Investigation of wind energy potentials in Brunei Darussalam

Abstract

Conventional power generation mainly depends on natural gas and diesel oil in Brunei Darussalam. The power utility company is now thinking of power generation using natural wind. In this paper, wind energy, being one of the most readily available renewable energy sources, was studied. The wind characteristic, velocity and directions were studied using Weibull distribution based on the measurement of wind speed at two different locations in Brunei Darussalam. These wind speed distributions were modeled using the Wind Power program. The wind rose graph was obtained for the wind direction to analyze the wind power density onshore and offshore. Based on this analysis, it has been found that the wind speed of 3 to 5 m/s has a probability of occurrence of 40%. Besides, the annual energy production at a wind speed of 5 m/s has been found to be in the range between 1000 and 1500 kWh for both the locations in Brunei Darussalam.

Keywords

wind speed / Weibull distribution / wind rose / wind energy / wind power

Introduction

The continuing demand for renewable energy is on the rise due to reduction of conventional reserved fuels resulting from their ongoing use for generation of electrical power. Brunei Darussalam, which enjoys its economic stability with the availability of significant oil and gas resources, is currently shifting its interest toward renewable energy resources due to two major issues. First, based on the current consumption rate of the non-renewable energy resources in this country, it is envisioned that these resources may dry out by 2050. And secondly, based on the fact that an increase in the consumption of oil and gas leads to global warming, this shift has become an obvious step. In this case, wind energy is being considered as a prime candidate because it is readily available. Electrical power is generated from natural wind by using a wind farm or wind turbines. The wind turbine converts the kinetic energy of wind into mechanical energy. This mechanical energy rotates the electrical generator which generates electrical power. Since, the electrical power output of a wind turbine is proportional to the cubic function of wind speed, it changes substantially with any change in wind speed. Wind speed is not a continuous function of time; it varies randomly with respect to season, elevation, and location. For example, at the sea beach, the velocity of wind is stronger compared to other locations. These kinds of variable characteristics of wind require researchers’ attention.

Recently, harnessing wind energy has become an important research issue in the renewable energy research domain. The hourly wind data collected for five years from 20-nine weather stations have been analyzed to identify the potential location for wind energy applications in Oman [¹]. Due to the seasonal power demand, a seasonal approach has also been introduced to identify the wind potential on different seasons. Finally, a scoring approach has been introduced in order to classify the potential sites based on the different factors mentioned in Ref. [¹]. Wind velocity and directions have been recorded at different times on the Tungku beach and compared with the data recorded by the weather station in Brunei Darussalam [²]. Orlova et al. [³] have conducted the calculation based on the decennial wind velocity monitoring at a height of 10 m (the height of the weather vane location) at the Tamdy (Uzbekistan) weather station with account for the variation of the temperature stratification in the course of the day and year. The results obtained have also been compared with the data of the aerologic observations at the weather station and with the main regularities of the variations of the velocity and specific power of the wind flow. This comparison has confirmed the correctness of the use of the calculation procedure for the specific orographic conditions.

A hybrid research methodology has been employed to identify most critical factors that affect the flexibility of the wind power industry chain [⁴]. These factors are structural flexibility, production flexibility, operational flexibility, technological flexibility, development flexibility, construction flexibility, and policy flexibility. The case study suggests that it is feasible to utilize this novel method to evaluate the flexibility of the wind power industry chain. The outcomes of the flexibility analysis provide useful information to assist the decision making process for both government and industry. The wind energy potential in Iberia has been assessed for recent-past (1961–2000) and future (2041–2070) climates using a COSMO-CLM simulation driven by ERA-40 by selecting a 2 MW rated power wind turbine [⁵]. The mean potentials, inter-annual variability, and irregularity have also been discussed on annual/seasonal scales and on a grid resolution of 20 km. Wang et al. [⁶] have conducted a medium-term wind speed forecasting performance analysis for three different sites using support vector regression (SVR) in Xinjiang Uygur Autonomous Region in China, utilizing the daily wind speed data collected over a period of eight years. The experimental results suggest that the hybrid models can forecast the daily wind velocities with a higher degree of accuracy over the prediction horizon compared to the other models. Djairam et al. [⁷] have explored a novel method of wind energy generation using an electrostatic wind energy converter. The annual wind energy estimation has been performed at the Vadravadra of Gauisland in Fiji [⁸]. The method for optimum wind turbine installation has also been proposed at the cyclone prone area. Airborne wind energy has been proposed as a new renewable technology that promises to deliver electricity at low costs and in large quantities [⁹].

