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.
翻译:电动车辆(EV)数量不断增加,产生了各种后果,从减少温室气体排放和当地污染到改变交通堵塞和电力消耗,更具体地说,运输和电力系统操作者的决定都是由EV决定的结果。因此,决策需要几个相互依存的操作者的模式以及EV驾驶和收费行为的模式。这种模式是在通勤方面为电力系统提出的,因为通勤具有典型的三层结构。在模式的较低层次,不同类型车辆之间的拥堵游戏提供了驾驶路线和收费站(或中心)的交通费用,取决于旅行时间和消费费用。在中层,收费服务操作者在中心设定收费价格,以尽量扩大EVC收费收入和电力供应费用之间的差别,这是由该模式高层的电气网络操作者决定的,目的是降低电网的成本。这种三层优化问题通过一种乐观的迭代双层算法和模拟净化来解决。在现实城市网络上展示了这种三层模型对诸如EV渗透和运输运营者激励等外部参数的敏感性。