# Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles
## 5 Example Analysis
### Analytics of Simulation Results  
To compare the real-time cluster dispatching strategy with the disorderly charging model, the load curve in this paper is shown in Figure 5. In particular, disorderly charging refers to the practice where an electric vehicle, upon connection to the grid, charges directly at maximum power until it reaches a need.  
From Figure 5, it can be seen that a certain peak-to-valley differential already exists in the conventional load during a single day of operation, where the load peak-to-valley difference is 12.42 MW and the standard deviation is 4.18 MW. However, disorderly charging further exacerbates the peak load during hours 31-44 (15:00-21:00), where the load peak-to-valley difference is 18.82 MW and the standard deviation is 6.28 MW. Random charging causes enormous pressure on the grid for real-time dispatch. By using the optimization strategy in the article, the peak-to-valley load difference can be reduced to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Peak Hour** & **Steady Hour** & **Valley Hour** \\ \hline \multirow{3}{*}{Hours} & 110:00–15:00 & 7:00–10:00 & \\  & 18:00–21:00 & 15:00–18:00 & 23:00–7:00 \\  & 18:00–21:00 & 21:00–23:00 & \\ \cline{1-1}  & 1.28 & 0.76 & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The division of peak and valley periods in Beijing and their electricity prices.  
Figure 5: Cluster optimization effect.  
7.29 MW, while the standard deviation drops to 1.83 MW. From the load curve, it is clear that the charging load of electric vehicles is mainly concentrated in the \"Valley\" period (e.g., 0-16, 44-48), while in the \"Peak\" period, the electric vehicle cluster feeds back to the grid in the form of discharge. Such a process dramatically smooths out the volatility of the load curve and achieves peak shaving. In addition, without considering the discharge, the load peak-to-valley difference is 8.34 MW and the standard deviation is 2.58 MW. Compared to random charging, the optimization of EV clusters by the EVGMS system is advantageous.  
Figure 6 illustrates the power response curves of three different nodes which are randomly accessed by electric vehicles during the dispatch time interval, as well as the load curves of the region where the nodes are located. As the figure shows, by adopting the charging and discharging strategy in this paper, each of the access nodes for electric vehicles can perform according to a charging and discharging schedule, while calming the regional load curve fluctuations.  
Figure 7 randomly selects three clusters whose scheduling times are 17:00-10:00, 15:00-8:30, and 24:00-6:30, respectively. As can be seen, the scheduling commands issued by EVGMS and the real-time response of each cluster are basically matched. Of these, the clusters with dispatch hours of 24:00-6:30 take full advantage of the low valley tariffs to recharge, whereas the clusters whose dispatch times are 17:00-10:00 and 15:00-8:30 discharge at peak load times, which not only feeds power back into the grid but also provides revenue for the vehicle owner.  
Figure 6: Electric vehicle clusters connected to nodes 13, 21 and 32 and regional load curve.  
In terms of the economic benefits to vehicle owners, the average cost of a single charge is CNY 15.64 when charging irregularly. On the other hand, with the optimized strategy, under the assumption that the average loss of a single discharge is CNY 0.28, it costs an average of CNY 9.26 to charge and discharge in an orderly manner, which is 39.24% less than random charging. Regardless of the discharge, the typical cost to the owner of an electric vehicle with only sequential charging control is CNY 10.31, which is 10.18% higher than the average cost of sequential charging and discharging. Consequently, the cluster-sequenced charging and discharging scheduling strategy reduces the peak-to-valley load differential and simultaneously takes into account the economic profit of the electric vehicle owners.