Impact of Electric Vehicle Routing with Stochastic Demand on Grid Operation

Oluwaseun Oladimeji, Alvaro Gonzalez-Castellanos, David Pozo, Yury Dvorkin, Samrat Acharya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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 a 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.

Original languageEnglish
Title of host publication2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665435970
DOIs
Publication statusPublished - 28 Jun 2021
Event2021 IEEE Madrid PowerTech, PowerTech 2021 - Madrid, Spain
Duration: 28 Jun 20212 Jul 2021

Publication series

Name2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings

Conference

Conference2021 IEEE Madrid PowerTech, PowerTech 2021
Country/TerritorySpain
CityMadrid
Period28/06/212/07/21

Keywords

  • Chance-constrained optimization
  • Electric vehicle
  • Vehicle routing problem

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