Bin Yu, Li-Guo Weng, Guozheng Zhou, Da-Hui Hong, Man Luo, Hao-Han Ying
{"title":"Self-Healing Power Supply Method Based on Topology Reconfiguration for Active Distribution System with Photovoltaic Generation Penetration","authors":"Bin Yu, Li-Guo Weng, Guozheng Zhou, Da-Hui Hong, Man Luo, Hao-Han Ying","doi":"10.1109/ICoPESA56898.2023.10141173","DOIUrl":null,"url":null,"abstract":"In recent years, the reliability enhancement of distribution networks has attracted increasing attention due to the occurrence of a natural large-scale power blackout. This paper proposed a self-healing power supply method based on topology reconfiguration for the active distribution system with photovoltaic (PV) generation penetration. The aim for this self-healing method is to realize the power system self-healing and improve the network reliability. A multi-objective optimization model is established to maximize power supply restoration and minimize the number of switch actions. The genetic algorithm (GA) is used to solve the optimization problem and the graph theory is used to optimize each new solution to improve the efficiency of finding the optimal solution. An IEEE 33-node test network is used to verify the efficiency of the proposed self-healing power supply method through the single-node fault and multi-node fault scenarios. The numerical results confirm that the proposed method can improve network reliability.","PeriodicalId":127339,"journal":{"name":"2023 International Conference on Power Energy Systems and Applications (ICoPESA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Power Energy Systems and Applications (ICoPESA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICoPESA56898.2023.10141173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, the reliability enhancement of distribution networks has attracted increasing attention due to the occurrence of a natural large-scale power blackout. This paper proposed a self-healing power supply method based on topology reconfiguration for the active distribution system with photovoltaic (PV) generation penetration. The aim for this self-healing method is to realize the power system self-healing and improve the network reliability. A multi-objective optimization model is established to maximize power supply restoration and minimize the number of switch actions. The genetic algorithm (GA) is used to solve the optimization problem and the graph theory is used to optimize each new solution to improve the efficiency of finding the optimal solution. An IEEE 33-node test network is used to verify the efficiency of the proposed self-healing power supply method through the single-node fault and multi-node fault scenarios. The numerical results confirm that the proposed method can improve network reliability.