Input: EV location data set . |
Output: the location and number of charging stations and the number of charging piles in the charging station under the optimal cost . |
1: According to the location and quantity of EVs in the planned area, estimate the range of the number of charging stations in the planned area |
2: Set the initial value of the number of charging stations |
3: while |
4: Randomly select group data from as the initial position data set of the charging station, |
5: repeat |
6: Let |
7: fordo |
8: Calculate the Euclidean distance from EV , to each charging station , |
9: According to the principle of closest distance, determine which charging station each EV belongs to: |
10: Assign EVs to the corresponding charging stations: |
11: end for |
12: fordo |
13: Calculate the location of the new charging station: |
14: ifthen |
15: Update the current charging station location to |
16: else |
17: Keep the current mean vector unchanged |
18: end if |
19: end for |
20: until the current charging station location is no longer updated |
21: Current output: division of service scope of charging stations |
22: Use queuing theory [M/M/S] to calculate the number of charging piles in the charging station |
23: Calculate the total cost of deploying charging stations , 。 |
24 end while |
25: Calculate the total cost of deploying different numbers of charging stations and get the total cost data set , |