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

页码：244 / 247

页数：4

共 50 条

[41] GGlueVaR-based participation of electric vehicles in automatic demand response for two-stage scheduling
Gao, Jianwei
Yang, Yu
Ma, Zeyang
Gao, Fangjie
INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2021, 45 (01) : 1128 - 1141
[42] Demand response of photovoltaic electric vehicle charging stations based on weather-impact risk assessment
Yan, Qin
Tu, Xiaofan
He Jishu/Nuclear Techniques, 2023, 46 (04): : 37 - 45
[43] Stackelberg based optimal planning of DGs and electric vehicle parking lot by implementing demand response program
Salyani, Pouya
Abapour, Mehdi
Zare, Kazem
SUSTAINABLE CITIES AND SOCIETY, 2019, 51
[44] Research on Demand Response of Electric Vehicle Agents Based on Multi-Layer Machine Learning Algorithm
Lin, Jianping
Dong, Ping
Liu, Mingbo
Huang, Xuewei
Deng, Wenli
IEEE ACCESS, 2020, 8 : 224224 - 224234
[45] Economic Benefit Analysis of Charging Models Based on Differential Electric Vehicle Charging Infrastructure Subsidy Policy in China
Yang, Meng
Zhang, Lihui
Dong, Wenjia
SUSTAINABLE CITIES AND SOCIETY, 2020, 59
[46] A Study on Private Vehicle Demand Forecasting based on Box-Jenkins Method
Abu Noratikah
Ismail, Zuhaimy
PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON AUTOMOTIVE INNOVATION GREEN ENERGY VEHICLE (AIGEV 2018), 2019, 2059
[47] Demand Response Strategies for Electric Vehicle Charging and Discharging Behavior Based on Road-Electric Grid Interaction and User Psychology
Gao, Yang
Zhang, Xiaohong
Yan, Qingyuan
Li, Yanxue
SUSTAINABILITY, 2025, 17 (06)
[48] Multiple Demand Response Models for Integrated Energy Systems Based on Electric-Heat Loads
Li, Qing
Wu, Yanjuan
Qiu, Jianniang
2024 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, ICMA 2024, 2024, : 26 - 31
[49] Debris flows dynamic risk assessment and interpretable Shapley method-based driving mechanisms exploring - A case study of the upper reach of the Min River
He, Yufeng
Ding, Mingtao
Duan, Yu
Zheng, Hao
He, Wen
Liu, Jun
ECOLOGICAL INDICATORS, 2025, 173
[50] Study on Frequency-Response Optimization of Electric Vehicle Participation in Energy Storage Considering the Strong Uncertainty Model
Cai, Li
Yang, Chenxi
Li, Junting
Liu, Yuhang
Yan, Juan
Zou, Xiaojiang
WORLD ELECTRIC VEHICLE JOURNAL, 2025, 16 (01):

← 1 2 3 4 5 →