2. Analysis of experimental HRES installed at Deokjeokdo island

Experimental HRES facility installed at Deokjeokdo island is shown in Figure 3. The experimental HRES consists of two Darrieus type vertical axis wind turbines (VAWT) and photovoltaic (PV) panels. The total capacity of this system is 24 kW, with each wind turbine rated at 1.5 kW and solar panels of 3 kW capacity, respectively. In order to record the wind conditions such as wind speed and wind direction, a vertical tower called the "wind master" has been installed at the local site, as shown in Figure 3. Anemometer and anemoscope are attached on wind master to record wind speed and wind angle, respectively. Solar panels are inclined at 30° to capture the maximum radiations from sun. This system was being monitored for two consecutive years, i.e., 2016 and 2018.

#### 2.1 Wind potential estimation

Prior to assessing the power production from HRES, specifically from wind turbine, it is of immense importance to analyze the wind conditions of local site at first place. In current case, the wind data used for this purpose come from measured by wind master as mentioned above. Figure 4 shows the season wise plots of wind characteristics in the form of wind rose. It is clear from the figures that prevailing wind direction is south-west (180–270°); with most frequent wind speeds are in the range from 2 to 3 m/s and spring is the "windiest" season. It is to be noted that wind data were measured at 10 m height.

Weibull probability density function (PDF) and cumulative density function (CDF) are two classical tools to study the wind characteristics of a region. Both functions can be defined as follows, respectively:

$$PDF = f(\boldsymbol{\upsilon}) = (^{k}\boldsymbol{\zeta})(\boldsymbol{\upsilon}/\boldsymbol{\zeta})^{k-1} \exp\left[-\left(^{\boldsymbol{\upsilon}}\boldsymbol{\zeta}\right)^{k}\right] \left(\boldsymbol{\upsilon} \circ \mathbf{0}; k, \boldsymbol{\upsilon} \circ \mathbf{0}\right) \tag{1}$$

$$\text{CDF} = F(\nu) = \mathbf{1} - \exp\left[-\left({\text{'}\_{c}}\right)^{k}\right] \tag{2}$$

Figure 4.

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Figure 3.

Experimental HRES at Deokjeokdo island.

Evaluation of PV-Wind Hybrid Energy System for a Small Island

DOI: http://dx.doi.org/10.5772/intechopen.85221

Wind rose at Deokjeokdo island (a) winter (b) spring (c) summer (d) fall.

where k, c and v are Weibull shape parameter, Weibull scale factor and wind speed, respectively. Shape factor and scale parameters are the defining parameters for Weibull distribution [7] and they determine the abscissa scale and the width of wind speed data distribution plot, respectively. There are many mathematical approaches to calculate k and c like graphical, maximum likelihood, empirical, power density and moment method [8]. Empirical method and method of Justus and Mikhail [9] will be used in this present study to estimate k and c.

Figure 3. Experimental HRES at Deokjeokdo island.

agriculture and tourism, rather than fishing, and is actively developing tourism resources as Green Island. This island is excessively long way from primary land of South Korea, so it is not monetarily suitable to associate it with main framework for power transmission. Subsequently, this island has its very own power generation system fueled by diesel. In any case, the local government has demonstrated its enthusiasm to make Deokjeokdo island, a green island as far as power generation is concerned. Present study investigates the sustainable power source potential at the mentioned site and after that recommends an ideal HRES dependent on economic

Figure 2 demonstrates geographical details of the Deokjeokdo including the

Experimental HRES facility installed at Deokjeokdo island is shown in Figure 3. The experimental HRES consists of two Darrieus type vertical axis wind turbines (VAWT) and photovoltaic (PV) panels. The total capacity of this system is 24 kW, with each wind turbine rated at 1.5 kW and solar panels of 3 kW capacity, respectively. In order to record the wind conditions such as wind speed and wind direction, a vertical tower called the "wind master" has been installed at the local site, as shown in Figure 3. Anemometer and anemoscope are attached on wind master to record wind speed and wind angle, respectively. Solar panels are inclined at 30° to capture the maximum radiations from sun. This system was being monitored for