The average wind from April 2007 to March 2008 in Aimangala at central dry zone part of Karnataka, India has been statistically analyzed to determine wind energy potential for electrical power generation by grouping the seasonal observations [¹⁰].

Rajeevan et al. [¹¹] have proposed a mathematical relationship which can be useful to wind power planners and policy makers. From a reliability point of view, to get optimum reliability in power generation, it is better to select a wind turbine generator which is best suited for a site.

The wind energy and wind assessment in some sites selected (Derna, Musrata, Zuara and Sebha) in Libya have been analyzed [¹²]. Here, the researchers have provided background information about wind power and its resources. In addition, a review of available data obtained from the representative meteorological stations, has been included in the paper.

Renewable energy supply assessment has been made to compute the utilization of solar thermal energy for domestic and industrial water heating in Brunei Darussalam [¹³].

Wind velocities and directions (sea and land) have been recorded in different days and times, compared with the weather data from the Brunei Darussalam Meteorological Service and the potential of wind energy has been predicted from the available data collected [²].

In this paper, wind speed data from the locations near Berakas area (location A) and Kuala Belaitsea beach (location B) in Brunei Darussalam were collected and analyzed. Location A is situated 2–3 km in land from the South China Sea shore. There are hilly regions and forests between location A and the sea. Location B is near the sea.

Weibull distribution

Weibull distribution [¹⁴] is the main mathematical tool for making analysis of wind energy. There are two parameters which characterize the Weibull distribution. These are the shape parameter k(dimensionless) and scale parameter c(m/s). The Weibull cumulative distribution function (CDF) is represented as

(1)F\left(v\right)={\displaystyle 1-{\mathrm{e}}^{\left[{-\left(\frac{v}{c}\right)}^k\right]}}.

The probability distribution function (PDF) can be defined as

(2)f\left(v\right)={\displaystyle \frac{\partial F\left(v\right)}{\partial v}}.

ƒDifferentiating Eq. (1) with respect to v provides

(3)f(v)={\displaystyle \frac{\partial F(v)}{\partial v}=\frac{\partial }{\partial v}(1-{{\mathrm{e}}^{\left(\frac{v}{c}\right)}}^k)=\left(\frac{k}{c}\right){\left(\frac{v}{c}\right)}^{k-1}}{{\mathrm{e}}^{\left(\frac{v}{c}\right)}}^k.

The average wind speed can be defined as

(4)\overline{v}={\displaystyle {\int }_{\mathrm{0}}^{\infty }v\hspace{0.17em}f\left(v\right)\hspace{0.17em}\partial v}.

Substituting Eq. (3) into Eq. (4) yields

(5)\overline{v}={\int }_0^{\infty }vf(v)\partial v={\int }_0^{\infty }v\left[\right(\frac{k}{c}\left){\left(\frac{v}{c}\right)}^{k-1}{{\mathrm{e}}^{\left(\frac{v}{c}\right)}}^k\right]\partial v.

To simplify Eq. (5), consider

(6)x={\left({\displaystyle \frac{v}{c}}\right)}^k,

(7)v=c{x}^{\frac{\mathrm{1}}{k}}.

ƒDifferentiating Eq. (6) with respect to wind speed v yields

(8){\displaystyle \frac{\partial x}{\partial v}}=\left({\displaystyle \frac{k}{c}}\right){\left({\displaystyle \frac{v}{c}}\right)}^{k-\mathrm{1}},

(9)\partial x=\left[\left({\displaystyle \frac{k}{c}}\right){\left({\displaystyle \frac{v}{c}}\right)}^{k-\mathrm{1}}\right]\partial v.

