Fast Charging Guidance Strategy for Multiple Demands of Electric Vehicle, Fast Charging Station and Distribution Network

被引:0
|
作者
Shao Y. [1 ]
Mu Y. [2 ]
Lin J. [3 ]
Wang K. [1 ]
Gong Y. [1 ]
机构
[1] State Grid Jibei Electric Power Research Institute, North China Electric Power Research Institute Co., Ltd., Beijing
[2] Key Laboratory of the Ministry of Education on Smart Power Grids, Tianjin University, Tianjin
[3] China Electric Power Research Institute, Beijing
基金
国家重点研发计划;
关键词
Charging demand; Charging guidance; Distribution network; Electric vehicles (EVs); Electricity price stimulation;
D O I
10.7500/AEPS20181127010
中图分类号
学科分类号
摘要
A fast charging guidance strategy for multiple demands of the electric vehicles (EVs), fast charging stations (FCSs) and the distribution network is proposed. Firstly, a charging guidance architecture in electric vehicle, fast charging startion and distribution network cooperation mode is introduced. Then, based on the distribution result of fast charging demand by origin destination (OD) analysis, a double-layer queue model is proposed to simulate the dynamic queue of FCSs. Secondly, according to the dynamic queue and charging load receptivity under the constrain of node voltage in distribution network, a charging price model is built by adapting the demand function of dynamic electricity price to guide the user to choose the FCSs with the goal of minimizing the charging cost. Finally, taking main urban areas of a city as an example, simulation is conducted under different participations of 1 000 EVs with fast charging demand. The result shows that proposed charging guidance strategy can not only save the charging cost of users, but also improve the operation efficiency of FCSs and ensure the safety operation of distribution network. © 2019 Automation of Electric Power Systems Press.
引用
收藏
页码:60 / 66and101
相关论文
共 26 条
  • [1] The 13th five-year plan for science and technology innovation in transportation
  • [2] Dong X., Mu Y., Yu L., Et al., Freeway FCS planning and correction considering power-flow constraints of distribution network, Electric Power Automation Equipment, 37, 6, pp. 124-131, (2017)
  • [3] Gao C., Zhang L., A survey of influence of electric vehicle charging on power grid, Power System Technology, 35, 2, pp. 127-131, (2011)
  • [4] Cheng Y., Zhang Z., Yu J., Et al., Research on operation and management multi-node of megacity energy internet, Global Energy Interconnection, 1, 2, pp. 130-136, (2018)
  • [5] Zhang H., Hu Z., Song Y., Et al., A prediction method for electric vehicle charging load considering spatial-temporal distribution, Automation of Electric Power Systems, 38, 1, pp. 13-20, (2014)
  • [6] Huang X., Chen J., Chen Y., Et al., Load forecasting method for electric vehicle charging station based on big data, Automation of Electric Power Systems, 40, 12, pp. 68-74, (2016)
  • [7] Zhang Q., Wang Z., Tan W., Et al., Spatial-temporal distribution prediction of charging load for electric vehicle based on MDP random path simulation, Automation of Electric Power Systems, 42, 20, pp. 59-73, (2018)
  • [8] Shao Y., Mu Y., Yu X., Et al., A spatial temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model, Proceedings of the CSEE, 37, 18, pp. 5207-5218, (2017)
  • [9] Wang S., Yang S., A coordinated charging control strategy for electric vehicles charging load in residential area, Automation of Electric Power Systems, 40, 4, pp. 71-77, (2016)
  • [10] Shi R., Liang Z., Ma Y., TOPSIS method based orderly charging strategy for electric vehicles in residential area, Automation of Electric Power Systems, 42, 21, pp. 104-110, (2018)