## 2 Data Collection from the Target Area
### Examination on Counter-Traded Power for Households Using an Average of 250 kWh in a Month  
Figure 3 was made by overlapping the estimated solar power generated by 3-kW PVs in a day (green line), and the hourly power usage pattern of households using an average of 250 kWh in a month (blue line). The overlapped area (purple) indicates the counter-traded power to help illustrate how installing PVs could affect enhancing energy self-sufficiency. Counter-traded power is calculated by subtracting the power generated by PVs from the power consumed during the same time period. This way, users can see how much electricity charge could be saved by installing PVs. Assuming that households have the same power usage pattern, the hourly power usage patterns could be predicted for cases where PVs are not installed [20]. If the load pattern of the household can be altered so that more power usage could be placed within the generating time of the PVs, then the portion for counter-traded energy would rise by over 61%, as shown in Figure 3, resulting in improved economic feasibility. Therefore, in this work, counter-traded power was taken into account when selecting the adequate capacity of the ESS. Minimizing the capacity of the ESS is crucial for decreasing the initial construction costs and increasing economic feasibility. Modifying the daily load pattern of the households to be placed within the generation time of the PVs will result in a rise of counter-traded power to over 61%, and help with deciding the economically suitable capacities of the ESS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Number of Households** & **Portion** \\ \hline
50–100 kWh & 15 & 1.66\% \\
100–200 kWh & 134 & 14.81\% \\
200–300 kWh & 304 & 33.59\% \\
300–400 kWh & 257 & 28.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average energy consumption ratio per household for the 905 households in 2017.  
Figure 2: Average energy consumption per household for the 905 study subject households in 2017.  
The load patterns of certain rural farming household areas were analyzed. The data concerning the insolation of the target area for the last five years and the monthly average power usage of the households in the previous year were collected. Once the PV module and inverter were selected, then, with the simulations from the SolarPro Program and/or modeling method [21], the average power generation per day could be calculated with different environmental conditions. By combining these data with the application and correlation coefficient between the PVs and ESSs, the optimized capacities of the PVs and ESSs can be derived [22].  
Figure 4 shows the procedure for optimizing the capacity of the ESS, using Equations (1)-(5), by considering the load pattern of a rural area.  
Table 3 shows the amount of power generated by the 3 kW PVs per day for each month in 2017. When eight years of data for the same target area (2009-2016) were analyzed, the average number of generating hours showed 3.56 h. The average generation efficiency is a percentage of the generation hour when 3.56 h was set as 100%. When the power generation efficiency exceeded 100%, it could cause errors in deciding the optimum capacity of the ESS. Therefore, for those months when the generating efficiencies were over 100%, the upper limit was regulated to 100%.  
Figure 3: Average counter-traded power in a day when 3-kW photovoltaics (PVs) are installed.