The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid. The coupling between the electric vehicle travel behaviors and charging events is therefore complex. This paper focuses on the demonstration of a scalable computational framework for the electric vehicle charging infrastructure planning over the tightly integrated transportation and electric grid networks. On the transportation side, a charging profile generation strategy is proposed leveraging the EV energy consumption model, trip routing, and charger selection methods. On the grid side, a genetic algorithm is utilized within the optimal power flow program to solve the optimal charger placement problem with integer variables by adaptively evaluating candidate solutions in the current iteration and generating new solutions for the next iterations.
翻译：由于运输系统和电网的网络规模日益扩大,因此,电动车辆旅行行为和收费事件之间的结合是复杂的,因此,在大型地理空间区域,最佳收费基础设施规划问题具有挑战性。本文件的重点是演示电动车辆可缩放计算框架,对紧凑整合的运输和电网网络进行基础设施规划。在运输方面,提议采用电动剖面生成战略,利用EV能源消耗模式、行程路线和充电器选择方法。在电网方面,在最佳电流程序内使用遗传算法,通过在目前的迭代中适应性地评估候选人解决方案,为下一个迭代产生新的解决方案,解决电动电动车辆与整数变量的最佳充电器配置问题。
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