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      A Price-Based Iterative Double Auction for Charger Sharing Markets

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          Abstract

          The unprecedented growth of demand for charging electric vehicles (EVs) calls for novel expansion solutions to today's charging networks. Riding on the wave of the proliferation of sharing economy, Airbnb-like charger sharing markets opens the opportunity to expand the existing charging networks without requiring costly and time-consuming infrastructure investments, yet the successful design of such markets relies on innovations at the interface between game theory, mechanism design, and large scale optimization. In this paper, we propose a price-based iterative double auction for charger sharing markets where charger owners rent out their under-utilized chargers to the charge-needing EV drivers. Charger owners and EV drivers form a two-sided market which is cleared by a price-based double auction. Chargers' locations, availability, and time unit costs as well as the EV drivers' time, distance constraints, and preferences are considered in the allocation and scheduling process. The goal is to compute social welfare maximizing allocations which benefits both charger owners and EV drivers and, in turn, ensure the continuous growth of the market. We prove that the proposed double auction is budget balanced, individually rational, and that it is a weakly dominant strategy for EV drivers and charger owners to truthfully report their charging time constraints. In addition, results from our computation study show that the double auction achieves on average 94% efficiency compared with the optimal solutions and scales well to larger problem instances.

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          Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains

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            Algorithm for optimal winner determination in combinatorial auctions

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              A dominant strategy double auction

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                Author and article information

                Journal
                30 September 2019
                Article
                1910.00053
                f7bc635d-6749-449a-9fd0-de8e8da6832e

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.GT

                Theoretical computer science
                Theoretical computer science

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