A bilevel conic optimization model for routing and charging of EV fleets serving long distance delivery networks

Recent unveiling of electric semi-trucks by a number of electric vehicle manufacturers indicates that part of the existing long-distance transportation fleets may soon be electrified. Operators of electric fleets will have to select travel routes considering charging station availability and cost of charging in addition to usual factors such as congestion and travel time. This requires combined modeling of transportation and electric power networks. We present such a model that considers interactions between the two networks to develop optimal routing strategies. The problem is formulated as a multi-objective bilevel conic optimization model. The upper level obtains the routing decision by minimizing a function of charging cost and travel time. The routing decision is used in the lower level that solves the AC optimal power flow model, using second order cone constraints, to determine nodal electricity prices. The model is demonstrated using a numerical problem with 24-Node transport network supported by a modified 5 Bus PJM network. The results show that our model yields optimal routes and charging strategies to meet the objectives of fleet operators. Results also indicate that the optimal routing and charging strategies of the electrified transportation fleet can support power networks to reduce nodal prices via demand response. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

The recent unveiling of electric semi-trucks by various electric vehicle manufacturers suggests that a portion of the current long-distance transportation fleets may be electrified in the near future. To select the optimal travel routes, operators of electric fleets need to consider charging station availability, charging costs, as well as common factors such as traffic congestion and travel time. This study presents a model that combines transportation and electric power networks to develop optimal routing strategies.