## 5 Results and Analysis  
### Charging Electric Vehicle without Any Optimal Strategy  
Figure 8 shows the daily load curves of the given network without considering any optimal charging strategy in four different seasons.  
Figure 8 reveals that the gap between the maximum demand and the minimum demand of the given network in a day is approximately 1.4 MW in spring and autumn. However, this gap can increase to 2.2 MW in summer and winter. This proves that seasonal factors do have an influence on calculating power system peak-valley difference, which has further impact the calculation of power system transmission loss.  
In addition, simulation results indicate that the actual total demands of the given network should be higher in summer and winter, if seasonal factors are considered. On the contrary, the actual total demands of the given network should be lower in spring and autumn. Moreover, in summer, the actual daily load curve (considering seasonal factors) lags behind the average daily load curve (ignoring seasonal factors). However, an opposite tendency is shown in winter. For spring and autumn, the actual daily load curve and the average daily load curve are in the same phase. This is caused by domestic users' preference of charging electric vehicle. As demonstrated in Section 2.1, users' finish their last trips early in winter and late in summer. Therefore, the actual daily load curve in summer lags behind the average daily load curve and the actual daily load curve in winter is ahead of the average daily load curve. In summary, seasonal factors can affect the shape of the daily load curve of a network.  
Taking node 8 as an example, Figure 9 shows the daily voltage curves of a node in the given network without considering any optimal charging strategy in four different seasons.  
Figure 8: Daily load curves of the given network without considering any optimal charging strategy; (**a**) spring, (**b**) summer, (**c**) autumn, (**d**) winter.  
Figure 9 reveals that the voltage fluctuation rate of the given node is about 2% in summer and winter, and this rate would reduce to around 1% in spring and autumn. It is worth noting that with the connection of electric vehicles to the grid, this rate could increase about 50% for all seasons. In other words, the voltage fluctuation rate increases to about 1.5% in spring and autumn and about 3% in summer and winter if electric vehicles charging loads are connected to the grid. In addition, similar to the load curve simulation results, the actual voltage curve lags behind to the average voltage curve in summer and is ahead of the average voltage curve in winter.  
Simulation results show that seasonal factors not only affect total demand of the given network, but also influence node voltage. Therefore, it is crucial to take these factors into account when scheduling electric vehicle charging loads. Additionally, simulation results also suggest that without an appropriate electric vehicle charging strategy, the maximum peak-valley difference of the given power system is 2.2 MW, which is more than half of the network peak demand. Therefore, the optimal electric vehicle charging strategy needs to be applied to regulate power and voltage fluctuation for this network.