Pricing for private charging pile sharing considering EV consumers based on non-cooperative game model

被引:52
作者
Zhao, Zhenli [1 ,2 ]
Zhang, Lihui [1 ,2 ]
Yang, Meng [1 ,2 ]
Chai, Jianxue [1 ,2 ]
Li, Songrui [1 ,2 ]
机构
[1] Sch Econ & Management, Beijing, Peoples R China
[2] North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R China
关键词
Private charging pile sharing service; Consumers' charging behavior; Sharing rate; Non-cooperative game model; ELECTRIC VEHICLE DRIVERS; PLUG-IN HYBRID; PARTNERSHIP PROJECTS; CHOICE BEHAVIOR; CHINA; INFRASTRUCTURE; MANAGEMENT; ENERGY; STRATEGY; PARKING;
D O I
10.1016/j.jclepro.2020.120039
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The supply of public charging infrastructure is insufficient to meet the charging demand of a large number of electric vehicles (EVs). Private charging pile sharing is an emerging solution to alleviate this imbalance. However, a reasonable price for charging pile sharing has not yet been determined. This study employs a non-cooperative game model to determine a charging pile sharing price considering EV consumers' charging behaviors. First, a multi-logit model is constructed to measure the probability of EV consumers' charging behavior choices. Then, a two-matrix game model is established between the private charging pile sharing and public charging mode. Using Beijing as a sample, the sharing rate and price strategies of private and public charging piles are calculated based on the proposed game model. The results show that the optimal sharing rate is 20.01% private charging pile sharing with 1.14 yuan/kWh and 79.99% public charging with 1.7946 yuan/kWh. Sensitivity analysis shows that economics is the most sensitive factor affecting the charging price of private charging pile sharing. Finally, policy recommendations are outlined to improve the private charging pile sharing rate and service efficiency, to broaden the charging options for EV consumers, reduce the construction of public charging piles, and save the government subsidy. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:13
相关论文
共 57 条
[1]  
Adnan N., 2017, Green Marketing and Environmental Responsibility in Modern Corporations, P198, DOI [10.4018/978-1-5225-2331-4.ch011, DOI 10.4018/978-1-5225-2331-4.CH011]
[2]  
Adnan N, 2019, STUD SYST DECIS CONT, V186, P121, DOI 10.1007/978-3-319-98923-5_7
[3]  
Adnan N, 2017, ADV FINANC ACCOUNT E, P183, DOI 10.4018/978-1-5225-1826-6.ch010
[4]   Climate Change, Population Ageing and Public Spending: Evidence on Individual Preferences [J].
Andor, Mark A. ;
Schmidt, Christoph M. ;
Sommer, Stephan .
ECOLOGICAL ECONOMICS, 2018, 151 :173-183
[5]  
[Anonymous], 2019, Statistical Yearbook
[6]   Energy trading with dynamic pricing for electric vehicles in a smart city environment [J].
Aujla, Gagangeet Singh ;
Kumar, Neeraj ;
Singh, Mukesh ;
Zomaya, Albert Y. .
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2019, 127 :169-183
[7]   Generalized Nash equilibrium problem based electric vehicle charging management in distribution networks [J].
Cao, Chong ;
Chen, Bo .
INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2018, 42 (15) :4584-4596
[8]   Travel mode choice: a data fusion model using machine learning methods and evidence from travel diary survey data [J].
Chang, Ximing ;
Wu, Jianjun ;
Liu, Hao ;
Yan, Xiaoyong ;
Sun, Huijun ;
Qu, Yunchao .
TRANSPORTMETRICA A-TRANSPORT SCIENCE, 2019, 15 (02) :1587-1612
[9]  
China-nengyuan, 2017, PRIV CHARG SHAR SERV
[10]   A credit risk evaluation based on intuitionistic fuzzy set theory for the sustainable development of electricity retailing companies in China [J].
De, Gejirifu ;
Tan, Zhongfu ;
Li, Menglu ;
Huang, Lilin ;
Wang, Qiang ;
Li, Huanhuan .
ENERGY SCIENCE & ENGINEERING, 2019, 7 (06) :2825-2841