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 open 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, availabilities, and unit time service costs as well as drivers’ time and location preferences are considered in the allocation and scheduling process. The goal is to compute social welfare maximizing schedules which benefit 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 and individually rational. In addition, results from our computational study show that the proposed auction achieves on average 94% efficiency compared with that of the optimal solutions and is suitable for a larger day-ahead charger sharing market setting in terms of running time.@en

Consider a decentralized electric vehicle (EV) charging scheduling problem where the chargers and vehicles are modeled as utility maximizing agents. To schedule chargers to vehicles in a day-ahead market, we propose a double auction mechanism, where chargers report their time availabilities and charging costs and vehicles report their preferences on different chargers. This auction is conducted in an iterative manner which enables the computation of effective schedules through multilateral negotiation between agents. We evaluate the efficiency performance of the mechanism through a computational study. The result generated from the iterative double auction mechanism is on average 35% better than the first come first serve (FCFS) allocation policy.

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