Substituting Eqs. (7) and (9) into Eq. (5),

(10)\overline{v}={\int }_0^{\infty }v\left({\mathrm{e}}^{-x}\right)\partial x={c\int }_0^{\infty }{x}^{\frac{\mathrm{1}}{k}}{\mathrm{e}}^{-x}\partial x.

The Gamma function is defined as

(11)\mathrm{\Gamma }\left(n\right)={\int }_0^{\infty }{\mathrm{e}}^{-x}{x}^{n-1}\partial x.

Based on the characteristic of Eq. (10), consider the following relation

(12)n=\mathrm{1}+{\displaystyle \frac{\mathrm{1}}{k}}.

Substituting Eq. (12) into Eq. (11) provides

(13)\mathrm{\Gamma }\left(\mathrm{1}+{\displaystyle \frac{\mathrm{1}}{k}}\right)={\displaystyle {\int }_{\mathrm{0}}^{\infty }{\mathrm{e}}^{-x}\hspace{0.17em}{x}^{\frac{\mathrm{1}}{k}}\hspace{0.17em}\partial x}.

Substituting Eq. (13) into Eq. (10) results in

(14)\overline{v}=c\mathrm{\Gamma }\left(\mathrm{1}+{\displaystyle \frac{\mathrm{1}}{k}}\right),

(15)c={\displaystyle \frac{\overline{v}}{\mathrm{\Gamma }\left(\mathrm{1}+\mathrm{1}/k\right)}}.

The standard deviation of wind speed is defined as

(16)\sigma =\sqrt{{\displaystyle {\int }_{\mathrm{0}}^{\infty }{\left(v-\overline{v}\right)}^{\mathrm{2}}f\left(v\right)\partial v}}\hspace{0.17em},

(17)\sigma =\hspace{0.17em}\sqrt{{\displaystyle {\int }_{\mathrm{0}}^{\infty }{v}^{\mathrm{2}}f\left(v\right)\partial v}-\mathrm{2}\overline{v}{\displaystyle {\int }_{\mathrm{0}}^{\infty }vf\left(v\right)\partial v+{\overline{v}}^{\mathrm{2}}}}.

The following integration can be modified as

(18){\displaystyle {\int }_{\mathrm{0}}^{\infty }{v}^{\mathrm{2}}f\left(v\right)\partial v}={\displaystyle {\int }_{\mathrm{0}}^{\infty }\left[v^{\mathrm{2}}\left({\displaystyle \frac{k}{c}}\right){\left({\displaystyle \frac{v}{c}}\right)}^{k-\mathrm{1}}{\mathrm{e}}^{-\left(\frac{v}{c}\right)k}\right]}\hspace{0.17em}\partial v={\displaystyle {\int }_{\mathrm{0}}^{\infty }\left[{\left(c{x}^{\frac{\mathrm{1}}{k}}\right)}^{\mathrm{2}}{\mathrm{e}}^{-x}\right]}\hspace{0.17em}\partial x,

(19){\displaystyle {\int }_{\mathrm{0}}^{\infty }{v}^{\mathrm{2}}f\left(v\right)\partial v}=c^{\mathrm{2}}{\displaystyle {\int }_{\mathrm{0}}^{\infty }{x}^{\frac{\mathrm{2}}{k}}{\mathrm{e}}^{-x}}\hspace{0.17em}\partial x.

Based on the characteristics of Eq. (19), the following parameter is defined as

n=\mathrm{1}+{\displaystyle \frac{\mathrm{2}}{k}}.

