Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework

TL;DR

This paper presents a scalable computational framework for planning electric vehicle charging infrastructure by integrating transportation and electric grid models, employing a genetic algorithm for optimal charger placement.

Contribution

It introduces a novel scalable framework combining transportation and grid models with a genetic algorithm for efficient charger placement planning.

Findings

Effective charger placement solutions demonstrated

Framework scales to large geospatial areas

Integration of transportation and grid models improves planning accuracy

Abstract

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.