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
  • [1] Chen L., Zhang H., Ni F., Et al., Present situation and development trend for construction of electric vehicle energy supply infrastructure, Automation of Electric Power Systems, 35, 14, pp. 11-17, (2011)
  • [2] Xiao X., Wen J., Tao S., Et al., Study and recommendations of the key issues in planning of electric vehicles' charging facilities, Transactions of China Electrotechnical Society, 29, 8, pp. 1-10, (2014)
  • [3] Liu W., Zahng L., Liu Z., Et al., A development pattern argumentation method for battery electric vehicles in cities, Automation of Electric Power Systems, 38, 24, pp. 34-40, (2014)
  • [4] Liu H., Zeng P., Guo J., An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic, Journal of Modern Power System and Clean Energy, 3, 2, pp. 232-239, (2015)
  • [5] Dong J., Liu C., Lin Z., Charging infrastructure planning for promoting battery electric vehicles: an activity-based approach using multiday travel data, Transportation Research Part C: Emerging Technologies, 38, pp. 44-55, (2014)
  • [6] Lam A., Leung Y., Chu X., Electric vehicle charging station placement: formulation, complexity, and solutions, IEEE Trans on Smart Grid, 5, 6, pp. 2846-2856, (2014)
  • [7] Gao C., Zhang L., Xue F., Et al., Study on capacity and site planning of large-scale centralized charging stations, Proceedings of the CSEE, 32, 31, pp. 27-34, (2012)
  • [8] Lu X., Liu N., Tang Q., Et al., Optimal capacity configuration of electric vehicle battery swapping station considering service availability, Automation of Electric Power Systems, 38, 14, pp. 77-83, (2014)
  • [9] Gao C., Wu Q., Xue F., Et al., Demand planning of electric vehicle battery pack under battery swapping mode, Power System Technology, 37, 7, pp. 1783-1791, (2013)
  • [10] Liu Z., Wen F., Ledwich G., Optimal planning of electric vehicle charging stations in distribution systems, IEEE Trans on Power Delivery, 28, 1, pp. 102-110, (2013)
123