# Economic Routing of Electric Vehicles using Dynamic Pricing in Consideration of System Voltage
## 5 Conclusions  
In this paper, a method was proposed for distributing power demand between CSs to improve the system through route selection for EVs in a grid using [PERSON]'s algorithm and dynamic pricing. The EV considers the distance from the CS using the price information, as well as the time it takes to get to the destination, depending on traffic conditions. The EV uses [PERSON]'s algorithm to find the minimum charging cost route. The EV transmits information on charging time and size of the charging energy to the CS on the route of the EV. At the CS, the charging schedule of the CS is integrated by compiling the information from the EVs. The system operator compiles the charging schedule for the CSs and determines the voltage of the system based on the charging schedule. The operator judges the voltage and detects risk factors in the system in advance, changing the charging price of the CS in real time. The changed price signal is sent to each CS, and is set so that the EV reflects the new price, while information about the route is updated in the charging schedule of the CS. The configuration for the system and verification of the proposed algorithm was performed using MATLAB.  
The system operator predicts the unstable factors of the system by using a schedule that considers the path of the EV. Operational costs can be reduced because the risk can be predicted in advance, and EV users can benefit economically from dynamic pricing. In future research it will be necessary to construct a stochastic modelling of vehicles using Big-Data and to conduct studies considering sudden events. The system operator should know the correlation between the price signal and the operating cost. The more EVs and CSs, the more advantages can be obtained from stable operation of the grid through dynamic pricing.  
Conceptualization, software and writing-original draft preparation were performed by [PERSON]-J.L. Software, writing-review and editing were performed by [PERSON]-J.C. [PERSON] performed supervision, writing-review and editing. All the authors read and approved the final manuscript.  
Figure 18: Charging price of CSs in Case 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Charging Station (CS)** & **CS 1** & **CS 2** & **CS 3** & **CS 4** \\ \hline Maximum Charging Power [kW] & 662.5 & 700 & 775 & 1375 \\ Minimum Charging Power [kW] & 0 & 0 & 0 & 125 \\ Highest Charging Price [KRW/kW] & 787 & 500 & 500 & 500 \\ Lowest Charging Price [KRW/kW] & 150 & 10 & 10 & 150 \\ Lowest Voltage [p.u.] & 0.9501 & 0.9505 & 0.9721 & 0.9934 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of Simulation Results in Case 4.  
**Funding:** This work is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), and funded by the Ministry of Education, Science and Technology (Grant no. NRF-2016R1D1A1B03935712). This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20161210200610).  
**Conflicts of Interest:** The authors declare no conflict of interest.