Assessment of wind power generation potential in Perlis, Malaysia

Renewable and Sustainable Energy Reviews 38 (2014) 296–308

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Assessment of wind power generation potential in Perlis, Malaysia M. Irwanto n, N. Gomesh, M.R. Mamat, Y.M. Yusoff Centre of Excellence for Renewable Energy (CERE), School of Electrical System Engineering, Universiti Malaysia Perlis, Kangar 01000, Malaysia

art ic l e i nf o

a b s t r a c t

Article history: Received 27 September 2011 Received in revised form 18 May 2014 Accepted 20 May 2014

This paper presents analysis of the wind speed characteristics at Chuping and Kangar in Perlis, Malaysia. The characteristics consist of daily, monthly and annual mean wind speed. The Weibull distribution function is applied to analyze the wind speed characteristics and used to calculate the wind power generation potential. The wind power and energy as functions of tower height are presented and analyzed in this paper. The result shows that during 2005–2009 the mean wind speed at Chuping is 1.12 m/s, and during 2012–2013 the mean wind speed at Kangar is 2.50 m/s. Based on the analysis of the Weibull distribution function, the wind speed and probability density are, respectively, 0.97 m/s and 73% at Chuping, also 2.5 m/s and 45% at Kangar. They are important information to choose a suitable wind turbine for a wind power generation. The monthly mean wind power and energy density in the beginning (January–March) and the end (December) of year are higher than in the middle of year. The analysis result of the wind power and energy density as functions of tower height shows that higher tower height will produce higher wind power and energy density. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Wind speed Weibull distribution function Wind power generation

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 Assessment of world wind energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 2.1. Global cumulative installed wind power capacity in the world . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 2.2. Overview of wind energy potential in selected countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 2.2.1. Africa and Middle East . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 2.2.2. Asia and Pacific . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 2.2.3. Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 2.2.4. Latin America and Caribbean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 2.2.5. North America . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 3. Assessment of wind energy in Malaysia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 4. Assessment of wind energy potential in Perlis, Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 4.1. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 4.1.1. Location description of meteorological station. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 4.1.2. Wind energy resource potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 4.1.3. Weibull distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 4.1.4. Wind power density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 4.2. Assessment analysis of measured wind speed data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 4.2.1. Daily wind speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 4.2.2. Monthly wind speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 4.2.3. Wind speed distribution function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 4.2.4. Wind power and energy density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307

n

Corresponding author. Tel.: +60 175513457. E-mail address: [email protected] (M. Irwanto).

http://dx.doi.org/10.1016/j.rser.2014.05.075 1364-0321/& 2014 Elsevier Ltd. All rights reserved.

M. Irwanto et al. / Renewable and Sustainable Energy Reviews 38 (2014) 296–308

1. Introduction Energy is one of the essential inputs for economic development and industrialization. Fossil fuels are the main resources and play a crucial role to supply world energy demand. However, fossil fuel reserves are limited and usage of fossil fuel sources has negative environment impact. Therefore, management of energy sources, rational utilization of energy, and renewable energy source usage are vital [1]. Renewable energy has an increasing role in achieving the goals of sustainable development, energy security and environmental protection. Nowadays, it has been recognized as one of the most promising clean energy over the world because of its falling cost, while other renewable energy technologies are becoming more expensive [2]. Wind energy is a renewable energy produced by continuously blowing wind and can be captured using wind turbines that convert kinetic energy from wind into mechanical energy and then into electrical energy [3]. Today, wind energy is widely used to produce electricity in many countries such as Denmark, Spain, Germany, United States, and India [4]. It is necessary to carry out long-term meteorological observation to accurately assess the wind power generation potential and its characteristics. Data of wind speed is needed to assess the potential. The wind speed is a random variable and variation of wind speed over a period of time is represented by probability density function. Wind speed frequency distribution has been represented by various probability density functions such as gamma, Rayleigh and Weibull distribution. However, in recent years Weibull distribution has been one of the most commonly used, accepted, recommended distribution to determine wind energy potential [1]. A lot of researchers have been studying the wind speed characteristics and its potential as a wind power generation in many countries worldwide. The potential and the feasibility basis of the wind energy resources was analyzed by [1] in some locations of coastal regions of Turkey (Canakkale, Balikesir, Istanbul, Takirdag, Izmir, Mugla, Antakya, Mersin, Antalya, Sinop, Bartin and Ordu). The result showed that Balikesir and Canakkale among annual averages show higher value of mean wind speed. The mean annual value of Weibull shape parameter k is between 1.54 and 1.86 while the annual value of scale parameter C is between 2.52 m/s and 8.34 m/s. Analysis of the wind speed characteristics was done by [4] in Ras Benas city located on the east coast of Red Sea in Egypt using measured data (wind, pressure and temperature) and Weibull function. The result showed that the annual mean wind density is 315 kW/m2 at a height of 70 m above ground level. The monthly and seasonal variations of the wind characteristics were investigated by [2] in term of wind energy potential using the wind speed data collected between 2002 and 2008 for four meteorological stations in Liguria region, in northwest of Italy, namely Capo Vado, Casoni, Fontana Fresca and Monte Settepani. The results showed that Capo Vado is the best site with a monthly mean wind speed between 2.80 and 9.98 m/s at a height of 10 m and a monthly wind power density between 90.71 and 1177.97 W/m2, while the highest energy produced may be reached in December with a value of 3800 MWh. Six kinds of numerical methods for estimating Weibull parameters were reviewed by [5]; i.e. the moment, empirical, graphical, maximum likelihood, modified maximum likelihood, and energy pattern factor method. The result showed that the maximum likelihood, modified maximum likelihood and moment methods present relatively better ability throughout the simulation test. From analysis of actual data it is found that if wind speed distribution matches well with Weibull function the six methods are applicable, but if not the maximum likelihood method performs the best followed by the modified

