# Analysis and Prioritization of the Floating Photovoltaic System Potential for Reservoirs in Korea
## 2 Study Area and Data
### Point Irradiance Calculation and Interpolation  
The solar irradiance was calculated based on an equal-area-equal-angle sky division grid [17] with the TMY dataset of 16 cities. Beam and diffuse irradiances for each position in the celestial sphere were calculated by summing all the irradiance over the entire year for a time interval. This study refers to this irradiance intensity map of the sky as Sunmap. Figure 4a shows the average of Sunmap for a tilt angle of 20\({}^{\circ}\) from all 16 cities, and Figure 4b shows the average of total irradiance as a function of solar panel direction.  
The top is north, and the center point represents the zenith. The sky diffuse model [18] is used when calculating the diffuse irradiance in the sky. The total incident irradiance of the tilted surface is  
Figure 4: (**a**) Mean Sunmap of TMY dataset in Korea with a tilt angle 20\({}^{\circ}\); (**b**) Mean total irradiance with respect to solar panel direction.  
Figure 3: Overall study process to select suitable reservoirs and assess the potential of floating photovoltaic systems in Korea.  
the summation of all positive normal intensities from the entire sky. Considering the irradiance of each city in the TMY dataset, it was assessed that the optimal panel direction of Korea is southward, and the optimal tilt angle is about 30\({}^{\circ}\) for most cities. However, many solar panels in the floating PV system have been installed with a tilt angle close to horizontal. National Renewable Energy Laboratory (NREL, Golden, Colorado, USA) research has shown that a tilt angle of 11\({}^{\circ}\) is the typical mounting angle for floating solar systems in Tokushima, Japan [19]. Other installed systems in Italy and Singapore also have a tilt angle close to the horizontal [20]. This preference is derived for reasons such as maximizing power density or structural stability. Therefore, this study sets a tilt angle of 20 \({}^{\circ}\), which has a difference in irradiance of less than 1%, compared to a tilt of 30 \({}^{\circ}\).  
Point irradiance data was interpolated to generate an irradiance map to estimate the irradiance in all areas in Korea. Interpolation is a technique to estimate values between known data. Kriging is a widely used geostatistical interpolation method, which calculates weighting from the correlation of known points. Although it is statistically powerful and is preferred in many cases, a sufficient number of points is necessary to estimate the statistical relationship of each point. Therefore, kriging is not applicable in this study, so the inverse distance weighting (IDW) method was applied. IDW is a deterministic method, which is frequently used in many studies [21]. As shown in Equation (1), the IDW method interpolates unknown values based on distances and values of known points.  
\[I_{p}=\frac{\sum_{i=1}^{n}\frac{I_{i}}{d_{i}^{p}}}{\sum_{i=1}^{n}\frac{1}{d_{i }^{p}}}, \tag{1}\]  
where \(I_{p}\) is the irradiance of an unknown point, \(n\) is number of points, \(i\) is an index of a point, \(I_{i}\) is irradiance of the known \(i\)th point, \(d_{i}\) is the distance between the unknown point and the known \(i\)th point, and \(P\) is power parameter. This method was used to generate a renewable energy resource atlas in Korea [22]. In addition, [PERSON] et al. [23] showed that the accuracies of IDW and co-kriging are similar for Turkey.