Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks

TL;DR

This paper analyzes how electric vehicle owners' individual charging and travel decisions impact coupled power and transportation networks, proposing a collaborative management scheme to optimize system-wide performance.

Contribution

It introduces a novel extended graph model for EV decision-making and proposes a privacy-preserving collaboration scheme for system operators.

Findings

Decentralized EV decisions can cause adverse network effects.

Collaborative management improves system efficiency.

Interdependencies between networks are critical for operational planning.

Abstract

We study the system-level effects of the introduction of large populations of Electric Vehicles on the power and transportation networks. We assume that each EV owner solves a decision problem to pick a cost-minimizing charge and travel plan. This individual decision takes into account traffic congestion in the transportation network, affecting travel times, as well as as congestion in the power grid, resulting in spatial variations in electricity prices for battery charging. We show that this decision problem is equivalent to finding the shortest path on an "extended" transportation graph, with virtual arcs that represent charging options. Using this extended graph, we study the collective effects of a large number of EV owners individually solving this path planning problem. We propose a scheme in which independent power and transportation system operators can collaborate to manage each network towards a socially optimum operating point while keeping the operational data of each system private. We further study the optimal reserve capacity requirements for pricing in the absence of such collaboration. We showcase numerically that a lack of attention to interdependencies between the two infrastructures can have adverse operational effects.