297

maximum likelihood and moment methods, based on double checks including potential energy and cumulative distribution function. Wind speed and direction at 20 m and 30 m above ground level and in the Gulf of Tunis were studied by [6] during 2008. The obtained results can be used to run wind park project and confirm that the Gulf of Tunis has promising wind energy potential. A new formulation for the turbine-site matching problem was presented by [7], based on wind speed characteristics at any site and the power performance curve parameters of any pitch-regulated wind turbine as well as turbine size and tower height. The results revealed that higher tower heights are not always desirable for optimality. This paper presents analysis of the wind speed characteristics at Chuping and Kangar in Perlis, Malaysia. The characteristics consist of daily, monthly and annual mean wind speed. The Weibull distribution function is applied to analyze the wind speed characteristics and used to calculate the wind power generation potential. The wind power and energy as functions of tower height are presented and analyzed in this paper.

2. Assessment of world wind energy This following section will provide a brief overview of the wind energy potential for wind power generation around the world at the end of 1999 and 2013. The overview of wind energy potential follows the country division (Africa and Middle East, Asia and Pacific, Europe, Latin America and Caribbean, North America). Data of wind energy and installed wind power from some representatives of the country division are explained briefly. 2.1. Global cumulative installed wind power capacity in the world Global Wind Energy Council (GWEC) reports a bar graph of global cumulative installed wind power capacity in the world for the year 1999–2013 as shown in Fig. 1 [8]. The graph shows that every year, the addition of installed wind power capacity is done continuously. Its average annual additional is 25.31%; thus it can be predicted that an installed wind power capacity in 2020 is 563,643 MW. Each country division (Africa and Middle East, Asia and Pacific, Europe, Latin America and Caribbean, North America) gives a contribution of installed wind power total capacity at the end of 1999 and 2013 as shown in Figs. 2 and 3, respectively [8,9]. Significant additions of installed wind power total capacity with multiple factors are 32.18, 85.40, 13.05, 54.13 and 27.07 for Africa and Middle East, Asia and Pacific, Europe, Latin America and Caribbean, and North America, respectively. The installed wind power total capacity in the world at the end of 2013 is 23.64 times that at the end of 1999 as shown in Table 1. It indicates that the wind power generation is very interesting as a friendly alternative energy source. 2.2. Overview of wind energy potential in selected countries The overview of wind energy potential in selected countries following a top 10 countries installed wind power cumulative capacity at the end 2013 is shown in Fig. 4 [8]. Every year, these countries always do additional works of installed wind power capacity. At the end of 2013, the installed wind power total capacity in the world from the top 10 countries is 318,137 MW. China, USA, Germany, Spain, India, UK, Italy, France, Canada, Denmark and Rest of the world have installed, respectively, 91,424 MW; 61,091 MW; 34,250 MW; 22,959 MW; 20,150 MW; 10,531 MW; 8522 MW; 8254 MW; 7803 MW; 4772 MW and 48,352 MW of wind power capacity.

298


Global cumulative installed wind capacity in the world in MW (1999-2013)

5

3.5

x 10

Wind power installed (MW)

3

2.5

2

1.5

1

0.5

0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year

Fig. 1. Global cumulative installed wind power capacity in the world in MW (1999–2013). Installed wind power total capacity in MW at the end of 1999 10000


9000 8000 7000 6000 5000 4000 3000 2000 1000 0

Africa & Middle East

Asia and Pacific

Europe Country

Latin America & Caribbean

North America

Fig. 2. Installed wind power total capacity in MW at the end of 1999.

4

14

Installed wind power total capacity in MW at the end of 2013

x 10


12

10

8

6

4

2

0

Africa & Middle East

Asia and Pacific

Europe Country

Latin America & Caribbean

Fig. 3. Installed wind power total capacity in MW at the end of 2013.

North America


Table 1 Installed wind power total capacity at the end of 1999 and 2013. Country

299

Table 3 Installed wind power total capacity at the end of 1999 and 2013 in Asia and Pacific [8,9].

