A Price-Based Iterative Double Auction for Charger Sharing Markets

TL;DR

This paper introduces a novel price-based iterative double auction mechanism for charger sharing markets that efficiently allocates charging resources among EV drivers and charger owners, promoting market growth and maximizing social welfare.

Contribution

It proposes a new auction mechanism that ensures truthfulness, budget balance, and high efficiency in large-scale EV charger sharing markets.

Findings

Achieves 94% efficiency compared to optimal solutions.

Ensures truthfulness and individual rationality.

Scales effectively to larger problem instances.

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.