Simulation Based Evaluation and Optimization for PEV Charging Stations Deployment in Transportation Networks

TL;DR

This paper introduces a simulation and genetic algorithm-based method for evaluating and optimizing the deployment of electric vehicle charging stations, incorporating user detour behaviors for more realistic planning.

Contribution

It presents a novel approach that models user detour behaviors in charging station deployment evaluation and planning, improving practical applicability.

Findings

The method provides more realistic deployment insights.

Simulation results suggest optimized station placement strategies.

User behavior modeling influences deployment effectiveness.

Abstract

Plug-in electric vehicles are receiving global attention. However, large-scale plug-in electric vehicles bring challenge to charging station deployment. This paper provides a deployment evaluation method and a planning method for plug-in electric vehicles charging stations based on simulation and genetic algorithm. In this research, reasonable user behaviors changes i.e., detouring for recharging, is included in the method for more practical application. A detailed logic for describing the plug-in electric vehicles users' detour behaviors is designed in this paper for the charging station deployment evaluation and further influence the later charging station planning strategy formulation. Intuitively, by taking the behaviors change of plug-in electric vehicles users after a charging station deployment is given into account, our evaluation method is the more reasonable. Actually a series of control experiments should be carried out to illustrate the different insights from our method. However, in this paper, due to the limited space, we just provide the result of our method in a relatively simple case with some analyses. Different insights from our method may work as useful suggestion for charging station plan in a city area.