Optimal planning of electric vehicle charging stations based on life cycle cost and quantum genetic algorithm

被引：0
|
作者：
Huang, Xiaoqing [1 ]
Yang, Hang [2 ]
Chen, Jie [1 ]
Jiang, Lei [1 ]
Cao, Yijia [1 ]
机构：
[1] College of Electrical and Information Engineering, Hunan University, Changsha
[2] Shandong Power Economic Research Institute, State Grid Shandong Electric Power Company, Jinan
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2015年 / 39卷 / 17期
基金：
中国国家自然科学基金;
关键词：
Charging station; Life cycle cost; Optimal planning; Power grid; Quantum genetic algorithm;
D O I：
10.7500/AEPS20150323009
中图分类号：
学科分类号：
摘要：
Optimal planning of electric vehicle charging stations is an important research area in the study of flexible interaction between electric vehicle and smart grid. The calculation method of cost-benefit and life cycle cost of electric vehicle charging stations is analyzed in the operation period of electric vehicle charging stations, based on which, a method of calculating charging station capacity using the data from the traffic flow is proposed, and an optimal objective of the operator gaining best net present value is proposed. With the traffic flow, the power quality and economy of grid and charging demand of customer as constraints, the location and capacity of charging stations can be determined. In addition, the optimal planning model of charging stations considering life cycle cost theory is proposed, with the quantum genetic algorithm used to solve the model. The simulation of the example has confirmed that the optimal planning model and solving method are effective. ©2015 Automation of Electric Power Systems Press
引用
收藏
页码：176 / 182
页数：6
相关论文
共 24 条
  • [21] Dai Q., Duan S., Cai T., Et al., Cost-benefit model and its sensitivity analysis for battery charging and swapping station for electric vehicles, Automation of Electric Power Systems, 38, 24, pp. 41-46, (2014)
  • [22] Yang J., Zhang M., Chen X., A class of the flow capturing location allocation model with service radius, Systems Engineering-Theory & Practice, 1, pp. 117-122, (2006)
  • [23] Bose N.K., Garga A.K., Neural network design using Voronoi diagrams, IEEE Trans on Neural Networks, 4, 5, pp. 778-787, (1993)
  • [24] Lee J.C., Lin W.M., Liao G.C., Et al., Quantum genetic algorithm for dynamic economic dispatch with value-point effects and including wind power system, International Journal of Electrical Power & Energy Systems, 33, 2, pp. 189-197, (2011)
123