Skip to main content

ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1460894
This article is part of the Research Topic Optimal Scheduling of Demand Response Resources In Energy Markets For High Trading Revenue and Low Carbon Emissions View all 18 articles

Conditional Value at Risk-based Island Partitioning and Fault Restoration Reconfiguration of Active Distribution Networks

Provisionally accepted
Zhuyi Peng Zhuyi Peng 1Wenjia Zhang Wenjia Zhang 1Wenchao Xu Wenchao Xu 2Hui Cai Hui Cai 1Feifei Zhao Feifei Zhao 1Xingning Han Xingning Han 1Kanghui Gu Kanghui Gu 2*
  • 1 State Grid Jiangsu Electric Power Co., LTD, Nanjing, China
  • 2 Other, Nanjing, China

The final, formatted version of the article will be published soon.

    In response to the increased penetration of distributed energy resources in distribution networks and the need to ensure stable and reliable operation in the face of faults, this study proposes a method for partitioning distribution network islands and reconstructing faults considering the Conditional Value at Risk (CVaR). This method aims to enhance the resilience of the distribution network and the recovery capability of critical loads. Initially, a partitioning model for distribution network islands based on depth-first and breadth-first search algorithms was constructed. Building upon this partitioning, a fault reconstruction method for distribution networks that considers CVaR was developed. This method utilizes CVaR theory to transform costs and quantifies the risk that the uncertainty of distributed energy resources poses to distribution network reconstruction strategies. Finally, the effectiveness of the proposed method is demonstrated using an improved IEEE 33-node system, generating typical fault scenarios.

    Keywords: Distribution network fault reconstruction, Island division, Load recovery, Conditional risk at value, Depth and breadth first search method

    Received: 07 Jul 2024; Accepted: 02 Aug 2024.

    Copyright: © 2024 Peng, Zhang, Xu, Cai, Zhao, Han and Gu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Kanghui Gu, Other, Nanjing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.