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Cited 29 time in webofscience Cited 30 time in scopus
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Battery-Wear-Model-Based Energy Trading in Electric Vehicles: A Naive Auction Model and a Market Analysis

Authors
Kim, JangkyumLee, JoohyungPark, SangdonChoi, Jun Kyun
Issue Date
Jul-2019
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Auction theory; battery degradation; electric vehicle (EV); energy trading; naive auction
Citation
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v.15, no.7, pp.4140 - 4151
Journal Title
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume
15
Number
7
Start Page
4140
End Page
4151
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/1276
DOI
10.1109/TII.2018.2883655
ISSN
1551-3203
Abstract
This paper proposes auction-based energy trading among electric vehicles (EVs) with consideration of practical battery status. At an arbitrary time, each EV can be a seller or a buyer according to their residual battery level and required energy for reaching the predefined destination. Under energy trading among EVs, there is an energy pool managed by an auctioneer in the market that gathers surplus energy from sellers and supplies it to buyers using an auction mechanism. Each buyer individually decides the initial bidding price and the amount of energy to buy from sellers according to an expected cost savings compared with buying from macrogrids as well as its battery wearout cost and charging/discharging efficiency. Similarly, each seller also individually decides its initial selling price and the amount of energy for sale subject to a tradeoff between the received revenue and discharging cost depending on its battery status. Notably, we provide a battery status model of EVs for designing well-defined utilities of both sellers and buyers. We design a naive auction process such that, in the auction process, the auctioneer controls the bidding increment to determine the best prices on a set of energies offered to multiple buyers through an iterative procedure. Finally, we show that distributing the energy based on a well-defined utility function converges to a unique optimal distribution for maximizing the payoff of all participating EVs.
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