TY - JOUR
T1 - A bilevel conic optimization model for routing and charging of EV fleets serving long distance delivery networks
AU - Subramanian, Vignesh
AU - Feijoo, Felipe
AU - Sankaranarayanan, Sriram
AU - Melendez, Kevin
AU - Das, Tapas K.
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/7/15
Y1 - 2022/7/15
N2 - 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.
AB - 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.
KW - ACOPF
KW - Conic bilevel optimization
KW - Demand response
KW - Electric vehicles
KW - Transportation network
UR - http://www.scopus.com/inward/record.url?scp=85127669210&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2022.123808
DO - 10.1016/j.energy.2022.123808
M3 - Article
AN - SCOPUS:85127669210
VL - 251
JO - Energy
JF - Energy
SN - 0360-5442
M1 - 123808
ER -
