# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 5 Case Analysis
### Analysis of the Impact on the Distribution Network Side  
In the future energy society, it is essential for the power grid to maintain resiliency enhancement [32]. Due to the charging behavior of EVs, the charging load exhibits a bimodal characteristic, mainly concentrated in the morning peak hours (8-10 am) and evening peak hours (7-9 pm). During the morning peak hours, the charging load is mainly concentrated in the work area, such as charging station 1. During the evening peak hours, the charging load is concentrated in the residential area, such as charging station 5. This is because most of the charging demand comes from work vehicles, which are more likely to search for charging stations in commercial and work areas during their commute. When charging demand arises on the way back home, EVs are already closer to their destination, located in residential areas, leading to most of them accessing residential charging stations. The location of the charging stations in the power distribution network is indicated in Table 4.  
The charging load in the two scenarios is shown in Figure 13a,b. In the unguided scenario, during the morning peak hours, a larger proportion of EVs prioritize work and entertainment needs. Based on their travel power consumption characteristics, these EVs tend to charge at stations 1 and 3, located within the working area and the central business district, respectively. During the evening peak hours, the vehicles return to residential areas, and station 5 is located at the center of the residential area. EVs that prioritize distance-based routes will choose station 5 for charging. This results in temporal and spatial imbalances in the load peaks, and the slow driving speed in congested areas leads to increased power consumption of EVs. In the time-guided scenario, considering that the routes around high-load charging stations are more congested, leading to longer travel times, it can be observed that the charging load at high-load stations in the original unguided scenario is reduced, while the charging load at underutilized stations is increased. This overall balances the temporal and spatial distribution of the charging load, which is beneficial in reducing the pressure on power distribution networks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **W** & **C** & **R** \\ \hline Charging station & NO. 1 & NO. 2 & NO. 3 & NO. 4 & NO. 5 \\ Distribution network & Node 8 & Node 11 & Node 14 & Node 21 & Node 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Location of the charging station in the power distribution network. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Triggering Location** & **Charging Path** & **Distance (km)** & **Time Cost (h)** & **Energy Consumption (kWh)** \\ \hline Unguided & 13 & 13–14 & 6 & 0.21 & 1.09 \\ Time-guided & 7 & 7–8 & 7 & 0.17 & 0.97 \\ Difference & \(\backslash\) & \(\backslash\) & +1 & \(-\)0.04 & \(-\)0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of midway charging paths in two scenarios. In Figure 14, in the scenario where the EV penetration rate is 35%, node 14 experiences a significant drop in voltage during two charging peak periods. In the early morning, at 9 o'clock, without guidance, the voltage drops to below 0.93, exceeding the safety operating requirement range (7% below the rated voltage). However, after implementing the time guidance strategy, the degree of voltage drop at node 14 is reduced, and the voltage is raised to 0.935, which is beneficial for the safe and stable operation of the power grid. Figure 15 illustrates the voltage levels at different nodes at 8 am under varying EV penetration rates. Under 15% and 35% penetration rates, the voltage remains around 0.93, which is acceptable. However, when the penetration rate reaches 50%, some nodes experience voltages below 0.9, which can impact the safety of the power grid. This indicates that as the scale of EVs expands in the future, it will pose even more challenging demands on power grid operations.