  Electric Vehicles' (EVs) growing number has various consequences, from
reducing greenhouse gas emissions and local pollution to altering traffic
congestion and electricity consumption. More specifically, decisions of
operators from both the transportation and the electrical systems are coupled
due to EVs' decisions. Thus, decision-making requires a model of several
interdependent operators and of EVs' both driving and charging behaviors. Such
a model is suggested for the electrical system in the context of commuting,
which has a typical trilevel structure. At the lower level of the model, a
congestion game between different types of vehicles gives which driving paths
and charging stations (or hubs) commuters choose, depending on travel duration
and consumption costs. At the middle level, a Charging Service Operator sets
the charging prices at the hubs to maximize the difference between EV charging
revenues and electricity supplying costs, which are decided by the Electrical
Network Operator at the upper level of the model, whose goal is to reduce grid
costs. This trilevel optimization problem is solved using an optimistic
iterative bilevel algorithm and simulated annealing. The sensitivity of this
trilevel model to exogenous parameters such as the EV penetration and an
incentive from a transportation operator is illustrated on realistic urban
networks.
