Co-optimization of charging station capacity configuration and electric vehicle charging strategy considering multiple attributes
Jiehui Zheng, Zhigang Cao, Yang Liu, Qian Wu
Abstract
With the increasing number of electric vehicles (EVs), the charging facilities appear to be overwhelmed, which leads to problems such as long queuing time and high charging costs for EVs. In addition, the disorderly charging of EVs will also lead to uneven benefits of charging stations (CSs) operators and high power loss in the distribution network. To solve these problems, we construct a capacity planning model for CSs with the objective of minimizing the total cost of EVs, CS operators and power grid. By solving this model, the capacity planning result of CSs could meet all given constraints even under a disorderly charging strategy. However, disorderly charging will lead to the unbalanced use of CSs, which may further cause the waste of charging resources. Therefore, we propose the fast charging strategy considering the utilization equilibrium of the CSs and slow charging strategy considering the total charging cost of EVs. By collecting travel plans and vehicle status information of EVs in advance, the intelligent transportation system (ITS) will centrally arranges the charging schedule for EVs and sets the time-sharing charging price for all CSs. Finally, compared with the disorderly charging strategy, we verify that the proposed fast charging strategies could improve the total income of CS operators, reduce power loss and balance the average queuing time of each CS and slow charging strategies could flexibly adjust the EVs charging cost and CSs charging income through IEEE 33-bus and road net 63-node coupling system. • A probabilistic model of EV travel characteristics is established. • A second-order cone non-linear model for charging station capacity configuration is proposed. • A coordinated optimization model for charging station planning and EV charging is proposed.