Planning of Charging Stations for Shared Electric Vehicle Based on Dynamic Stochastic Programming

被引：0

作者：

Wang Y. ^{[1
]}

Tang K. ^{[1
]}

Lai K. ^{[2
]}

Zhao Z. ^{[1
]}

Xiong J. ^{[3
]}

Liu W. ^{[3
]}

机构：

[1] School of Electrical and Information Engineering, Jiangsu University, Jiangsu Province, Zhenjiang

[2] Hitachi ABB Power Grids, San Jose, 95134, CA

[3] State Grid Xiamen Power Supply Company, Fujian Province, Xiamen

来源：

Dianwang Jishu/Power System Technology | 2022年 / 46卷 / 09期

关键词：

car sharing; dynamic stochastic programming; electric vehicle; electric vehiclecharging stations planning;

D O I：

10.13335/j.1000-3673.pst.2021.1602

中图分类号：

学科分类号：

摘要：

The cooperation between car-sharing enterprises and charging station construction companies is increasing, which calls for the co-optimization of the charging station planning and scheduling of the electric vehicles in the car-sharing business. In this paper, the dynamic stochastic programming framework is exploited to integrate a variety of operating scenarios under multi-cycles which reflect the uncertainties of transportation networks, the construction and maintenance costs of the charging stations, the drivers’ and passengers’ journeys, etc. Based on the real-time operation model of shared electric vehicle fleet, this paper develops an optimization model for the electric vehicle charging stations’ placements and type selections. The proposed model aims to improve the welfare of all passengers and reduce the total cost of fulfilling passengers’ pickup & delivery requirements simultaneously by determining the optimal locations and types of charging facilities. Using a real-world transportation network, the numerical study is conducted verifying the practicability and effectiveness of the proposed model. The results show that the locations and types of shared electric vehicle charging stations can be determined according to the actual demand by adjusting the installation budget of electric vehicle charging station, the weight factors and the charging prices, etc. The advantages of dynamic stochastic framework are verified by calculating value-of-stochastic-solution and discussing the forecasting error and cost analysis. © 2022 Power System Technology Press. All rights reserved.

引用

页码：3485 / 3495

页数：10

共 28 条

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