Multi-agent-based pricing strategy for electric vehicle charging considering customer satisfaction degree

被引：0
作者：
Piao, Longjian [1 ]
Ai, Qian [1 ]
Yu, Zhiwen [1 ]
Chen, Jingpeng [1 ]
机构：
[1] School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2015年 / 39卷 / 22期
基金：
中国国家自然科学基金;
关键词：
Coordinated charging; Customer satisfaction degree; Electric vehicles; !text type='Java']Java[!/text] multi-agent development framework (JADE); Multi-agent system;
D O I：
10.7500/AEPS20150103003
中图分类号：
学科分类号：
摘要：
With the large-scale charging of increasing electric vehicles, which is of great randomness and autonomy, the far-reaching impacts on power grids can hardly be handled using centralized control. In order to solve this problem, the customer satisfaction degree is modeled as an index of vehicle user charging preferences, based on which a coordinated pricing strategy for charging stations is proposed in the multi-agent framework to implement the price-based decentralized control of electric vehicles. Furthermore, the proposed strategy is modeled on a JADE platform, which is capable of realizing the behavior and communication among electric vehicles, charging stations and smart grid agents. In the end, the grid-friendly charging behavior of electric vehicles using the proposed strategy is proved by simulation results, while the impact of vehicle user preferences and grid constraints is analyzed in detail. © 2015 Automation of Electric Power Press.
引用
收藏
页码：68 / 75and82
页数：7514
相关论文
共 20 条
  • [1] Wang X., Shao C., Wang X., Et al., Survey of electric vehicle charging load and dispatch control strategies, Proceedings of the CSEE, 33, 1, pp. 1-10, (2013)
  • [2] Taylor J., Maitra A., Alexander M., Et al., Evaluation of the impact of plug-in electric vehicle loading on distribution system operations, 2009 IEEE Power & Energy Society General Meeting, (2009)
  • [3] Mets K., D'Hulst R., Develder C., Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid, Journal of Communications and Networks, 14, 6, pp. 672-681, (2012)
  • [4] Clement K., Haesen E., Driesen J., Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids, 2009 Power Systems Conference and Exposition, (2009)
  • [5] Li Z., Guo Q., Sun H., Et al., Real-time charging optimization method considering vehicle charging prediction, Automation of Electric Power Systems, 38, 9, pp. 61-68, (2014)
  • [6] Yang J., Zhao J., Wen F., Et al., Development of bidding strategies for virtual power plants considering uncertain outputs from plug-in electric vehicles, Automation of Electric Power Systems, 38, 13, pp. 92-102, (2014)
  • [7] Cao Y., Tang S., Li C., Et al., An optimized EV charging model considering TOU price and SOC curve, IEEE Trans on Smart Grid, 3, 1, pp. 388-393, (2012)
  • [8] Deilami S., Masoum A.S., Moses P.S., Et al., Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile, IEEE Trans on Smart Grid, 2, 3, pp. 456-467, (2011)
  • [9] Zhao J., Wen F., Yang A., Et al., Impacts of electric vehicles on power systems as well as the associated dispatching and control problem, Automation of Electric Power Systems, 35, 14, pp. 2-9, (2011)
  • [10] Shao S., Pipattanasomporn M., Rahman S., Grid integration of electric vehicles and demand response with customer choice, IEEE Trans on Smart Grid, 3, 1, pp. 543-550, (2012)
12