Impact of Electric Vehicle Routing with Stochastic Demand on Grid Operation

TL;DR

This paper develops a stochastic EV routing model that minimizes travel and energy costs, incorporating real-life demand uncertainties, and evaluates its impact on urban grid operation.

Contribution

It introduces a chance-constrained optimization model for EV routing with stochastic demand and assesses its effects on city grid infrastructure.

Findings

Model converges quickly with verifiable solutions.

Stochastic demand significantly affects EV routing efficiency.

Charging impacts on urban grid are validated in Manhattan.

Abstract

Given the rise of electric vehicle (EV) adoption, supported by government policies and dropping technology prices, new challenges arise in the modeling and operation of electric transportation. In this paper, we present a model for solving the EV routing problem while accounting for real-life stochastic demand behavior. We present a mathematical formulation that minimizes travel time and energy costs of an EV fleet. The EV is represented by a battery energy consumption model. To adapt our formulation to real-life scenarios, customer pick-ups and drop-offs were modeled as stochastic parameters. A chance-constrained optimization model is proposed for addressing pick-ups and drop-offs uncertainties. Computational validation of the model is provided based on representative transportation scenarios. Results obtained showed a quick convergence of our model with verifiable solutions. Finally, the impact of electric vehicles charging is validated in Downtown Manhattan, New York by assessing the effect on the distribution grid.