基于遗传算法的配电网DG系统最优位置和容量确定

Abdulwahab Alhamali, M. Farrag, G. Bevan, D. Hepburn
{"title":"基于遗传算法的配电网DG系统最优位置和容量确定","authors":"Abdulwahab Alhamali, M. Farrag, G. Bevan, D. Hepburn","doi":"10.1109/UPEC.2017.8231996","DOIUrl":null,"url":null,"abstract":"Concerns over global climate changes coupled with growing demand for energy are leading to increased penetration of distributed generation from intermittent sources into low voltage networks. In such cases distribution network (DN) operation will be affected. Consequently, there have been serious concerns over reliability and satisfactory operation of these power systems which contain distributed generation (DG) equipment. Distributed power generated from renewable sources is variable particularly in the case of wind generation or solar energy. The variability affects the stability of the system between supply and consumers. In DN, the losses and voltage drop across the network are significant matters and the DG location has a critical impact on the network operation. So, there is a clear need to optimise the DG size and location in the DN; for example, optimising the number of DG's and co-ordinating their operation can improve voltage drop and network losses. In this paper, an optimisation technique based on the genetic algorithm (GA) in conjunction with the power flow (PF) method is used to improve the DN performance and to identify the best location and size of the DG's. The main goal of the optimisation function is to reduce both the network losses and regulate the voltage level under different loading conditions.","PeriodicalId":272049,"journal":{"name":"2017 52nd International Universities Power Engineering Conference (UPEC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Determination of optimal site and capacity of DG systems in distribution network based on genetic algorithm\",\"authors\":\"Abdulwahab Alhamali, M. Farrag, G. Bevan, D. Hepburn\",\"doi\":\"10.1109/UPEC.2017.8231996\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Concerns over global climate changes coupled with growing demand for energy are leading to increased penetration of distributed generation from intermittent sources into low voltage networks. In such cases distribution network (DN) operation will be affected. Consequently, there have been serious concerns over reliability and satisfactory operation of these power systems which contain distributed generation (DG) equipment. Distributed power generated from renewable sources is variable particularly in the case of wind generation or solar energy. The variability affects the stability of the system between supply and consumers. In DN, the losses and voltage drop across the network are significant matters and the DG location has a critical impact on the network operation. So, there is a clear need to optimise the DG size and location in the DN; for example, optimising the number of DG's and co-ordinating their operation can improve voltage drop and network losses. In this paper, an optimisation technique based on the genetic algorithm (GA) in conjunction with the power flow (PF) method is used to improve the DN performance and to identify the best location and size of the DG's. The main goal of the optimisation function is to reduce both the network losses and regulate the voltage level under different loading conditions.\",\"PeriodicalId\":272049,\"journal\":{\"name\":\"2017 52nd International Universities Power Engineering Conference (UPEC)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 52nd International Universities Power Engineering Conference (UPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UPEC.2017.8231996\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 52nd International Universities Power Engineering Conference (UPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UPEC.2017.8231996","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16

摘要

对全球气候变化的担忧,加上对能源需求的不断增长,导致间歇性分布式发电越来越多地渗透到低压电网中。在这种情况下,将影响配电网络(DN)的运行。因此,这些包含分布式发电(DG)设备的电力系统的可靠性和令人满意的运行受到了严重关注。可再生能源产生的分布式电力是可变的,特别是在风力发电或太阳能的情况下。这种可变性影响供给者和消费者之间系统的稳定性。在分布式电网中,整个网络的损耗和压降是一个重要的问题,DG的位置对网络的运行有着至关重要的影响。因此,显然需要优化DG在DN中的大小和位置;例如,优化DG的数量并协调它们的运行可以改善电压降和网络损耗。本文提出了一种基于遗传算法(GA)的优化技术,结合功率流(PF)方法来提高分布式电网的性能,并确定分布式电网的最佳位置和大小。优化函数的主要目标是在不同负载条件下降低电网损耗和调节电压水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determination of optimal site and capacity of DG systems in distribution network based on genetic algorithm
Concerns over global climate changes coupled with growing demand for energy are leading to increased penetration of distributed generation from intermittent sources into low voltage networks. In such cases distribution network (DN) operation will be affected. Consequently, there have been serious concerns over reliability and satisfactory operation of these power systems which contain distributed generation (DG) equipment. Distributed power generated from renewable sources is variable particularly in the case of wind generation or solar energy. The variability affects the stability of the system between supply and consumers. In DN, the losses and voltage drop across the network are significant matters and the DG location has a critical impact on the network operation. So, there is a clear need to optimise the DG size and location in the DN; for example, optimising the number of DG's and co-ordinating their operation can improve voltage drop and network losses. In this paper, an optimisation technique based on the genetic algorithm (GA) in conjunction with the power flow (PF) method is used to improve the DN performance and to identify the best location and size of the DG's. The main goal of the optimisation function is to reduce both the network losses and regulate the voltage level under different loading conditions.
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