基于PSO算法场景约简的有源配电网可靠性评估

Mehran Memari, A. Karimi, H. Hashemi‐Dezaki
{"title":"基于PSO算法场景约简的有源配电网可靠性评估","authors":"Mehran Memari, A. Karimi, H. Hashemi‐Dezaki","doi":"10.1109/SGC52076.2020.9335770","DOIUrl":null,"url":null,"abstract":"The challenges of active distribution networks (ADNs) have received a great deal of attention due to the increase in the penetration of distributed generations (DGs). The uncertainties of renewable DGs adversely affect the ADNs performance; thus, reliability evaluation of ADNs considering uncertainties is important. The analytical and Monte Carlo Simulation (MCS) methods are used for studying the uncertainties, while MCS is more popular than analytical methods. The main challenge of the MCS method is its computation time. In this paper, a new scenario-based reliability evaluation method using particle swarm optimization (PSO) clustering algorithm is proposed, which can significantly reduce the computation time of reliability evaluation in comparison with MCS. Also, the optimal number of clusters is obtained in the introduced method. Test results of applying the introduced method to the real 20 kV distribution network of the Barzok region of Kashan in Iran illustrate its effectiveness and advantages.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Reliability evaluation of active distribution networks based on scenario reduction method using PSO algorithm\",\"authors\":\"Mehran Memari, A. Karimi, H. Hashemi‐Dezaki\",\"doi\":\"10.1109/SGC52076.2020.9335770\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The challenges of active distribution networks (ADNs) have received a great deal of attention due to the increase in the penetration of distributed generations (DGs). The uncertainties of renewable DGs adversely affect the ADNs performance; thus, reliability evaluation of ADNs considering uncertainties is important. The analytical and Monte Carlo Simulation (MCS) methods are used for studying the uncertainties, while MCS is more popular than analytical methods. The main challenge of the MCS method is its computation time. In this paper, a new scenario-based reliability evaluation method using particle swarm optimization (PSO) clustering algorithm is proposed, which can significantly reduce the computation time of reliability evaluation in comparison with MCS. Also, the optimal number of clusters is obtained in the introduced method. Test results of applying the introduced method to the real 20 kV distribution network of the Barzok region of Kashan in Iran illustrate its effectiveness and advantages.\",\"PeriodicalId\":391511,\"journal\":{\"name\":\"2020 10th Smart Grid Conference (SGC)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 10th Smart Grid Conference (SGC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SGC52076.2020.9335770\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th Smart Grid Conference (SGC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SGC52076.2020.9335770","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

随着分布式代(dg)的普及,主动配电网络(ADNs)面临的挑战受到了广泛关注。可再生dg的不确定性对ADNs的性能有不利影响;因此,考虑不确定性的ADNs可靠性评估是很重要的。分析方法和蒙特卡罗模拟方法(MCS)是研究不确定性的常用方法,但MCS比分析方法更受欢迎。MCS方法的主要挑战是其计算时间。本文提出了一种新的基于场景的可靠性评估方法,该方法采用粒子群优化(PSO)聚类算法,与MCS相比可显著减少可靠性评估的计算时间。该方法还得到了最优聚类数。将该方法应用于伊朗卡尚巴尔佐克地区20kv实际配电网的试验结果表明了该方法的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliability evaluation of active distribution networks based on scenario reduction method using PSO algorithm
The challenges of active distribution networks (ADNs) have received a great deal of attention due to the increase in the penetration of distributed generations (DGs). The uncertainties of renewable DGs adversely affect the ADNs performance; thus, reliability evaluation of ADNs considering uncertainties is important. The analytical and Monte Carlo Simulation (MCS) methods are used for studying the uncertainties, while MCS is more popular than analytical methods. The main challenge of the MCS method is its computation time. In this paper, a new scenario-based reliability evaluation method using particle swarm optimization (PSO) clustering algorithm is proposed, which can significantly reduce the computation time of reliability evaluation in comparison with MCS. Also, the optimal number of clusters is obtained in the introduced method. Test results of applying the introduced method to the real 20 kV distribution network of the Barzok region of Kashan in Iran illustrate its effectiveness and advantages.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信