Installed wind power total capacity (MW) Country End of 1999

Africa & Middle East Asia and Pacific Europe Latin America & Caribbean North America Total

39 1403 9307 87

End of 2013

Additional (1999– 2013)

1255 119,813 121,474 4709

1216 118,410 112,167 4622

Multiplication factor (1999– 2013) 32.18 85.40 13.05 54.13

2619

70,885

68,266

27.07

13,455

318,136

304,681

23.64

Installed wind power total capacity (MW) End of 1999 End of 2013

Additional (1999–2013)

Multiplication factor (1999–2013)

China India Sri Lanka South Korea Japan New Zealand Australia

182 1095 3 7 68 37 11

91,424 20,150 63 561 2661 623 3239

91,242 19,055 60 554 2593 586 3228

502.32 18.40 21.00 80.14 39.13 16.84 294.45

Total

1403

118,721

117,318

84.62

Table 4 Installed wind power total capacity at the end of 1999 and 2013 in Europe [8,9]. Country

Installed wind power total capacity (MW) End of 1999

End of 2013

Germany Spain UK Italy France Denmark Portugal Sweden Poland Turkey Netherlands Romania Ireland Greece Austria

4445 1530 356 211 23 1742 60 220 7 9 410 1 73 87 42

34,250 22,959 10,531 8552 8254 4772 4724 4470 3390 2959 2693 2600 2037 1865 1684

Total

9216

115,740

Fig. 4. Top 10 countries installed wind power cumulative capacity at the end of 2013.

installed wind power capacity, it is because wind energy for wind power generation has big potential. Table 2 Installed wind power capacity (MW) and ranking in the world. Country

Installed wind power capacity (MW) and ranking 1999 [9]

China USA Germany Spain India UK Italy France Canada Denmark

2005 [10]

2013 [8]

Capacity

Ranking

Capacity

Ranking

Capacity

Ranking

182 2492 4445 1530 1095 356 211 23 127 1742

8 2 1 4 5 6 7 10 9 3

1260 9149 18,428 10,028 4430 1353 1718 757 683 3128

8 3 1 2 4 7 6 9 10 5

91,424 61, 091 34,250 22,959 20,150 10,531 8522 8254 7803 4772

1 2 3 4 5 6 7 8 9 10

The ranking of installed wind power capacity by the top 10 countries at the end of 1999, 2005 and 2013 is shown in Table 2 [8–10]. At the end of these years, each country installed wind power generation. Germany installed the biggest wind power capacity at the end of 1999 and 2005. At the end of 2013, China installed the biggest wind power capacity in the world. In this case, if an Asian developing country has achieved the biggest

2.2.1. Africa and Middle East The wind energy development in Africa is very slow. Most projects require financial support by international aid organizations, as only limited regional support exists. The New and Renewable Energy Authority (NREA) was established in Cairo, Egypt, in 1986 to act as a national focal point for expanding efforts to develop and introduce renewable energy technologies to Egypt [11]. Egypt has annual mean wind speed of 4.8 m/s at 10 m height above ground level [11,12]. For this case, 550 MW of wind power capacity was installed in Egypt at the end of 2013 [8]. The tentative time schedule for additional wind power capacity by NREA shows that new capacity of 1000 MW will be installed in Egypt in 2020 [12]. National Meteorological Service Agency (NMSA) in Ethiopia has collected and documented the wind data. The annual mean wind speed is 4.2 m/s at 10 m height above ground level [13,14]. For this case, 171 MW of wind power capacity was installed in Egypt at the end of 2013 [8]. 2.2.2. Asia and Pacific Like other developing countries, the energy demand in India is increasing rapidly. Continuous economic development and exponential population growth are driving energy demand faster than India can produce it. Based on state-wise and station-wise data of

300


Wind energy project T u rb i n e < 50 m et e r s Local building permit No EIA

T u rbi ne > 50 me t er s 1-2 turbine

3-19 turbine

No EIA

20+ turbine

EIA

Simplified PCA permit

EIA

Full PCA permit

Fig. 5. Permission regime and EIA for wind energy project in Germany [18].

wind monitoring stations for potential sites in India, 221 weather stations have been installed to monitor the wind speed [10]. Its annual mean wind speed is between 4.00 m/s and 8.25 m/s. This can be classified in the excellent wind resource category [14]. The excellent wind energy potential is used to construct wind power generation; thus 20,150 MW of wind power total capacity has been installed at the end of 2013 with the typical wind turbine size around 300 kW [8,9]. China is one of the largest energy consumers in the world. To fulfill the energy demand, wind energy development was explored and started in the late 1970s and early 1980s in the form of demonstration with foreign government grants and loans [15]. The initial projects were located in remote areas to supply shepherds in Mongolia autonomous region; most were off-grid stand-alone small wind turbines at that period. Some large-scale wind projects are under planning and construction. The total capacity of Rongcheng wind project by China Huaneng Group is 102 MW [16]. The capacity of Jiangsu Dafeng intertidal zone wind power demonstration project is 300 MW, consisting of 100 wind turbines of 3 MW [16]. The New Zealand Energy Research and Development Committee was established in 1974 in response to the oil crisis. The wind Energy Resources Survey of New Zealand started in 1974 [17]. A Wind Energy Task Force coordinated a cross-disciplinary team of engineers and physicists drawn from universities and government department. They have been made to demonstrate either a pilot farm or a well-sited single medium-sized wind power generation in New Zealand. Thus, 623 MW of wind power total capacity has been installed at the end of 2013 [8]. The wind power total capacity installed by the other countries in Asia and Pacific are shown in Table 3. The countries in Asia and Pacific which have big wind energy potential are suitable to build wind power generation. Table 3 shows the installation works of large additional wind power capacity. The installed additional wind power total capacity from the selected countries is 117,318 MW. It is 84.62 times installed wind power total capacity at the end of 1999.

