Battery-Wear-Model-Based Energy Trading in Electric Vehicles: A Naive Auction Model and a Market Analysis
- Authors
- Kim, Jangkyum; Lee, Joohyung; Park, Sangdon; Choi, 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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