Reliability evaluation of active distribution networks based on scenario reduction method using PSO algorithm

Mehran Memari, A. Karimi, H. Hashemi‐Dezaki
{"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}
引用次数: 4

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于PSO算法场景约简的有源配电网可靠性评估
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。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Multi-Objective Generation Expansion Planning with Modeling Load Demand Uncertainty by a Deep Learning- Based Approach Distributed Secondary Voltage and Current Control Scheme with Noise Nullification Ability for DC Microgrids A Robust Controller for Stabilization HVAC Systems in Smart Homes On the Impact of Cyber-Attacks on Distributed Secondary Control of DC Microgrids Reliability evaluation of active distribution networks based on scenario reduction method using PSO algorithm
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1