## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **References** \\ \hline PV system price & 1852.88 \(\upxi\)/kWp & [45; 46] \\ PV lifetime & 20 years & [28] \\ Battery system price & 350 \(\upxi\)/kWh & [45; 47] \\ Battery lifetime & 10 years & [47] \\ Sell price per kWh peak time & 0.0966 \(\upxi\)/kWh & [48; 49] \\ Sell price per kWh off-peak time & 0.0712 \(\upxi\)/kWh & [48; 49] \\ Purchase price per kWh peak time & 0.0966 \(\upxi\)/kWh & [48] \\ Purchase price per kWh off-peak time & 0.0712 \(\upxi\)/kWh & [48] \\ Tax included in the price & 21\% & [50] \\ Increase of electricity price per year & 2\% & [51] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters with values used for the economic analysis. improves the system performance in terms of VD reduction after integrating the optimally sized PV-battery system into the studied grid. In particular, the results reflect reductions of approximately 4% and 17% for the TCO and VD, respectively. The optimal multi-objective solution of PV-battery sizing while considering the TCO and VD provides the results in Table 3. The PV farm and battery capacity with 2 MWp and 1.27 MWh, respectively, are the optimal solutions with regard to improvement of system performance, system stability, and minimization of the total cost of DG while considering the purchased and sold energy. Figure 12 compares the voltage deviation for one day before and after the implementation of the optimally sized PV farm and BESS. To verify the effectiveness and performance of the proposed FLC controller, the input/output membership functions were introduced to control the charging and discharging rates of the battery bank and facilitating B2G and G2B operations. The objective of controlling the charging and discharging rates is to provide the exact power to the grid respecting voltage, load constraints, and battery characteristics. Uncoordinated charging of batteries threatens the distribution system and it can lead to power outage and undesirable voltage sag [19]. Hence, the real time smart control charging strategy maximizes customer satisfaction and it adjusts both the energy that is delivered by the grid to the battery and the energy that is injected on the grid from the battery, with fair power allocation. To perform energy management, the real time charging and discharging rates could be controlled via proper controllers with the aim of achieving peak shaving and enhancing the battery to be charged during off-peak times and discharged during on-peak times. Figure 13 illustrates \begin{table}
\begin{tabular}{c c c} \hline  & **TCO (€)** & **VD (\%)** \\ \hline Before optimization & \(1136\times 10^{3}\) & 4.35 \\ After optimization & \(1094\times 10^{3}\) & 3.62 \\ \hline \end{tabular}
\end{table} 
Table 2: System improvement after implementation of the optimally sized DG in year 2017. Figure 12: Voltage deviation before and after implementation of the optimally sized Distributed Generator (DG) and battery. the load voltage profile through 24 hours in one-second step time. Moreover, Figure 14 shows the behavior of the load, grid, PV, and SOC in 24 h on January 1 st in one farm. The results show that, in winter, the maximum power generation from PV can be 1.2 MW due to low irradiation. In the meanwhile, the supplied power from the grid between 10:00 a.m. and 05:00 p.m. decreases and the battery is charged. During peak hours (e.g., 9 a.m., 7:30 p.m.), corresponding to high load demand (see Figure 2), and when the voltage drops below 0.98, the designed controller incites the battery to discharge, as a 'High SoC' (between 81% and 100%) characterizes its SoC. During off-peak hours, (e.g., 2:30 a.m., 3:30 p.m., 23:30 p.m.), and when the voltage is very high (VH), the designed controller incites the battery to charge. In the case of a normal voltage (e.g., 7:30 a.m., 4:30 p.m.), the battery is neither in charge nor in discharge mode.