The Gamma function is modified as

(20)\mathrm{\Gamma }\left(\mathrm{1}+{\displaystyle \frac{\mathrm{2}}{k}}\right)={\displaystyle {\int }_{\mathrm{0}}^{\infty }{\mathrm{e}}^{-x}{x}^{\frac{\mathrm{2}}{k}}\hspace{0.17em}\partial x.}

Substituting Eqs. (4), (18) and (20) into Eq. (17),

(21)\sigma =\sqrt{{\displaystyle {\int }_{\mathrm{0}}^{\infty }\left(v^{\mathrm{2}}·f\left(v\right)\right)\partial v}-\mathrm{2}{\overline{v}}^{\mathrm{2}}+v^{\mathrm{2}}}=\sqrt{c^{\mathrm{2}}\mathrm{\Gamma }\left(\mathrm{1}+{\displaystyle \frac{\mathrm{2}}{k}}\right)-c^{\mathrm{2}}{\left[\mathrm{\Gamma }\left(\mathrm{1}+{\displaystyle \frac{\mathrm{1}}{k}}\right)\right]}^{\mathrm{2}}},

(22)\sigma =\sqrt{{\displaystyle \frac{{\overline{v}}^{\mathrm{2}}}{{\left[\mathrm{\Gamma }\left(\mathrm{1}+\mathrm{1}/k\right)\right]}^{\mathrm{2}}}}\left[\mathrm{\Gamma }\left(\mathrm{1}+{\displaystyle \frac{\mathrm{2}}{k}}\right)-{\left[\mathrm{\Gamma }\left(\mathrm{1}+{\displaystyle \frac{\mathrm{1}}{k}}\right)\right]}^{\mathrm{2}}\right]},

(23){\displaystyle \frac{\sigma }{\overline{v}}}=\sqrt{{\displaystyle \frac{\mathrm{\Gamma }\left(\mathrm{1}+\mathrm{2}/k\right)}{{\left[\mathrm{\Gamma }\left(\mathrm{1}+\mathrm{1}/k\right)\right]}^{\mathrm{2}}}}-\mathrm{1}}.

The shape factor, k, can be determined through a fitting procedure as

(24)k={\left({\displaystyle \frac{\mathrm{0.9874}}{\sigma /U_m}}\right)}^{\mathrm{1.0983}}.

The kinetic energy for an air mass m for a mean wind speed \overline{v} is given as

(25)E_{\mathrm{K}}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}m{\left(\overline{v}\right)}^{\mathrm{2}}.

The expression of an air mass for a given time, t and rotor cross sectional area can be written as

(26)m=\rho A_{\mathrm{r}}\overline{v}\hspace{0.17em}.

Substituting Eq. (26) into Eq. (25) yields

(27)E_{\mathrm{K}}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\rho A_{\mathrm{r}}\overline{v}\hspace{0.17em}{\left(\overline{v}\right)}^{\mathrm{2}}.

For a unit rotor area and per second, the expression of wind power density is given by

(28)D_{\mathrm{P}}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\rho {\left(\overline{v}\right)}^{\mathrm{3}}.

Data collection

The weather station is situated at a latitude of 4.56°N and a longitude of 114.55°E at location A where the apparatus is placed at a height of 14 m above the ground. The data were collected in hourly basis from the year 2012 to 2014. The second experiment was conducted at a height of 2 m near the beach area of the Kuala Belait district (location B) at a latitude of 4.56°N and a longitude of 114.55°E during the months of February and March 2015 at different times. The probe for measuring the wind speed was secured to the ground with the aid of a fixed stand and nylon strings. The probe input was connected to the Mini Air 2 equipment via connecting cables. This equipment can measure the wind speed from 3 to 20 m/s with a flow accuracy of 0.5% and 1.5% rdg. In this case, the nylon string and micro fan were used to identify the direction of the wind. The micro fan of the anemometer was placed facing the North. The android applications “Compass” software was used with the longitude and latitude to confirm the direction of the wind. The direction of the wind speed with bearing in degree and North, South, East and West was measured accordingly from the mobile application.

The data at location A was collected at one hour interval and at location B at 5 min interval. In this case, three wind speeds for the same hours of the day were selected for comparison. These readings were 4.38 m/s, 4.33 m/s, 3.92 m/s and 3.17 m/s, 4.63 m/s, 4.67 m/s for locations A and B, respectively. Although locations A and B are far away from each other, the readings during the same hours reveal a similar behavior, which shows that the wind velocity pattern in this country remains similar.