Prior to assessing the power production from HRES, specifically from wind turbine, it is of immense importance to analyze the wind conditions of local site at first place. In current case, the wind data used for this purpose come from measured by wind master as mentioned above. Figure 4 shows the season wise plots of wind characteristics in the form of wind rose. It is clear from the figures that prevailing wind direction is south-west (180–270°); with most frequent wind speeds are in the range from 2 to 3 m/s and spring is the "windiest" season. It is to be noted that wind

Weibull probability density function (PDF) and cumulative density function (CDF) are two classical tools to study the wind characteristics of a region. Both

CDF <sup>¼</sup> F vð Þ¼ <sup>1</sup> � exp � <sup>v</sup>ð Þ <sup>=</sup><sup>c</sup> <sup>k</sup> h i

where k, c and v are Weibull shape parameter, Weibull scale factor and wind speed, respectively. Shape factor and scale parameters are the defining parameters for Weibull distribution [7] and they determine the abscissa scale and the width of wind speed data distribution plot, respectively. There are many mathematical approaches to calculate k and c like graphical, maximum likelihood, empirical, power density and moment method [8]. Empirical method and method of Justus

and Mikhail [9] will be used in this present study to estimate k and c.

exp � <sup>v</sup>ð Þ <sup>=</sup><sup>c</sup> <sup>k</sup> h i

ð Þ v > 0; k;c > 0 (1)

(2)

2. Analysis of experimental HRES installed at Deokjeokdo island

Urumsil town and test bed of the hybrid renewable energy system.

two consecutive years, i.e., 2016 and 2018.

Wind Solar Hybrid Renewable Energy System

2.1 Wind potential estimation

data were measured at 10 m height.

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functions can be defined as follows, respectively:

PDF <sup>¼</sup> f vð Þ¼ <sup>k</sup>ð Þ <sup>=</sup><sup>c</sup> <sup>v</sup>ð Þ <sup>=</sup><sup>c</sup> <sup>k</sup>�<sup>1</sup>

assessments.

Figure 4. Wind rose at Deokjeokdo island (a) winter (b) spring (c) summer (d) fall.

$$\upsilon\_m = \frac{1}{n} \left[ \sum\_{i=1}^n \upsilon\_i \right] \tag{3}$$

<sup>k</sup> <sup>¼</sup> <sup>σ</sup> vm �1:<sup>086</sup>

Evaluation of PV-Wind Hybrid Energy System for a Small Island

DOI: http://dx.doi.org/10.5772/intechopen.85221

from Figure 4.

Figure 6.

169

Observed WPD at Deokjeokdo island (a) winter (b) spring (c) summer (d) fall.

direction is south-west.

<sup>c</sup> <sup>¼</sup> vm

Figure 5 shows the season wise Weibull plots for Deokjeokdo island prepared using 2 years measured data (2016 and 2017). These figures also show the curves for Rayleigh distributions (PDF and CDF), which are essentially Weibull distributions at k = 2. Figure 5 reveals that the most frequently occurring wind during all the seasons is 3 m/s and spring has high wind speeds, as it was also concluded above

Table 1 explains the distribution of wind coming from different directions on monthly basis. Table 1 also concludes the same as Figure 4 that prevailing wind

ð Þ 1 ≤k ≤10 (5)

<sup>Γ</sup>ð Þ <sup>1</sup> <sup>þ</sup> <sup>1</sup>=<sup>k</sup> (6)

$$
\sigma^2 = \frac{1}{n-1} \sum\_{i=1}^n \left( v\_i - v\_m \right)^2 \tag{4}
$$



Table 1.

Monthly variation in percentages of total wind speed according to wind direction ranges.

Evaluation of PV-Wind Hybrid Energy System for a Small Island DOI: http://dx.doi.org/10.5772/intechopen.85221

vm <sup>¼</sup> <sup>1</sup>

<sup>n</sup> � <sup>1</sup> <sup>∑</sup> n i¼1

<sup>σ</sup><sup>2</sup> <sup>¼</sup> <sup>1</sup>

Wind Solar Hybrid Renewable Energy System

Weibull plots at Deokjeokdo island (a) winter (b) spring (c) summer (d) fall.

Angle range [°] Percentage of total wind occurrence

Monthly variation in percentages of total wind speed according to wind direction ranges.