2.2.3. Europe Between the end of 1999 and the end of 2013, around 92% of installed wind power total capacity in Europe is new installation (see Table 4). Germany is the country in Europe that has the largest installed wind power total capacity. It is a leader in Europe on shifting to renewable sources of energy [18]. The wind energy projects should follow the permit and environment impact assessment (EIA) regulations [18]. This permission process applies to all wind energy turbines taller than 50 m regardless of their location or the landownership or whether the investor is a private or public entity, and is carried out as a simplified permit without public involvement unless an EIA is required. The permit for a wind farm

Table 5 Installed wind power total capacity at the end of 1999 and 2013 in Latin America and Caribbean [8,9]. Country


End of 2013

Brazil Argentina Costa Rica Caribbean

20 14 46 4

3456 218 148 191

Total

84

4013

Table 6 Installed wind power total capacity at the end of 1999 and 2013 in North America [8,9]. Country


End of 2013

USA Canada Mexico

2492 127 1

61,091 7803 1992

Total

2620

70,886

according to the pollution control act (PCA) concentrates all other necessary permits and approvals. Fig. 5 shows the permission regime and EIA for wind energy projects in Germany. The EIA of wind energy project is federally regulated by the EIA Act and requires an obligatory EIA for large projects with turbine diameter size of 50 m and number of turbines 20 or more. The conditional EIA depends on the results of an initial screening process for projects with 3–19 turbines. 2.2.4. Latin America and Caribbean Despite large wind energy resources in many regions of South and Central America, the development of wind energy is very slow. This is due to the lack of a suitable wind energy policy as well as due to low electricity prices. Many wind projects in South America have been financially supported by international aid programs [9]. The installed wind power total capacities at the end of 1999 and 2013 from four selected countries in Latin America and Caribbean are shown in Table 5. 2.2.5. North America In 1998, the wind project developers in USA aimed at installing projects before the federal Production Tax Credit (PTC) expired on June 30, 1999. The PTC added $0.016 70.017/kWh to wind power


301

Fig. 6. Location of wind stations in Peninsular Malaysia.

projects for the first 10 years of a wind plant's life. Between the middle of 1998 and June 30, 1999, the final day of PTC, more than 800 MW of new wind power generation was installed in the USA [9]. Most of Canada's large-scale wind power has been developed as a direct result of a Federal production incentive implemented in 2002 [19]. Using this incentive structure as a successful model, this paper explores how an incentive tailored to remote wind power could be deployed. Based on monitoring results of 89 Northwest Territories in Canada, the mean wind speed is between 5 m/s and 8.5 m/s at 30 m height above ground level [19]. This potential of wind energy is used to build wind power generation; thus at the end of 2013, Canada has installed wind power total capacity of 7803 MW (see Table 6). Mexico ranks 9th in the world in crude oil reserves [20]. The Mexican government has made a serious commitment to include wind energy in its energy policy. It aims at reducing the dependence on fossil fuels for the generation of electricity and thus cut down the emissions of environmentally harmful gases [21]. The potential works of wind power in Mexico have been done by [22], using data collected every 10 min between 2000 and 2008 at 133 automatic weather stations around the country. The wind speed, the number of hours of wind useful for generating electricity and the potential electrical power that could be generated were estimated for each year via the modeling of a wind turbine

employing a logistic curve. The results show that Mexico has great wind power potential with practically the entire country enjoying more than 1700 h of useful wind per year and the potential to generate over 2000 kW of electrical power per year per wind turbine installed. The serious commitment has led to the installed wind power total capacity of 1992 MW at the end of 2013 (see Table 6).

3. Assessment of wind energy in Malaysia Since Malaysia lies in the equatorial region and its climate is governed by the monsoons, the potential for wind energy generation in Malaysia depends very much on the availability of the wind resource that varies with specific location. This section discusses the research works that have been carried out in Malaysia. Ten stations based on Fig. 6 have been selected in [23] for study on wind which are Alor Setar, Bayan Lepas, Cameron Highlands, Chuping, Ipoh, Kota Bahru, Kuantan, Malacca, Mersing and also the Kuala Terengganu Airport using wind speed hourly data from 2007 till the November of 2009. By using three different root tests called the Augmented Dickey–Fuller, Dickey–Fuller with generalized least squares (GLS) detrending and the Phillips–Perron test based on the random walk process, Kuantan and Alor Setar showed the highest maximum value of wind speed of 11.8 m/s and 11.5 m/s,

302


Fig. 7. Meteorological Station at Chuping, and CERE Station at Kangar, Perlis, Malaysia.