Results and discussion

Location A

As mentioned earlier, the Berakas area in Brunei Darussalamhas was considered for wind measurement which was designated as location A. The hourly wind speed recorded in this location from 2012 to 2014 is plotted in Figs. 1 and 2. From Fig. 1, it is observed that the characteristics of the hourly wind speed for the three years are almost the same. The wind speed remains constant with a value of around 1.8 m/s till the 7th hour. Then it increases to a peak value of around 4.5 m/s at around the 16th hour. After that, it starts to decrease and finally reaches to a value of around 1.8 m/s at the 24th hour. The wind speeds for the above mentioned three years are plotted against the month of the year in Fig. 3. The month to month variation in wind speed for the three years lies within a narrow band. However, the wind speeds in the months of January, February, and March are higher compared to those in the rest of the months. The wind speeds in these months are found to be 2.7 to 3 m/s and, in the rest of the months the speed is around 2.5 m/s.

The normalized frequency distribution with the wind class speed range is plotted in Fig. 3. The most frequency distribution occurrence of 43% happens with the wind class of 2.1–3.6 m/s. The distribution percentages for the wind class of 3.6–5 m/s and 3.6–5.7 m/s are around 33% and 22%, respectively. There is a slim chance of having a wind speed higher than or equal to 8.0 m/s in Brunei Darussalam.

The annual wind rose plot for location A for the years 2012–2014 was drawn using the data from Tables 1 to 7 as illustrated in Fig. 4. From Fig. 4, it is seen that the wind blowing from the South direction with a speed ranging from 5.7 to 8.8 m/s has the highest occurrence whereas the wind blowing from 23 degree South-West direction with a speed range of 3.6–5.7 m/s has the second most occurrence. Since this speed range has the probability of occurrence of 33%, most of the time, the wind turbine can produce power in the above mentioned direction (23 degree South-West).

The two Weibull parameters k and c can be calculated from the mean wind speed using Eqs. (15) and (24). These values for the monthly mean wind speed are tabulated in Table 1 for the years from 2012 to 2014 for location A. In the year 2013, the ranges of k and c are found to be 2.49–3.63 and 2.55–3.45, respectively. However, these ranges are 1.53–2.59 and 2.27–5.46, respectively at a height of 20 m [¹⁵]. The probability of occurrence for a wind speed of 4.6–5.2 m/s is found to be 22% [¹⁵]. However, the probability of occurrence in the same range is found to be 33% at a height of 14 m.

Wind power software is an interactive computer program used to calculate the mean power, annual energy output, maximum turbine efficiency, and the intermittent nature of wind power. Initially, the shape parameter ({\displaystyle \frac{\sigma }{\overline{v}}}) was calculated and entered into the program. Then, the wind speed probability density distribution was formed with respect to Weibull parameter (k) and, the Gamma function. The mean wind speed, shape and scale factor are plotted against months using the data from Tables 2 to 8 as demonstrated in Fig. 5. From Fig. 5, it is observed that the mean wind speed and the scale factor exhibit the same pattern for the three years. The scale factor shows the highest peak value of 4.5 in January, 2014 and lowest peak value of 2.4 in June and August, 2012.

The manufacturer specification of different wind turbines is listed in Table 8. Four horizontal axis wind turbines (HAWT) and three vertical axis wind turbines (VAWT) that generate an electricity power of below 5 kW were selected from manufacturer’s specification in this wind analysis. For the simulation using Wind Power program, the input value of {\displaystyle \frac{\sigma }{\overline{v}}} was considered in the range from 0.20 to 1.00. The value of {\displaystyle \frac{\sigma }{\overline{v}}} for location A was found to be 0.39. The simulation was done using the maximum wind speed of 9 m/s for all the selected turbines and, the results are displayed in Fig. 6.

For all the turbines selected, it is found that there is no power produced at the initial steady speed of 1 to 2 m/s. However, the power begins to increase exponentially corresponding to a wind speed of 3 to 9 m/s as can be seen in Fig. 6. At 9 m/s, the Windspire turbine produces the highest power of 0.747 kW, whereas the Samprey Wren produces the lowest power of 0.110 kW and the Earth-Tech turbine can start with a lower wind speed.

