Intelligent Charging Management of Electric Vehicles Considering Dynamic User Behavior and Renewable Energy: A Stochastic Game Approach

TL;DR

This paper presents a stochastic game approach to manage electric vehicle charging by considering dynamic user preferences and renewable energy variability, aiming to optimize costs and service quality.

Contribution

It introduces a novel stochastic game model capturing customer behavior and renewable energy uncertainty, along with an online algorithm to achieve Nash equilibria in EV charging management.

Findings

20% reduction in electricity costs compared to benchmark methods

Higher quality of service in charging and waiting times

Effective decision-making guidelines for charging providers

Abstract

Uncoordinated charging of a rapidly growing number of electric vehicles (EVs) and the uncertainty associated with renewable energy resources may constitute a critical issue for the electric mobility (E-Mobility) in the transportation system especially during peak hours. To overcome this dire scenario, we introduce a stochastic game to study the complex interactions between the power grid and charging stations. In this context, existing studies have not taken into account the dynamics of customers' preference on charging parameters. In reality, however, the choice of the charging parameters may vary over time, as the customers may change their charging preferences. We model this behavior of customers with another stochastic game. Moreover, we define a quality of service (QoS) index to reflect how the charging process influences customers' choices on charging parameters. We also develop an online algorithm to reach the Nash equilibria for both stochastic games. Then, we utilize real data from the California Independent System Operator (CAISO) to evaluate the performance of our proposed algorithm. The results reveal that the electricity cost with the proposed method can result in a saving of about 20% compared to the benchmark method, while also yielding a higher QoS in terms of charging and waiting time. Our results can be employed as guidelines for charging service providers to make efficient decisions under uncertainty relative to power generation of renewable energy.