  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.