The power coefficient (%) is depicted in Fig. 7. At a speed of lower than or equal to 2.5 m/s, all the turbines except Earth-Tech show zero percent of power coefficient. In the case of Earth-Tech at 2.5 m/s, the power coefficient is found to be around 20%. The Earth-Tech turbine is found to be suitable for application at location A (inland) in Brunei because it can start at a lower speed with a reasonably higher power-coefficient.

When the wind speed is above 4 to 5 m/s, the power coefficients of all the turbines become more or less constant. The values of these power coefficients are around 45%, 30%, 30%, 25%, 25%, and 15% for Ampair 600-230, Earth-Tech ET500, Samprey Wren, Urban Green Energy Eddy, Turby, and Windspire, respectively.

The annual energy output was obtained from the power curve and Weibull parameters. The simulated energy output for the seven different wind turbines are plotted in Fig. 8. From Fig. 8, it is observed that the annual energy output increases linearly for all the turbines except Turby, Windspire, and Earth-Tech ET500. For these turbines, the annual output energy increases asymptotically with the wind speed. From the wind speed of 5.5 to 9 m/s, the Turby always provides the highest (6 MWh) annual energy output whereas Samprey Wren (0.5 MWh) provides the lowest annual energy output. It is also observed that the Earth-Tech provides the moderate annual energy output (4 MWh). However, the Earth-Tech turbine can start producing electrical energy in the lower range of wind speed (2 to 4 m/s).

Location B

The Kuala Belait beach area has been considered for wind measurement which has been designated as location B. The occurrence of sea breeze in day time is due to the rise of hot air with a lower density from the land surface which is replaced by the cold air movement from the surface of the ocean. During the later part of the day, the land mass becomes cool and the sea is relatively warmer. So, the wind flows from the land to the sea. The wind data were recorded for every 5 min interval from 15:10 to 17:55 for a duration of 2 h and 45 min at location B as shown in Fig. 9(a) to (d). Most of the time, the wind speeds were recorded in the afternoon except on February 13 when the wind-speed data were recorded in the forenoon. From Fig. 9, it is seen that the minimum recorded wind speed is around 3 m/s. An average wind velocity was calculated to be 4.33 m/s. It is also seen that the wind speed at 3 p.m. to the later hours has a rising tendency. At 3 p.m., the wind speeds for all the recorded days remain in the range between 4 and 5 m/s. However, the wind speeds measured before 3 p.m. were less than the value at 3 p.m. or beyond.

The wind rose diagram for location B is plotted in Fig. 10. From Fig. 10, it is seen that most of the wind is blowing from the North or North-East direction with a speed range of 3 to 5 m/s.

The value of {\displaystyle \frac{\sigma }{\overline{v}}} for location B is found to be 0.22. Using this input with the maximum wind speed of 9 m/s, the simulation was conducted for the same turbines, and the simulation results for annual energy generation were plotted in Fig. 11. From Fig. 11, it is observed that the annual energy generation for Urban Green, Ampair, Samprey Wren is low compared to the other three turbines. The other three turbines namely Earth-Tech, Turby, and Winspire have a higher annual energy generation capability, of which, the Earth-Tech turbine can be started with a lower wind speed than the other two.

Conclusions

The wind speed data were collected from near the Berakas area (location A) and Kuala Belait sea beach (location B) in Brunei Darussalam. The data from location A were collected for three years and, and that for the location B were collected for four days in February and March of 2015. Generally, the wind speed in location B is higher than that in location A. The data collected were analyzed using the Wind Power software and the appropriate mathematical models. The wind rose was plotted for both the locations. The probability of occurrence of wind speed for the range of 3–5 m/s is found to be 40%. The annual energy production at 5 m/s is found to be 1000–1500 kWh for both the locations. From the analysis, it is found that the Earth-Tech wind turbine can start at a lower wind speed. Therefore, it is suitable for wind farm power generation in Brunei Darussalam.

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