Mobility-Aware Electric Vehicle Fast Charging Load Models with Geographical Price Variations

TL;DR

This paper models the impact of geographical price variations on electric vehicle charging behaviors, capturing mobility-aware congestion and load patterns, and proposes optimal pricing strategies to improve station utilization and social welfare.

Contribution

It introduces a traffic and charge assignment problem that accounts for mobility, heterogeneity, and pricing, providing analytical and optimization tools for EV charging network management.

Findings

Characterizes equilibrium EV charging behavior based on prices and mobility.

Derives a convex optimization framework for equilibrium analysis.

Suggests pricing strategies for socially optimal charging behavior.

Abstract

We study the traffic patterns as well as the charging patterns of a population of cost-minimizing EV owners traveling and charging within a transportation network equipped with fast charging stations (FCSs). Specifically, we study how the charging network operator (CNO) can influence where EV users charge in order to optimize the utilization of fast charging stations. These charging decisions of private EV owners affect aggregate congestion at stations (i.e., waiting time) as well as the aggregate EV charging load across the network. In this work, we capture the resulting equilibrium wait times and electricity load through a so-called \textit{traffic and charge assignment problem} (TCAP) in a fast charging station network. Our formulation allows us to: 1) Study the expected station wait times as well as the probability distribution of aggregate charging load of EVs in a FCS network in a mobility-aware fashion (an aspect unique to our work), while accounting for heterogeneities in users' travel patterns, energy demands, and geographically variant electricity prices. 2) Analytically characterize the special threshold-based structure that determines how EV owners choose where to charge their vehicle at equilibrium, in response to the FCS's charging price structure, users' energy demands, and users' mobility patterns. 3) Provide a convex optimization problem formulation to identify the network's unique equilibrium. Furthermore, we illustrate how to induce a socially optimal charging behavior by deriving the socially optimal plug-in fees and electricity prices at the charging stations.