TY - GEN
T1 - Impact of Electric Vehicle Routing with Stochastic Demand on Grid Operation
AU - Oladimeji, Oluwaseun
AU - Gonzalez-Castellanos, Alvaro
AU - Pozo, David
AU - Dvorkin, Yury
AU - Acharya, Samrat
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/28
Y1 - 2021/6/28
N2 - 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.
AB - 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.
KW - Chance-constrained optimization
KW - Electric vehicle
KW - Vehicle routing problem
UR - http://www.scopus.com/inward/record.url?scp=85112361361&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112361361&partnerID=8YFLogxK
U2 - 10.1109/PowerTech46648.2021.9495092
DO - 10.1109/PowerTech46648.2021.9495092
M3 - Conference contribution
AN - SCOPUS:85112361361
T3 - 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
BT - 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Madrid PowerTech, PowerTech 2021
Y2 - 28 June 2021 through 2 July 2021
ER -