When optimizing the configuration of electric vehicle charging piles, it’s necessary to consider the limited number of charging piles in the parking lot. We assume that the charging information can be shared with EVs in real-time to provide decisions for charging decisions and path planning. 3.11.2. Route planning
The demand for slow charging piles is only 18. Its total number is 30. There is a reduction of 80% compared with the 153 charging piles obtained from the charging demand forecast. Assume that the time cost of electric vehicles to queue or transfer to a new charging station is the same as the time cost of fuel vehicles.
The data of charging piles include the charging pile code, location, user’s code, start time, end time, charging capacity, etc. 2.2. Research Framework
The optimization model aims to design the configuration of charging piles to minimize the sum of electric vehicle queueing time, gasoline vehicle queueing time, and vehicle transfer time to idle parking lots. The model is solved by the genetic algorithm. This paper takes the Wulin Square business district in Hangzhou as a real-world example.
When the number of EVs increases by 300 %, the optimal number of charging piles for the PV-ES-CS near hospitals increases significantly from 5 to 40. However, the optimal number of charging piles for the PV-ES-CS near office buildings does not increase from 5.
Charging pile configurations may change drivers' parking choices, therefore, leading to better parking allocation and resource utilization. Based on the ABM, this paper proposes a simulation optimization method, which combines the charging demand prediction and the charging pile optimization configuration problem to maximize the system benefit.