Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework
This work provides a scalable computational framework for urban planners and energy utilities to optimize electric vehicle charging infrastructure, addressing the increasing complexity of integrated transportation and electric grid networks.
This paper addresses the challenge of optimal electric vehicle charging infrastructure planning across large geospatial areas, considering the complex interplay between transportation networks and the electric grid. It proposes a scalable computational framework that integrates EV travel behaviors and charging events, utilizing a charging profile generation strategy for transportation and a genetic algorithm within an optimal power flow program for grid-side charger placement.
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.