Table 7 Classification of wind energy resource. Wind resource category

Wind class

Wind speed (m/s)

Wind power density (W/m2)

Poor Marginal Moderate Good Excellent Excellent Excellent

1 2 3 4 5 6 7

3.5–5.6 5.6–6.4 6.4–7.0 7.0–7.5 7.5–8.0 8.0–8.8 Above 8.8

50–200 200–300 300–400 400–500 500–600 600–800 Above 800

respectively which is in the region of range suitable for turning the windmill but none of the stations are suitable for generating wind energy because the mean wind speed for each station does not even exceed 3.00 m/s which is the minimum value for turning the windmills. However, Mersing showed considerable promise in terms of the potential for energy production which was determined using the wind speed duration curve. Due to Malaysia's location the mean wind speed is low, measuring at 2 m/s and having erratic wind blows which vary

according to the month and region [24]. This result is in agreement with that of [25]; the research states that despite Malaysia having an average wind flow still it could generate high amount of energy especially on remote islands or the East Coast States of Malaysia, which experience a wind speed of about 15.4 m/s during strong surges of cold air from north Sabah and Sarawak [25] also described Mersing as having the highest potential for wind in Peninsular Malaysia throughout 2008–2009 but suggested that wind turbines are not to be used as a dependent energy supply; instead it recommends the use of hybrid systems integrated with either solar panels or diesel generators for constant sustainability. This is due to lightning activity in Malaysia that ranks among the highest in the world and once it strikes the wind turbines it could damage the electronic components [25]. The wind speed data of places such as Langkawi, Penang, Kuala Terengganu, Kota Bharu and Mersing in Malaysia was analyzed by [26] for 2005–2009 using the Weibull Distribution method and wind power potential in Mersing was found despite having the annual mean wind speed of approximately 2.65 m/s. The investigation also states that Langkawi Island has an annual wind speed of 1.76 m/s followed by Kuala Terengganu which is 1.69 m/s, Kota Bharu having 1.58 m/s and Penang of 1.15 m/s [26].


a

2.5

3.5

2005 2006 2007 2008 2009

3

2005 2006 2007 2008 2009

2

2.5 wind speed (m/s)

wind speed (m/s)

303

2 1.5 1

1.5

1

0.5 0.5 0 0

50

100

150

200

250

300

350

400

day of the year

0 1

b

3

4

5

6

7

8

9

10 11 12

Month of the year 7 2012 2013

6

5

2012 2013

4.5

5

4

4 Wind speed (m/s)

Wind speed (m/s)

2

3 2 1

3.5 3 2.5 2 1.5

0 0

50

100

150

200

250

300

350

400 1

Day of the year Fig. 8. Daily wind speed in Perlis, Malaysia: (a) at Chuping and (b) at Kangar.

0.5 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month of the year

Table 8 Maximum, minimum and mean wind speed. Wind speed (m/s)

Maximum Minimum Mean

Fig. 9. Monthly wind speed in Perlis, Malaysia: (a) at Chuping and (b) at Kangar.

At Chuping

At Kangar

2005

2006

2007

2008

2009

2012

2013

3.5 0.3 1.31

2.5 0.3 1.1

2.8 0.1 1.07

2.5 0.3 1.09

2.6 0.1 1.05

6.87 0.46 2.46

6.95 0.43 2.53

The METAR data from 2005 to 2011 for places in peninsular Malaysia such as Kota Bharu, Johor Bharu, Langkawi, and Kuala Terengganu, and for the east of Malaysia Sandakan, Miri, Kota Kinabalu, Kuching and Kudat is the research focus of [27] while the wind speed and direction are analyzed using WAsP software to generate wind rose chart and Weibull distribution together with the wind power density. Based on the overall analysis, Kudat shows great wind energy potential with wind power density of 21 W/m2 and Weibull scale parameter c of 2.8 m/s with shape parameter k of 1.74. Kudat also has the highest annual energy production at 14.6 MWh/year as well as highest capacity factor and full load hour at 7.6% and 661 h/year respectively [27]. The 2-parameter Weibull distribution is used by [28] to assess the wind energy potential in Kudat and Labuan from 2006 to 2008. The WRPLOT software was use to distinguish the wind direction and its magnitude. Based on the analysis, it is found that the monthly and yearly highest mean wind speeds are 4.76 m/s and 3.39 m/s at Kudat and Labuan respectively. The highest annual values of Weibull shape parameter (k) and scale parameter (c) are 1.86 m/s and 3.81 m/s respectively while the maximum wind power density and wind energy density are 67.40 W/m2 and

