Study on Shapley method-based benefit allocation models for electric vehicle participation in demand response

被引：0
|
作者：
Ma, Jie [1 ]
Wang, JinFeng [2 ]
Zheng, HaiFeng [1 ]
Cao, Min [3 ]
Jia, YueLong [1 ]
Zhang, YuZhuo [1 ]
Ren, ZhengMou [2 ]
Sun, XiaoChen [2 ]
机构：
[1] Sate Grid Energy Res Inst, Beijing 102209, Peoples R China
[2] State Grid Shaanxi Elect Power Econ Technol Res I, Xian 710065, Peoples R China
[3] State Grid Shaanxi Elect Power Co Ltd, Xian 710048, Peoples R China
来源：
2022 9TH INTERNATIONAL FORUM ON ELECTRICAL ENGINEERING AND AUTOMATION, IFEEA | 2022年
关键词：
demand response; Shapley method; benefit allocation;
D O I：
10.1109/IFEEA57288.2022.10038018
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In recent years, China's electricity supply and demand situation has become increasingly tight and demand response needs to be further developed in China in order to secure electricity supply. As the number of electric vehicles in China is gradually increasing, the ability of electric vehicles to participate in demand response is also expanding, however, a benefit allocation model for electric vehicle participation in demand response in the Chinese environment has not yet been developed. This paper first analyzes the current business model of EV participation in demand response in China, and then proposes two benefit allocation model based on the Shapley method, in addition, the effectiveness of two models is investigated.
引用
收藏
页码：244 / 247
页数：4
相关论文
共 50 条
  • [31] Capacity Configuration Method for Second-use Electric Vehicle Batteries of Active Distribution Network Based on Demand Side Response
    Wu M.
    Sun L.
    Kou L.
    Qu X.
    Gaodianya Jishu/High Voltage Engineering, 2020, 46 (01): : 71 - 79
  • [32] Optimal Control Model of Electric Vehicle Demand Response Based on Real - time Electricity Price
    Liang Danxi
    Bo, Zhao
    Yan, Qi
    Xu Yu-jie
    PROCEEDINGS OF 2017 IEEE 2ND INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC), 2017, : 1815 - 1818
  • [33] An Evaluation of Electric Vehicle Penetration under Demand Response in a Multi-Agent Based Simulation
    Wang, Zhanle
    Paranjape, Raman
    2014 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC), 2014, : 220 - 225
  • [34] Stackelberg game-based demand response in multiple utility environments for electric vehicle charging
    Shinde, Priyanka
    Swarup, Kesasanakurty Shanti
    IET ELECTRICAL SYSTEMS IN TRANSPORTATION, 2018, 8 (03) : 167 - 174
  • [35] A novel cost-benefit analytical method of demand response based on input-output method
    Liu, Yao
    Guo, XuXin
    Gao, CiWei
    Li, JunFeng
    Li, JingBo
    2017 IEEE 7TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER), 2017, : 938 - 942
  • [36] Research on benefit allocation based on multi-weight H-Shapley value: A case study of express logistics sharing
    Wang, Chenglin
    Chen, Jintao
    Yu, Xiaohui
    PLOS ONE, 2024, 19 (07):
  • [37] Study of the Aumann-Shapley value based allocation method for congestion cost in bilateral transaction environment
    Yang, Hong-Ming
    Duan, Xian-Zhong
    2002, Chinese Society for Electrical Engineering (22):
  • [38] Payment and Incentive Allocation Method in Demand Response Programs Based on Causer Pays Principle
    Zhang, Yi
    Fan, Shuai
    Meng, Yan
    He, Guangyu
    2023 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA, I&CPS ASIA, 2023, : 2212 - 2220
  • [39] Data-driven method for electric vehicle charging demand analysis: Case study in Virginia
    Liu, Zhaocai
    Borlaug, Brennan
    Meintz, Andrew
    Neuman, Christopher
    Wood, Eric
    Bennett, Jesse
    TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2023, 125
  • [40] Electric Vehicle Charging Facility Planning Based on Flow Demand-A Case Study
    Wang, Cheng
    Gao, Zhou
    Yang, Peng
    Wang, Zhenpo
    Li, Zhiheng
    SUSTAINABILITY, 2021, 13 (09)
12345