Coordinated Planning for Fast Charging Stations and Distribution Networks Based on an Improved Flow Capture Location Model

Shiqu Xiao; Xia Lei; Tao Huang; Xiang Wang

doi:10.17775/CSEEJPES.2021.01470

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Coordinated Planning for Fast Charging Stations and Distribution Networks Based on an Improved Flow Capture Location Model

Regular Papers | 更新时间：2026-10-15

- Coordinated Planning for Fast Charging Stations and Distribution Networks Based on an Improved Flow Capture Location Model
- CSEE Journal of Power and Energy Systems Vol. 9, Issue 4, Pages: 1505-1516(2023)
- 作者机构：
  
  1. School of Electrical Engineering and Electronic Information, Xihua University,Chengdu,China
  2. Department Ingegneria Elettrica, Politecnico Di Torino, Torino,Italy
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.01470
  CLC：
- Published：01 July 2023
- 稿件说明：
移动端阅览
Shiqu Xiao, Xia Lei, Tao Huang, et al. Coordinated Planning for Fast Charging Stations and Distribution Networks Based on an Improved Flow Capture Location Model[J]. CSEE Journal of Power and Energy Systems, 2023, 9(4): 1505-1516.
DOI：

Shiqu Xiao, Xia Lei, Tao Huang, et al. Coordinated Planning for Fast Charging Stations and Distribution Networks Based on an Improved Flow Capture Location Model[J]. CSEE Journal of Power and Energy Systems, 2023, 9(4): 1505-1516. DOI： 10.17775/CSEEJPES.2021.01470.

摘要

Abstract

The increasing penetration of plug-in electric vehicles (PEVs) should lead to a significant reduction in greenhouse gas emissions. Nevertheless

the development of PEVs is limited by the lack of charging facilities

which is constrained by the coupled transportation-distribution network. This paper presents a stochastic bi-level model for the optimal allocation of fast charging stations (FCSs) and distribution network expansion planning (DNEP). First

a sequential capacitated flow-capturing location-allocation model (SCFCLM) is proposed at the lower level to optimize the allocation of FCSs on the transportation network. Monte-Carlo simulation (MCS) is utilized to estimate daily charging load requirements. Then

we propose an economic model for DNEP at the upper level

and the chance constrained method is employed to relax power flow constraints to address the uncertainties of loads. Numerical experiments are conducted to illustrate the proposed planning method. The influences of the flow capturing sequence and relaxed confidence level on the PEV charging load

FCS planning strategies and DNEP schemes are analyzed.

关键词

Keywords

references

27. C. Olson, E. Lee, and B. Langdon, ( 2005 ). “ White Paper on Ion Beam Transport for ICF: Issues, RD Need, and Tri-Lab Plans ,” [Online ] : Available: https://escholarship.org/uc/item/5zs0k05p.

29. L. Zhang, R. Y. Huo, G. W. Cai, K. L. Hai, L. Lyu, and P. Wang, “A joint planning method of charging piles and charging-battery swapping stations considering spatial-temporal distribution of electric vehicles,” CSEE Journal of Power and Energy Systems . [Online ] , Available: 10.17775/CSEEJPES.2021.05780.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Maximizing Robust and Risk-Averse Hosting Capacity for Unbalanced Distribution Network with Volt/Var Control

Virtual P2P Transaction Integrated Expansion Planning for Distribution Networks

Robust Expansion Planning of Electric Vehicle Charging System and Distribution Networks

Novel Planning Approach for Fast-charging Station in Integrated System

Optimal Energy Management of Networked Multi-Energy Microgrids Based on Improved Multi-Agent Federated Reinforcement Learning

Related Author

Qijun Liang

Cuo Zhang

Jianguo Zhu

Jiakun Dai

Yue Xiang

Yongtao Guo

Ping Xue

Yanliang Wang

Related Institution

School of Electrical and Computer Engineering, The University of Sydney

College of Electrical Engineering, Sichuan University

State Grid Sichuan Technical Training Center, Sichuan Electric Vocational and Technical College

School of Technology and Innovations, University of Vaasa

Department of Electronic and Electrical Engineering, University of Bath

AI问答

⁰