590.40 kWh/m2/year at Kudat for the year 2008. The probable wind speed and wind speed carrying maximum energy are estimated at 2.44 m/s at Labuan in 2007 and 6.02 m/s at Kudat in 2007. However it is suggested that large-scale wind energy generation is inappropriate but small-scale wind energy is feasible at the turbine height of 100 m [28]. This research is concurrent with the research from [29] which describes an average wind speed of 1.8–2.9 m/s at Bintulu, Kota Kinabalu, Kuala Terengganu, Kuching, Kudat, Mersing, Sandakan, Tawau and Pulau Langkawi by analyzing hourly wind speed from these places. The annual energy of the wind hitting a wind turbine with a 1 m2 swept area is in the range of 15.4–25.2 kWh/m2 [29]. In the same research, [30] presents wind speed and relative humidity using the feed forward artificial neural network (FFNN) method to predict the hourly wind speed. The parameters used to train the neural network, the mean absolute percentage error, MAPE, mean bias error, MBE, and root mean square error, RMSE, are used to evaluate the neural networks in which results show accurate prediction of hourly wind speed with MAPE, RMSE and MBE values of 43%, 0.56 and 0.35, respectively [30]. Spatial analysis was used by [31], with WAsP software that produced the Kudat wind speed and wind energy map. Furthermore, wind rose charts and Weibull curves were also generated. Results show that the highest interpolated wind speed in the selected site is 5.4 m/s, while the lowest wind speeds is 4.3 m/s. A choice of turbines suitable for the place is wind turbine with capacity 10 kW [31]. Najid et al. [32] studies the wind energy

304


2 1.8

wind speed (m/s)

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1

2

3

4

5

6

7

8

9

10 11 12

Month of the year 0.5

5 Probability density function

4.5 4 3.5 wind speed (m/s)

20012 2013 2012-2013

0.45

3 2.5 2

0.4 0.35 0.3 0.25 0.2 0.15 0.1

1.5

0.05

1

0 0

0.5 0

1

2

3

4

5

6

7

8

9

10

Wind speed (m/s)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fig. 11. Wind speed probability density: (a) at Chuping and (b) at Kangar.

Month of the year Fig. 10. Mean monthly wind speed in Perlis, Malaysia: (a) at Chuping and (b) at Kangar.

potential in Kuala Terengganu which is located in the east of Malaysia. The study is based on wind speed data, measured over 2 years period, that uses model validations from the three distribution methods. Results show that the Burr distribution using the maximum likelihood principle provides the best fit for the years 2005 and 2006 [32].

4. Assessment of wind energy potential in Perlis, Malaysia 4.1. Methodology 4.1.1. Location description of meteorological station Based on the Meteorological Station in Chuping, Perlis, Perlis (61290 N, 1001160 E) has about 795 km2 land area, 0.24% of the total land area of Malaysia, with a population of about 204,450 people [33,34], as shown in Fig. 7. The wind speed data was measured hourly during 2005–2009 at a height of 21.7 m above ground level by the Meteorological Station at Chuping and during 2012–2013 at a height of 10 m above ground level by the Centre of Excellence for Renewable Energy (CERE) Station at Kangar, Perlis, Malaysia. 4.1.2. Wind energy resource potential Renewable energy potentials are classified into different categories, namely theoretical potential, available potential, technical potential and economical potential [3].

Theoretical potential refers to the total wind energy available for extraction in a defined region without consideration of technical restrictions. Available potential refers to the part of the theoretical potential that can be harvested easily without causing impacts on the environment. Technical potential refers to the amount of wind energy that can be exploited using existing technologies and thus depends on the time point of assessment. Economical potential refers to the amount of potential wind energy that is economically viable by currently given technologies. Infrastructure or technical constraints and economic aspects define the limits for the economic potential. Therefore, the economical potential depends on the cost of alternative or competing energy resources. The wind energy resource is categorized by [35,36]; it is based on wind speed and power density as shown in Table 7.

4.1.3. Weibull distribution The value of wind speed continuously changes with time. The observed wind speed data in a period of time can be analyzed and gives information on the percentage of time for which the speed is within a specific range. To analyze the data is usually presented in the form of frequency distribution. There are several probability density functions, which can be used to present the wind speed frequency curve. The Weibull distribution is the most commonly used statistical distribution for representing wind speed data. This function has the advantage of


305

4

Wind power density(W/m.m)

3.5 3 2.5 2 1.5 1 0.5 0 1

2

3

4

5

6

7

8

9

10

11

12

Month of the year

2012 2013

60

0.8

50

0.7

Wind power density(W/m.m)

Cumulative distribution function

1 0.9

0.6 0.5 0.4 0.3 0.2 0.1 0 0

1

2

3

4

5

6

7

8

9

40

30

20

10

10

Wind speed (m/s)

Fig. 12. Wind speed cumulative probability distribution: (a) at Chuping and (b) at Kangar.

