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Bargaining game based on psychology cost of electric vehicles and risk assessment of aggregator
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  • Yonggang LI,
  • Hui Lin,
  • Yichen Zhou,
  • Haoyang Zheng,
  • Xinghao Zhen,
  • Zhaoyuan Bian,
  • Ziyi Yue,
  • Yang Yang
Yonggang LI
North China Electric Power University - Baoding Campus

Corresponding Author:lygzxm0@163.com

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Hui Lin
North China Electric Power University
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Yichen Zhou
North China Electric Power University
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Haoyang Zheng
North China Electric Power University
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Xinghao Zhen
North China Electric Power University - Baoding Campus
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Zhaoyuan Bian
North China Electric Power University - Baoding Campus
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Ziyi Yue
North China Electric Power University - Baoding Campus
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Yang Yang
North China Electric Power University - Baoding Campus
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Abstract

As electric vehicles(EVs) begin to participate in the peak regulating auxiliary service market, it has become a major problem that how can price aggregators maximize the peak shaving capacity provided by EVs and maximize their own interests. This paper proposes a bargaining game pricing method based on the psychology cost of EVs and the risk assessment of aggregators. First of all, according to the impact of users’ participation in peak shaving on the battery life of EVs, the impact of participation in peak shaving on users’ original travel plans and time, and the impact of aggregator pricing on users’ psychology, the comprehensive psychology cost of EV users is obtained. Then, based on the user’s psychology cost and the law of gravitation, the evaluation scheme for the peak shaving capacity of EVs is obtained. On the basis of conditional value at risk(CVaR), the mixed CVaR is obtained by considering the behavior of users who may chase risks. Based on the mixed CVaR, the risk assessment of aggregators’ participation in the peak regulating auxiliary service market is carried out. According to the above information, the aggregators and the EV teams are engaged in a bargaining game based on the peak shaving pricing problem, which is divided into complete information game and incomplete information game. Finally, the feasibility of the proposed method is verified by an example analysis.
11 Nov 2022Submitted to IET Generation, Transmission & Distribution
11 Nov 2022Submission Checks Completed
11 Nov 2022Assigned to Editor
20 Nov 2022Reviewer(s) Assigned
06 Dec 2022Review(s) Completed, Editorial Evaluation Pending
28 Dec 2022Editorial Decision: Revise Major
02 Jan 20231st Revision Received
03 Jan 2023Submission Checks Completed
03 Jan 2023Assigned to Editor
08 Jan 2023Reviewer(s) Assigned
12 Jan 2023Review(s) Completed, Editorial Evaluation Pending
19 Jan 2023Editorial Decision: Revise Major
24 Jan 20232nd Revision Received
30 Jan 2023Submission Checks Completed
30 Jan 2023Assigned to Editor
07 Feb 2023Reviewer(s) Assigned
09 Feb 2023Review(s) Completed, Editorial Evaluation Pending
13 Feb 2023Editorial Decision: Revise Minor
15 Feb 20233rd Revision Received
15 Feb 2023Submission Checks Completed
15 Feb 2023Assigned to Editor
03 Mar 2023Reviewer(s) Assigned
08 Mar 2023Review(s) Completed, Editorial Evaluation Pending
08 Mar 2023Editorial Decision: Accept