (RMSE) for maximum likelihood method is always lowers than others methods. Meanwhile, the graphical method is inaccurate and the results are affected by the bin size in the cumulative distribution format [8]. The maximum likelihood method is used for the wind speed data analysis. This method was used by Stevens and Smulders [9] in their study for the estimation of parameters of Weibull wind speed distribution for the wind energy utilization purpose. Since the maximum likelihood method provides better estimation compare to other methods, it is suggested to 285 As the value of k increases, the distribution has a sharper peak, and as c increases, the winds become higher [6]. The shape factor, k and the scale factor, c can be estimated by using the following: n k V i ln k i n k V i i and n c V n i k i n V V i ln i n k i Where n is the number of none zero data values.. Wind Power Analysis Another important aspect that will be executed is wind power analysis. Firstly, the mechanical wind power will be calculated. It is given in (5). (3) () P AV 3 (5) 2 Where is the air density, A is swept area, and V is wind speed. From the (5), it is believe that the energy

3 available for conversion mainly depends on the wind speed and the swept area of the turbine. In this research, the assessing of wind power density (WPD) available in the wind region prevailing at a site is one of the preliminary steps in the planning of a wind energy project [7]. WPD indicates how much energy per unit of time is available at the selected area for conversion to electricity by a wind turbine [] can be defined as: P WPD V 3 A 2 In order to acquires the most accurate estimate for WPD the summation using data taken over a time interval was performed. It is given by: (6) n i i V 3 WPD (7) i 2 n Where n is the number of wind speed readings, i is the ith readings of air density and Vi is the ith readings of wind speed. After that, wind energy density (WED) can be calculated by time factor as WPD is known. It can be define as: WED WPD A T Cp (8) Where T is the time which can be 876 hours per year. WED depends on the efficiency of the wind turbine (power coefficient, Cp) and the swept area, A. The value of power coefficient is unique to each turbine type and is a function of wind speed that the turbine is operating in. The real world limit is well below the Betz Limit with values of common even in the best designed wind turbines [2]. Finally, the annual energy output will be determined using (8) where the time taken is about 876 hours. 5. Results and Discussion In this analysis, the potential of wind energy were investigated at Langkawi Island, Penang, Kuala Terengganu, Kota Bharu and Mersing. The results of wind speed obtained from MMS presented that the corresponding annual mean speed in Langkawi Island within five year in is approximately.76m/s. Meanwhile in Penang, it is approximately.5m/s whilst Kuala Terengganu having annual wind speed around 9m/s. The highest annual mean wind speed happened at Mersing with approximately 2.65m/s and Kota Bharu obtaining the lower of annual mean wind speed which is about.58m/s. Further work is conducted at Mersing as it has the potential for used wind in generating energy. Accordingly, the annual and monthly wind speed variation at Mersing has been performed and it is viewing in Table 2. As can be seen, the development annual and monthly mean wind speed in 25 until 29 is similar respectively. It is established that the stronger mean wind speed at Mersing was occurred during the Northeast monsoon season from November to February. It was range roughly 2m/s to 5m/s. During this region, the wind northeast monsoon blowing and dominate this region. It is also expected that there is a heavy rainfall occurred. In contrast, the worst wind speed had been experience from March until October. Mostly during the months, the mean wind speed remains constant between.9m/s to 3.6m/s. From this result, it can perceive that the annually and monthly mean wind speed at Mersing is higher and more unwavering than other regions. From example, in April until October 25, the mean wind speed remains constant at 2.22m/s except August. This is almost similar in 26 until 28. However, in 29 there is a slight change in term of increment and decrement of wind speed variation. Table 2: Annually Monthly Mean Wind Speed at Mersing Month/Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec The daily wind speed variation at Mersing is also analyzed. The result is shown in Fig. and Table 3. It is believed that the highest mean daily wind speed is just around 7.22m/s that occurred just single day. The same frequency also occurred 6.67m/s followed by 6.39m/s. The common of the mean daily wind seed is about 2.22m/s where the total number of occurrence as much as 89 days per year (2.38%). The constructive outcome of the results shows that there is about 7.23% of the mean wind speed is above 2m/s. Meanwhile the second largest contribution to the high mean wind speed was 2.78m/s with the total incidence is year 29 was about 76 days. Table 3 evidently illustrates the result of mean daily wind speed. 286

