## 5 Results Analysis and Discussion  
Table 11 presents the comparison results of our work with [44; 45; 46]. The outputs are presented in terms of the total numbers of EVs used, voltage drop points (with V2G), total capacity of the EV to the network, and the total benefit of loss reduction. The authors in [34] estimated and located 975 EVs in the network with a total capacity of 14,625 kW. In contrast, ref. [46] explored four methods (Competition over resource optimization--COR, Gray Wolf Optimization--GWO, Water Cycle Algorithm--WCA, and Whale Optimization Algorithm--WOA approaches), resulting in the location of 600, 687, 735, and 712 EVs in the network, respectively, with the same total capacity of 14,625 kW. On the other hand, ref. [47] did not consider EVs in the distribution network; instead, they estimated and located about 5 MW of DGs in the network, distributed across three load types (light, medium, peak). Consequently, their approach demonstrates the potential for significantly reducing losses and increasing benefits compared to others. This study achieves a higher benefit in loss reduction with a smaller number of EVs compared to [34; 46]. In addition, the results in [34; 46] are based on specific parameters, such as \(\mathrm{Pv}=15\) kW and \(\mathrm{ES}=50\) kWh, without considering other network conditions. Furthermore, the optimization constraint for bus number 8 in [34; 46] is not within the appropriate range (voltage drop occurs at bus number 8 during peak hours). This paper, by considering various situations, demonstrates that utilizing fast charging stations and vehicles with higher battery capacities can significantly reduce the total number of EVs while improving voltage drop during peak times.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Methodology** & **Network** & **Bus** & **Optimum** & **Voltage** & **Total** & **Benefit** \\  & **Condition** & **Number** & **Number** & **Drop** & **Capacity** & **of** \\  & & & **of EVs** & **Points** & **of** & **Loss** \\  & & & **in Each** & **(with** & **Network** & **Reduction** \\  & & & **Bus** & **V2G**) & **(W)** & **(USD)** \\ \hline Proposed & Peak load & 6 & 249 & - & 8265 & 62,266.4 \\ approach & \(P_{\mathrm{p}}\): 15 kw & 8 & 302 & & KW & \\ (PSO) & ES: 50 kwh & & & & & \\  & (200 h) & & & & & \\  & \(P_{\mathrm{p}}\): 50 kw & 6 & 58 & - & 6600 & 66,483.2 \\  & ES: 100 kwh & 8 & 74 & & KW & \\  & \(P_{\mathrm{p}}\): 100 kw & 6 & 26 & - & 5800 & 68,552.4 \\  & ES: 100 kwh & 8 & 32 & & KW & \\ \hline Competition & Peak & 2 & 100 & 8 & 14,625 & 33,010 \\ over & load & 3 & 300 & & KW & \\ resource & (200 h) & 6 & 200 & & & \\ optimization & \(P_{\mathrm{p}}\): 15 kw & & & & & \\ algorithm & ES: 50 kwh & & & & & \\
[46] & & & & & & \\ \hline Gray wolf & Peak & 2 & 107 & 8 & 14,625 & 34,110.38 \\ optimizer & load & 3 & 350 & & KW & \\
[46] & (200 h) & 6 & 230 & & & \\  & \(P_{\mathrm{p}}\): 15 kw & & & & & \\  & ES: 50 kwh & & & & & \\ \hline Water cycle & Peak & 2 & 115 & 8 & 14,625 & 38,664.45 \\ algorithm & load & 3 & 370 & & KW & \\
[46] & (200 h) & 6 & 250 & & & \\  & \(P_{\mathrm{p}}\): 15 kw & & & & & \\  & ES: 50 kwh & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results comparison.