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month of the year

Table 9 Weibull distribution function. Wind speed (m/s)

Fig. 13. Monthly mean wind power density: (a) at Chuping and (b) at Kangar. At Chuping

At Kangar

2005 2006 2007 2008 2009 2012 2013 Scale parameter c (m/s) Shape parameter k Maximum pdf Wind speed on max. pdf (m/s)

1.47 2.31 0.65 1.15

1.24 2.49 0.81 1.01

1.20 2.27 0.78 0.93

1.24 2.34 0.78 0.97

1.18 2.01 0.73 0.84

2.78 1.96 0.30 1.90

2.86 2.01 0.30 2.00

making it possible to quickly determine the annual wind energy production of a given wind turbine. In Weibull distribution, the variations in wind speed are characterized by two functions [6]:

the probability density function, the cumulative distribution function. The probability density function f(v) indicates the percent of time for which the wind flows with a specific wind speed. It is expressed as [1,6] k kvk 1 v f ðvÞ ¼ exp ð1Þ c c c where v is the wind speed, c is a Weibull scale parameter and k is a dimensionless Weibull shape parameter. The cumulative distribution function FðvÞ is also called the cumulative density function or simply the distribution function; it gives the percent of time over which the wind speed is equal or

lower than the wind speed v. It is expressed by the integral of the probability density function: k Z v v FðvÞ ¼ f ðvÞ dv ¼ 1 exp ð2Þ c 0 In order to estimate Weibull k and c parameters, numerous methods have been proposed over last few years. In this study, the two parameters of Weibull are determined using mean wind speed (v) and standard deviation (s) [1,5,37]: k¼ c¼

s 1:086 v

ð1 r k r 10Þ

ð3Þ

v Γð1 þ 1=kÞ

ð4Þ

The mean wind speed (v) is given by v ¼ cΓð1 þ 1=kÞ

ð5Þ

or v¼

1 n ∑ v ni¼1 i

ð6Þ

The standard deviation (s) is given by

s ¼ c Γð1 þ 2=kÞ Γ 2 ð1 þ 1=kÞ

1=2

ð7Þ

306


obtained from the following expression [40,41]:

3

α¼ Wind energy density (kWh/m.m)

2.5

E ¼ PT

2

ð12Þ

where T is the time period. For the annual wind energy density estimation the value of 8640 h is used. 1.5

4.2. Assessment analysis of measured wind speed data 1

The most important part of the measured wind speed data is its characteristics. An evaluation of the data is needed to understand its characteristics. The characteristics can be evaluated from the daily wind speed, the monthly wind speed, the annual wind speed, the wind speed distribution function, the mean wind power and energy density. The data is measured at the height of 21.7 m above ground level by the Meteorological Station at Chuping and at the height of 10 m above ground level by the CERE Station at Kangar, Perlis, Malaysia. They become an original value of estimation of the mean wind power and energy density for other heights.

0 1

2

3

4

5

6

7

8

9

10

11

12

Month of the year

45 40 Wind energy density (kWh/m.m)

ð11Þ

The wind energy density for a period of time can be calculated as

0.5

35 30 25 20 15 10 5 0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month of the year

Fig. 14. Monthly mean wind energy density: (a) at Chuping and (b) at Kangar.

or "

s¼

0:37 0:088 lnðv21:7 Þ 1 0:088 lnðh0 =21:7Þ

n 1 ∑ ðvi vÞ2 n 1 i ¼ 1

#1=2 ð8Þ

4.1.4. Wind power density For a period of measurement the mean wind power density (the available power of wind per unit area) is given by the following expression [6]: 1 P ¼ ρ v3 2

ð9Þ

where ρ is the standard air density (ρ ¼ 1:225 kg/m3 dry air at 1 atm and 15 1C). The standard wind speed height extrapolation equation is given by [37–39] α v h ð10Þ ¼ v0 h0 where v is the wind speed at the height h, v0 is the original wind speed at the height h0 , and α is the friction coefficient, which depends on the surface roughness and atmospheric stability. In this study, the wind speed data was measured at the height of 21.7 m above the ground level; therefore the value of α can be

