Mobility-Aware Electric Vehicle Fast Charging Load Models with Geographical Price Variations
For charging network operators, this work offers a framework to influence EV charging decisions to optimize station utilization and manage grid load, though the contribution is incremental as it extends existing traffic assignment models to EV charging with geographical price variations.
This paper develops a traffic and charge assignment problem (TCAP) model to study how cost-minimizing EV owners choose fast charging stations, capturing equilibrium wait times and aggregate charging load while accounting for mobility patterns, energy demands, and geographically varying electricity prices. The authors provide a convex optimization formulation to find the unique equilibrium and derive socially optimal plug-in fees and electricity prices.
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.