4 Figure.Daily mean wind speed variation at Mersing in 29 Table 3: Mean daily wind speed distribution at Mersing in 29 Wind speed(m/s) Frequency Percent Cumulative Percent The Evaluation of Wind Energy Potential in Peninsular Malaysia region. In November until March, the mean wind speed during the day time is a stable with night. However, there is a slight variation between the months where the month that experience stronger speed is January, followed by December and the weakest is March. In January, at hour diurnal wind speed distribution, the wind speed is about.m/s. Then it was amplified dramatically to reach the peak hour at 8. After that, it is fall to.9m/s at 9 and become consistent at 5.m/s from 9 to. Then started from the 2 until night, the wind speed movement is roughly unvarying and decrease rapidly at 7 until 2 to become.m/s (a) From the results obtained, it seen that Mersing is a very potential site to generate electricity via wind energy system. This is because, the results verified that the monthly mean wind speed throughout the year at least 2.6m/s for every day. Additionally the result mean daily wind speed also shows that there s about 7.23% of the total wind speed is more than 2m/s. While the peak maximum wind speed at this region is 9.m/s are rarely occurred in the years. Consequently, it is believe there is an enormous potential of wins power that can installed this region. To further verify the potential of Mersing for wind energy generation, the diurnal wind speed variation at Mersing has also been performed. The result is shown in Fig. 2 (a) and (b). Fig. 2 (a) shows the diurnal wind speed distribution from November to March. A result reveals that the trend at all the time is almost closely is other. The result is totally different with other potential 287 (b) Figure 2.Comparison of diurnal wind speed variation at Mersing (a) November to March (b) April to October Moreover the diurnal wind speed from April to October is presented in Fig. 2(b). It can be seen that the stronger wind speed occurred during the morning. The wind speed stated to increase from 3 until reach the peak wind speed at 8. After that it was dropped slowly until 3 at 2.m/s and remains stable until mid night. On the other hand, there is a minor change of wind speed distribution trend in October. It is show that the diurnal wind speed

5 distribution in this month is lower compare to the other months. The climate of mean wind speed is at 6. Then it was fall at 8 to become 2.5m/s. After that, the wind speed distributions become constant until night. Mersing experience higher wind speed variation through the year with the average of mean wind speed ranging from 2m/s to 5m/s. According to the wind energy classification, this wind speed at this region can be classified as class. The wind speed distribution at this region is higher than other region and it can be said that this region has higher potential of small wind energy system. Moreover, the trend of diurnal wind speed during the Northeast monsoon and Southwest monsoon is diverse. During Northeast monsoon, the wind speed is similar for the whole day. However, January experiences highest diurnal wind speed while the lowest was March. On the other hand, the trend of diurnal wind speed during the Southwest monsoon is similar respectively. The peak hour of wind speed is occurred during morning. Fig.3 demonstrated the wind direction at Mersing. From the pie chart, it is found that the NNE wind direction was dominated this region during the Northeast monsoon start from December until February. Conversely, the SW wind occurred from March to November. This results suppory the wind speed distribution explained. Furthermore, the result also reveal that January having the highest proportion of NNE path followed by December and February. Meanwhile during the Southwest monsoon, the percentage of wind speed is less than % with the highest fraction is about 5.98% in June. Hence, it is believed that the optimum wind energy generation can be obtained during the Northeast monsoon especially in January and December while Southwest monsoon having low mean wind speed and lack of wind energy generation. Mersing is higher compare to other regions. It can be seen clearly that Mersing having the peak mean wind power density during the Northeast monsoon which is in January (62W/m 2 ) followed by December (2.8W/m 2 ), February (7.6W/m 2 ) and November (.9W/m 2 ). Meanwhile, during the Southwest monsoon (March to October), it was ranged between 5.9W/m 2 and 8.W/m 2. Moreover, it can be noted the higher mean wind power density at Langkawi, Kuala Terengganu and Kota Bharu is occurred in January and December which is close to W/m 2. This is because, during these months, the regions experience stronger wind speed. Then, other months obtaining mean wind power density less than 3W/m 2. The results of mean wind power density at Kuala Terengganu prove that it was below than 3W/m 