4.2.1. Daily wind speed The hourly wind speed data was measured by the Meteorological Station at Chuping, and CERE Station at Kangar, Perlis, Malaysia. The daily wind speed data can be calculated from the hourly data. As shown in Fig. 8, each daily wind speed curve has similar shape. The mean wind speeds are 1.12 m/s and 2.50 m/s at Chuping and Kangar, respectively. The maximum, minimum and mean wind speeds for each year are shown in Table 8. In 2005, the maximum and minimum wind speeds occurred, respectively, on 15 January and 29 March. In 2006, the maximum wind speed occurred on 7 March and the minimum wind speed on 30 April and 31 July. In 2007, the maximum and minimum wind speeds occurred, respectively, on 2 February and 31 July. In 2008, the maximum and minimum wind speeds occur on 16 February and 7 August. In 2009, the maximum and minimum wind speeds occurred, respectively, on 8 February and 22 September. 4.2.2. Monthly wind speed The monthly wind speed for every year and its mean are shown in Figs. 9 and 10, respectively. In the beginning and the end of every year (January–April and November–December) the monthly wind speeds are above 1 m/s and 2.5 m/s at Chuping and Kangar, respectively. However, in the middle of every year their values are below 1 m/s and 2.5 m/s at Chuping and Kangar, respectively. From the wind speed condition at Chuping as shown in Fig. 9(a) the highest monthly wind speed occurs in 2005 and Fig. 10(a) shows that the highest and lowest mean monthly wind speeds are 1.851 m/s in January and 0.7284 m/s in July, respectively. The highest monthly wind speeds occur in December 2013 at Kangar as shown in Figs. 9(b) and 10(b). 4.2.3. Wind speed distribution function Weibull distribution function is usually used to describe the wind speed distribution of a given location over a certain period of time. In this paper, the annual Weibull distribution function and its two parameters are derived from the available data and are shown in Figs. 11 and 12 and Table 9. The result shows that 2005 is the windiest year which has the largest scale parameter, c, of 1.47, and its wind speed of 1.15 m/s has the highest probability density, exactly 65%. 2006 has the most ‘peaked’ probability density with the highest shape parameter k of 2.49 and its wind speed is 1.01 m/s. 2007 and 2008 have the same wind speed probability density, but their wind speeds are 0.93 m/s


power and energy density as shown, respectively, in Figs. 13(b) and 14(b). The highest monthly mean wind power density of 55.86 W/m2 occurred in December and the lowest one of 1.38 W/m2 occurred in June. The highest monthly mean wind energy density is 41.56 kWh/m2 occur on December and the lowest one is 0.99 kWh/m2 occur on June. Fig. 15 shows the wind power and energy density as functions of tower height. It shows that higher tower height will produce higher wind power and energy density. At the tower height of 50 m above ground level, the wind power density is 2.13 W/m2 and 19.69 W/m2 at Chuping and Kangar, respectively. Based on Table 7, they indicate that the wind energy source in Perlis is categorized to be very poor.

40 Power density(w/m.m) Energy density (kWh/m.m)

35 30 Power/Energy density

307

25 20 15 10 5 0 10

5. Conclusion 20

30

40

50

60

70

80

90

100

In this study the wind speed characteristics in Perlis, Malaysia, are analyzed. The following conclusions can be drawn from the result of the presented study:

Tower height (m)

350 Power density(w/m.m) Energy density (kWh/m.m)

Power/Energy density

300 250 200 150 100 50 0 10

20

30

40

50

60

70

80

90

100

Tower height (m) Fig. 15. Wind power and energy density as functions of tower height: (a) at Chuping and (b) at Kangar.

and 0.97 m/s, respectively. 2009 has the lowest scale and shape parameter; its wind speed and probability density are, respectively, 0.84 m/s and 73%. During 2005–2009, from the measured data results, the wind speed and probability density are, respectively, 0.97 m/s and 73%. Based on the above analysis of the Weibull distribution function, it is important to choose a suitable wind turbine for a wind power generation.

4.2.4. Wind power and energy density The evaluations of the wind power and energy per unit area are important information of wind power project assessment. During 2005–2009 at Chuping, the wind speed data at 21.7 m above ground level is evaluated to obtain the monthly mean wind power and energy density as shown, respectively, in Figs. 13(a) and 14(a). The highest monthly mean wind power density of 3.8847 W/m2 occurred in January and the lowest one of 0.2367 W/m2 in July. The highest monthly mean wind energy density of 2.8902 kWh/m2 occurred in January and the lowest one of 0.1761 kWh/m2 in July. Based on Figs. 13(a) and 14(a) the beginning (January–March) and the end (December) of year have a high wind power and energy potential, but in the middle of year they are very low. The annual mean wind power and energy density are 0.8668 W/m2 and 7.4893 kWh/m2, respectively. Also during 2012–2013 at Kangar, the wind speed data at 10 m above ground level is evaluated to obtain the monthly mean wind

(1) The daily wind speed data can be calculated from the hourly data recorded from the Meteorological Station at Chuping, and CERE Station at Kangar, Perlis. The mean wind speeds are 1.12 m/s and 2.50 m/s at Chuping and Kangar, respectively. (2) In the beginning and the end of every year (January–April and November–December) the monthly wind speeds are above 1 m/s and 2.5 m/s at Chuping and Kangar, respectively. But in the middle of every year their values are below 1 m/s and 2.5 m/s at Chuping and Kangar, respectively. (3) Based on the analysis of the Weibull distribution function, the wind speed and probability density are, respectively, 0.97 m/s and 73% at Chuping, and 2.5 m/s and 45% at Kangar. They give important information in choosing a suitable wind turbine for wind power generation. (4) At the tower height of 50 m above ground level, the wind power density is 2.13 W/m2 and 19.69 W/m2 at Chuping and Kangar, respectively. They indicate that the wind energy source in Perlis is categorized to be very poor. (5) The analysis results of the wind power and energy density as functions of tower height show that higher tower height will produce higher wind power and energy density.

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