## 5 Results and Analysis
### The Transmission Loss Optimization Results  
Based on the 0-1 Integer Programming Model, which is proposed in Section 3.1, Figure 12 shows the optimal charging strategy for a selected domestic electric vehicle. In addition, by analyzing power fluctuation of the given network, transmission loss of the given network can be calculated. Table 3 summarizes the transmission loss of the given network under different conditions.  
Figure 12 reveals that for a selected domestic electric vehicle, optimal charging periods are vastly different if seasonal factors are considered, and this is mainly reflected in two aspects. Firstly, the selected electric vehicle needs a longer charging period in summer and winter, but a shorter charging period in spring and autumn, if seasonal factors are considered. This is because more energy is consumed in cooling and heating systems when considering seasonal factors. Another important  
Figure 12: Optimal charging strategy for a selected domestic electric vehicle in different seasons; (a) spring, (b) summer, (c) autumn, (d) winter.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Seasons and Cases**} & \multicolumn{1}{c}{**Daily Network Demands (MW-h)**} & \multicolumn{1}{c}{**Network Loss (MW-h)**} & \multicolumn{1}{c}{**Loss Rate (\%)**} \\ \hline \multirow{4}{*}{Spring} & Case 1  & 57.61 & 1.66 & 2.88 \\  & Case 2  & 59.94 & 1.96 & 3.27 \\  & Case 3  & 1.86 & 3.10 \\  & Case 4  & 59.94 & 1.88 & 3.14 \\ \hline \multirow{4}{*}{Summer} & Case 1  & 75.10 & 2.85 & 3.79 \\  & Case 2  & 78.13 & 3.25 & 4.16 \\  & Case 3  & 78.13 & 3.05 & 3.90 \\  & Case 4  & 78.13 & 3.12 & 3.99 \\ \hline \multirow{4}{*}{Autumn} & Case 1  & 58.08 & 1.69 & 2.91 \\  & Case 2  & 60.48 & 1.99 & 3.29 \\  & Case 3  & 60.48 & 1.89 & 3.13 \\ \hline \multirow{4}{*}{Winter} & Case 1  & 65.31 & 2.18 & 3.34 \\  & Case 2  & 68.04 & 2.63 & 3.87 \\  & Case 3  & 68.04 & 2.50 & 3.67 \\  & Case 4  & 2.54 & 3.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A summary of the given network transmission loss under different conditions.  
aspect is that the optimal electric vehicle charging time period may be shifted if seasonal factors are considered. For example, in Figure 12c, the selected electric vehicle is charged between 23:00 to 24:00 when considering seasonal factors, while this period is postponed to 2:00 to 3:00 if the seasonal factors are ignored. The reason for this is that when considering seasonal factors, the load charging curves may lag behind or be ahead of the average electric vehicle load charging curves for one year, which has been demonstrated in Section 5.1.  
Table 3 shows that the average network transmission loss rate for one year increases to 3.65%, which is about a 13.06% increase if electric vehicle charging loads are connected to the network. Therefore, with the increasing number of domestic electric vehicles, the average network transmission loss increases significantly. To reduce transmission loss, an optimal electrical vehicle charging strategy should be applied. By applying the proposed electric vehicle charging strategy, the average network loss rate for one year drops to 3.50%, which is about a 4.11% reduction in average if seasonal factors are ignored. On the contrary, when seasonal factors are taken into account, the network transmission loss rate can further be reduced to 3.45% by applying the proposed optimal charging strategy. Therefore, the simulation results verify the efficacy of the proposed 0-1 Integer Programming model and emphasize the necessity of considering seasonal factors when regulating electric vehicle charging loads.