2 except January and December which is close to W/m 2. Consequently, the highest annual wind power density is close to W/ 2 at Mersing followed by Langkawi and Kuala Terengganu which is approximately 3W/m 2 only. All these values, and the corresponding annual mean wind speed, verify that selected regions in Peninsular Malaysia falls into Class of the commercially international system of wind classification. Figure 3.Wind direction at potential region at Mersing 5. Wind Power Density Analysis Another important aspect that has been considered in mapping the wind energy potential in Peninsular Malaysia is about evaluating the mean wind power density. The wind power density analysis has been executed using (7). Fig. illustrated a histogram of monthly variation of the mean wind power density for each region in Peninsular Malaysia. Generally, the mean wind power density at 288 Figure.Wind power density in Peninsular Malaysia 5.2 Statistical Analysis Weibull Distribution Simple knowledge of the mean wind speed of the selected area could not be taken as sufficient for obtaining a clear view of the available wind potential. Therefore, in order to surpass the non predictability of the wind characteristics, a statistical analysis was considered necessary. For this reason, Weibull distribution models have been applied. Fig. 5 (a) to (e) presents the probability density function of the annual wind speed distribution, in which Weibull models have been fitted using (). The probability density function indicates the fraction of time for which a wind speed possibly prevails at the area under investigation. Hence, it can be observed in Fig. 5 that the most frequent wind speed expected in twelve areas in Peninsular Malaysia is between.m/s to 2.22m/s. For instance, Penang experiencing the PDF around.56m/s. Alternatively, the most frequency wind speed at Langkawi, Kuala Terengganu and Kota Bharu is

6 approximately.83m/s. In the contrary, Mersing is the area which obtaining highest frequency wind speed around 2.2m/s all over the year. This result agrees with that already obtained from the initial analysis of the mean wind speed. Clearly, in Fig. 5 that the chances of wind speed exceeding m/s for each region were very limited Histogram (e) Weibull (3P) Figure 5.Comparison of probability density function of annual wind speeds in Peninsular Malaysia (a) Langkawi (b) Penang (c) Kuala Terengganu (d) Kota Bharu (e) Mersing Histogram Weibull (a) Histogram Weibull (b) Another important aspect considered during the statistical analysis was the prediction of the time for which a potentially installed, in this area, wind turbine could be functional. In order to achieve that, the determination of the cumulative distribution function was required. Since this function indicates the fraction of time the wind speed is below a particular speed, by taking the difference of its values the corresponding time for which the turbine would be functional can be estimated. In this analysis, the cumulative density function has been performed using (2). The obtained cumulative density function is shown in Fig. 6 (a) to (e). From this Fig. 6, it indicates that the wind speed that more than 2m/s for each region except Mersing is less than 3 %. For instance, Langkawi has been estimated to be 3% of wind speed more than 2m/s while Kota Bharu and Kuala Terengganu is about 2%. Meanwhile, the highest proportion of wind speed more than 2m/s occurred at Mersing with approximately 7% of total wind speed distribution Histogram (c) Weibull F(V) Sample (a) Weibull Histogram (d) Weibull 289

7 F(V) F(V) Sample Weibull (b) 2. Sample Weibull (c) The next step after establishing the PDF and CDF, the Weibull parameter shape factor and scale factor has been performed using (3) and (). The results are presented in Table. From this result, it can be noted that the values of shape factor is varied significantly throughout the year for each region in Peninsular Malaysia. By refereeing to the results of Langkawi, it can be said that the maximum values of k is about. in December and the minimum values of k is approximately.2 in February. After that, at Kuala Terengganu, the results proves that the maximum values of k is occurred in July with about 2.96 and the value of c is approximately, 2.8m/s in December. The range of scale factor at this region is between.2m/s to 2.8m/s. Next, the results of Kota Bharu show that the peak value of shape factor is about 2.38 and the minimum is about.22. The result is much lower than Kuala Terengganu with the total average shape factor is around 7 and scale factor is about.53m/s. The interesting and potential area to apply wind energy system is located at Mersing. there is a prove that this area obtaining highest scale factor which is the average of scale factor throughout the years is about 2.78m/s and the peak is around 5.3m/s in January. Basically, it is range between 2.29m/s to 5.3m/s and remains consistent for each month. Additionally, the shape factor indicates that the maximum value is about.78 in July and the minimum value is about 2.7 in April. Hence, it can be said that the peaked wind distribution observed can be correlated with the high values of k parameter Table : Weibull Parameter Shape Factor and Scale Factor F(x).5..3 Month Langkawi Penang Kuala Terengganu F(V) x Sample Weibull (d).8 Sample Weibull (3P) (e) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec k c k c k c Figure 6.Comparison of cumulative density function of annual wind speeds in Peninsular Malaysia (a) Langkawi (b) Penang (c) Kuala Terengganu (d) Kota Bharu (e) Mersing 29

