Robust Joint Planning of Electric Vehicle Charging Infrastructures and Distribution Networks
Xue, Ping; Xiang, Yue; Shafie-Khah, Miadreza; Zhou, Run; Guo, Yongtao; Guo, Jingrong; Liu, Junyong (2022-06-01)
Xue, Ping
Xiang, Yue
Shafie-Khah, Miadreza
Zhou, Run
Guo, Yongtao
Guo, Jingrong
Liu, Junyong
IEEE
01.06.2022
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2023030730304
https://urn.fi/URN:NBN:fi-fe2023030730304
Kuvaus
vertaisarvioitu
©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Tiivistelmä
The electric vehicles (EVs) are developed rapidly due to the demand of fossil fuel depletion and environmental pressure. However, as a flexible and clean resource, EV charging is full of uncertainty. With its large-scale integration in grid, the reliable operation of distribution network is challenged, especially when there is a large penetration of distributed generation (DG) in the system as well. As a result, how to perform the distribution network planning to ensure its reliable operation under the uncertainties is an urgent need for the current grid development. Based on this, considering the uncertainty of EV charging demand and DG supply, a robust joint planning model of EV charging infrastructures and distribution networks is proposed in this paper to adapt to the grid development. Big-M technique and second-order cone relaxation technique are adopted to linearize the non-linear part. Finally, the effectiveness and scalability of the proposed method is illustrated by the numerical case studies.
Kokoelmat
- Artikkelit [3030]