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Title:

Charging Station Planning under Multiple Objectives with Stochastic Decision Model

Accession Number:

01626927

Record Type:

Component

Abstract:

Optimal deployment for charging stations can significantly alleviate the range anxiety of electric vehicle users, thus promoting the diffusion of electric vehicles. Unlike the existing works, this study formulates the charging station planning problem as a multi objective optimization problem to determine the location, type, and scale of charging stations with six objectives (incomplete trips, waiting time, crowding rate, land cost, construction cost, and number of workers), among which three are in favor of electric vehicle users and the others are mainly considered by the planner. The tour-based representation scheme is used to reflect the charging demands with assistance of a stochastic charging decision model capturing the uncertainty of charging decision. Furthermore, the land cost and the land capacity varying with locations are considered in this study. With the introduction of the crowding rate, the balance between the charging demand and the charging facility supply is addressed in the optimization. The non-dominated sorting genetic algorithm-II is employed to deliver a set of optimal solutions. A series of experiments are performed to evaluate the proposed charging decision model, the variable land cost, the variable land capacity, and the crowding rate on the optimal solutions. The experimental results show that the time waiting for charging can be significantly reduced with the proposed decision model, as compared to the "last-minute” charging decision model widely assumed in the previous works. Also, the experimental results demonstrate the influences of the variable land capacity and cost and the crowding rate on the station scale.

Supplemental Notes:

This paper was sponsored by TRB committee ADC80 Standing Committee on Alternative Transportation Fuels and Technologies.

Monograph Accession #:

01618707

Report/Paper Numbers:

17-01891

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Wang, Yang
Chen, Yanyan
Yan, Changshun

Pagination:

15p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Subject Areas:

Highways; Planning and Forecasting; Terminals and Facilities

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-01891

Files:

TRIS, TRB, ATRI

Created Date:

Dec 8 2016 10:40AM