8 Table : Continued Month Kota Bharu Mersing Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec The Evaluation of Wind Energy Potential in Peninsular Malaysia k c k c Conclusion The study has shown that the higher wind speed region in Peninsular Malaysia are mostly located at the East coast areas which are Mersing followed Kota Bharu and Kuala Terengganu. However, the north areas also are included such as Langkawi and Penang. The East coast especially Mersing is probably the best wind site in Peninsular Malaysia as the strong Northeast monsoon reaches these coastal areas first. It is due to the strong Northeast monsoon reaches these coastal areas first. The Northeast monsoon together with the Southwest monsoon forms the dominant winds in Peninsular Malaysia. However, the Northeast monsoon is a stronger wind because the South China Sea presents no obstacle to the wind before it reaches the East coast, while the Southwest monsoon is a weaker wind because the Sumatera island act as an impediment to the wind before it reaches the West coast. Therefore, higher wind speed will occurs in the Northeast monsoon season from November to March, while slower wind speed will occurs in the Southwest monsoon season from April to October. Both the monsoons landed on the coastal areas first before moving inland, so the coastal areas are normally windier compare to inland areas. The strong Northeast monsoon landed on the East coast first, so these areas can be predicted as having the highest wind speed and wind potential in Peninsular Malaysia. The wind potential at twelve selected regime in Peninsular Malaysia can be classified as Class wind categories. ACKNOWLEDGMENT The authors would like to thank to Universiti Sains Malaysia Engineering Campus, Malaysia for the fellowship to conduct this work. 29 REFERENCES [] AWEA, Wind Energy Basic. Available at: [2] Varol, C. Ilkilic, Y. Varol., Increasing the efficiency of wind turbines Journal Wind Engineering and Industrial Aerodynamic : 89-85,2. [3] Asian Info. Available at: [] K. Sopian, M. Y. Hj Othman, and A. Wirsat., The wind energy potential of Malaysia. Renewable Energy 6 (8):5-6, 99. [5] Encyclopedia of the Nation. Available at: LOCATION-SIZE-AND-EXTENT.html. [6] I. Fyrippis, P.J. Axaopoulos, and g. Panayiotou., Wind energy potential assessment in Naxos Island, Greece, Applied Energy Publication, 29. [7] J.F. Manwell, J.G. Mc Gowan and A.L. Rogers., Wind Energy Explained Theory, Design and Application, John Wiley and Sons Ltd, England, 22. [8] S. Ahmad, W. Hussain, M.A. Bawadi, S.A. Sanusi., Analysis of wind speed variations and estimation of Weibull parameters for wind power generation in Malaysia. [9] M.J. Stevens and P.T. Smulders., The estimation of parameters of the Weibull wind speed distribution for wind energy utilization purpose, Wind Eng. 3 (2), 32-5, 979. [] E.K. Akpinar and S. Akpinar., Determination of the wind energy potential for Maden, Turkey, Energy Convers Manage 5 (2) (8 9), pp [] K. Ulgen and A. Hepbasli., Determination of Weibull parameters for wind energy analysis of Izmir, Turkey, Int J Energy Res 26 (22), pp [2] A.N. Celik., On the distributional parameters used in assessment of the suitability of wind speed probability density functions, Energy Convers Manage 5 (2) ( 2), pp [3] A.N. Celik., A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey, Renew Energy 29 (2), pp [] R. Kose., An evaluation of wind energy potential as a power generation source in Kütahya, Turkey, Energy Convers Manage 5 (2) ( 2), pp [5] A.N. Celik., Weibull representative compressed wind speed data for energy and performance calculations of wind energy systems, Energy Convers Manage (23) (9), pp [6] D.M. Deaves and I.G Lines., On the fitting of low mean wind speed data to the Weibull distribution, J Wind Eng Ind Aerodyn 66 (997), pp [7] S. Persaud, D. Flynn and Fox B., Potential for wind generation on the Guyana coastlands, Renew Energy 8 (999), pp [8] J.V. Seguro and T.W Lambert., Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis, J Wind Eng Ind Aerodyn 85 (2), pp [9] I.Y.F. Lun and J.C Lam., A study of Weibull parameters using long-term wind observations, Renew Energy 2 (2), pp [2] A. Balouktsis, D. Chassapis and T.A. Karapantsios., A nomogram method for estimating the energy produced by wind turbine generators, Solar Energy 72 (22), pp [2] L. Zubair., Diurnal and seasonal variations in surface wind at Sita Eliva. Sri Lanka. Theoretical and Applied Climatology, 7 l, I 9-27, 22.