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

[0–30] 0 0 0 0 0 0 0 0 0 0 0 0 [30–60] 0 0 0 0 0 0 0 0 0 0 0 0 [60–90] 0 0 0 0 0 0 0 0 0 0 0 0 [90–120] 2 0 0 0 0 1 1 2 2 1 1 1 [120–150] 8 4 2 3 5 14 12 14 15 8 9 5 [150–180] 23 18 13 23 29 43 43 36 36 24 28 17 [180–210] 32 36 33 47 46 35 36 35 34 37 36 28 [210–240] 26 31 35 24 19 6 7 12 12 24 20 26 [240–270] 8 10 15 3 2 0 0 1 1 6 5 14 [270–300] 1 1 1 0 0 0 0 0 0 0 1 4 [300–330] 0 0 0 0 0 0 0 0 0 0 0 4 [330–360] 0 0 0 0 0 0 0 0 0 0 0 0

Figure 5.

Table 1.

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<sup>n</sup> <sup>∑</sup> n i¼1 vi 

(3)

ð Þ vi � vm <sup>2</sup> (4)

$$k = \left(\frac{\sigma}{v\_m}\right)^{-1.086} (1 \le k \le 10) \tag{5}$$

$$\mathcal{L} = \frac{v\_m}{\Gamma(1 + 1/k)}\tag{6}$$

Figure 5 shows the season wise Weibull plots for Deokjeokdo island prepared using 2 years measured data (2016 and 2017). These figures also show the curves for Rayleigh distributions (PDF and CDF), which are essentially Weibull distributions at k = 2. Figure 5 reveals that the most frequently occurring wind during all the seasons is 3 m/s and spring has high wind speeds, as it was also concluded above from Figure 4.

Table 1 explains the distribution of wind coming from different directions on monthly basis. Table 1 also concludes the same as Figure 4 that prevailing wind direction is south-west.

Figure 6. Observed WPD at Deokjeokdo island (a) winter (b) spring (c) summer (d) fall.

Figure 6 shows the wind power density (WPD) on the basis of seasons. The patterns being observed in Figure 6 are very much identical to patterns of Figure 4.

## 2.2 Solar potential estimation

Figure 7 shows the average solar radiations (W/m<sup>2</sup> ) over different major cities of South Korea. Daejeon has the highest solar radiations value (175 W/m<sup>2</sup> ) whereas Seoul has the lowest (145 W/m<sup>2</sup> ).

Similarly, Figure 8 shows the average values of daily solar radiations and clearness index over Deokjeokdo island, on monthly basis.

#### 2.3 Estimation of power production from wind turbine

This section presents the results such as power production from small Darrieus VAWT. Table 2 summarizes the important details about the wind turbine whereas Figure 9(a) shows the geometrical dimensions and Figure 9(b) shows the power curve of wind turbine installed at Deokjeokdo island. The blade height and chord of the turbine rotor are 3 and 0.2 m, respectively. Design blade section profile is NACA0015, while the rotational diameter of the turbine rotor is 2 m. Rated wind speed and rotor rotational speed are 13.5 m/s and 300 rpm, respectively.

Figure 8.

Table 2.

Figure 9.

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Solar radiations over Deokjeokdo island [10].

Evaluation of PV-Wind Hybrid Energy System for a Small Island

DOI: http://dx.doi.org/10.5772/intechopen.85221

Specifications of test Darrieus wind turbine.

Parameter Value Rated power, kW 1.5 Rated wind speed, m/s 13.5 Rated rotational speed, RPM 300 Cut-in wind speed, m/s 3 Chord length, m 0.2 Blade length (height), m 3 Rotational diameter, m 2 Blade profile NACA0015

Darrieus wind turbine installed at Deokjeokdo island (a) rotor dimensions (b) power curve.

Figure 10 shows diagram for data acquisition system to obtain experimental data from the wind turbine and the wind master. Turbine performance data is measured between turbine and power transducer, thus contains power generator loss. Power output is stored in battery bank first, then supplied to users after converting to AC voltages.

The commercial code, SC/Tetra, has been employed in the present numerical simulation. It solves the governing fluid dynamics equations, which consist of

Figure 7. Solar radiations over different cities of South Korea (W/m2 ) [10].
