Dynamic Pricing of Electric Vehicle Charging Station Alliances Under Information Asymmetry

TL;DR

This paper develops a dynamic, privacy-preserving pricing strategy for electric vehicle charging station alliances, accounting for information asymmetry and bounded rationality, validated through a Shanghai case study.

Contribution

It introduces a real-time information interaction framework and models the pricing game among alliances using evolutionary game theory, addressing practical challenges in EV charging markets.

Findings

Framework effectively supports privacy protection and real-time decision-making

Dynamic pricing strategy improves profit optimization for CSAs

Case study confirms the framework's practical applicability

Abstract

Due to the centralization of charging stations (CSs), CSs are organized as charging station alliances (CSAs) in the commercial competition. Under this situation, this paper studies the profit-oriented dynamic pricing strategy of CSAs. As the practicability basis, a privacy-protected bidirectional real-time information interaction framework is designed, under which the status of EVs is utilized as the reference for pricing, and the prices of CSs are the reference for charging decisions. Based on this framework, the decision-making models of EVs and CSs are established, in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated. To solve the pricing decision model, the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs, the equilibrium of which gives the optimal pricing strategy. Finally, the case study results in a real urban area in Shanghai, China verifies the practicability of the framework and the effectiveness of the dynamic pricing strategy.