Ordered charging of electric vehicles considering grid-station-user multi-party demands and decision-making behavior characteristics

被引:0
|
作者
Lin X. [1 ]
Qian B. [1 ]
Xiao Y. [1 ]
Luo X. [2 ]
Yang J. [1 ]
机构
[1] China Southern Power Grid Research Institute, Guangzhou
[2] Shenzhen Power Supply Bureau Co., Ltd., Shenzhen
关键词
Decision-making behavior; Electric vehicles; Operation risk; Ordered charging; Price regulation cost; Subsidy mechanism;
D O I
10.16081/j.epae.202102002
中图分类号
学科分类号
摘要
In order to solve the security problems caused by the integration of large-scale charging load into the distribution network when the construction of charging facilities and distribution network is not fully matched to the popularization speed of EVs(Electric Vehicles), an ordered charging model with the participation of power grid company, charging station operator and EV users(all three are called grid-station-user for short) is established. The demands and decision-making behavior characteristics of all parties are analy-zed. The concept of price regulation cost is proposed and a subsidy mechanism considering the price regulation cost is established. Based on the decision-making behavior characteristics of all parties, the decision-making behavior model of grid-station-user is built. The cost and benefit of each party involved in ordered charging are modeled, and the net income model of power grid company is established by integrating the security of distribution network and the economic cost of power grid company, based on which, the comprehensive objective function of ordered charging is set up. Through simulation examples, the demand change of each party under different charging price strategies are analyzed to verify the rationality of the proposed subsidy mechanism and the effectiveness of the established ordered charging model. © 2021, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码:136 / 143
页数:7
相关论文
共 14 条
  • [1] SADEGHIAN O, NAZARI-HERIS M, ABAPOUR M, Et al., Imp-roving reliability of distribution networks using plug-in elec-tric vehicles and demand response, Journal of Modern Po-wer Systems and Clean Energy, 7, 5, pp. 1189-1199, (2019)
  • [2] YANG Jingxu, YI Yingqi, ZHANG Yongjun, Et al., Operation risk analysis of electric vehicle integrated to distribution network based on weighted distribution entropy, Automation of Electric Power Systems, 44, 5, pp. 171-179, (2020)
  • [3] CHANG Fangyu, HUANG Mei, ZHANG Weige, Research on coordinated charging of electric vehicles based on TOU char-ging price, Power System Technology, 40, 9, pp. 2609-2615, (2016)
  • [4] WANG Yi, MA Xiu, WAN Yi, Et al., Sequential charge-discharge guidance strategy for electric vehicles based on time-sharing charging-discharging margin, Power System Technology, 43, 12, pp. 4353-4361, (2019)
  • [5] WANG Bin, GUO Wenxin, LI Shiming, Et al., Real-time char-ging optimization for large-scale electric vehicles based on short term forecast information and long term value function approximation, Power System Protection and Control, 47, 24, pp. 47-56, (2019)
  • [6] YANG Jingxu, ZHOU Lai, ZHANG Yongjun, Et al., Ordered charging of EVs considering time-varying electricity price and transition probability under "dedicated transformer sha-ring" mode, Electric Power Automation Equipment, 40, 10, pp. 173-180, (2020)
  • [7] CUI Jindong, LUO Wenda, ZHOU Niancheng, Research on pri-cing model and strategy of electric vehicle charging and discharging based on multi view, Proceedings of the CSEE, 38, 15, pp. 4438-4450, (2018)
  • [8] WANG Yufei, CAI Chuangao, XUE Hua, Optimized charging strategy of community electric vehicle charging station based on improved NSGA-Ⅱ, Electric Power Automation Equipment, 37, 12, pp. 109-115, (2017)
  • [9] CHEN Lixing, HUANG Xueliang, Ordered charging strategy of electric vehicles at charging station on highway, Electric Power Automation Equipment, 39, 1, pp. 112-117, (2019)
  • [10] WANG Haolin, ZHANG Yongjun, MAO Haipeng, Charging load forecasting method based on instantaneous charging probability for electric vehicles, Electric Power Automation Equipment, 39, 3, pp. 207-213, (2019)