优化配电系统中光伏分布式发电的效率

Ly Huu Pham, Tai Thanh Phan, Van Thanh Ngoc Nguyen, Khoa Dang Tran Phan, Phung Hai Nguyen
{"title":"优化配电系统中光伏分布式发电的效率","authors":"Ly Huu Pham, Tai Thanh Phan, Van Thanh Ngoc Nguyen, Khoa Dang Tran Phan, Phung Hai Nguyen","doi":"10.55579/jaec.202481.440","DOIUrl":null,"url":null,"abstract":"This article studies the influence of distributed generation (DG), specifically the influence of photovoltaic (PV) in the distribution system. The particle swarm optimization algorithm (PSO) will be applied to determine the best capacity and location of PV on a test system of EEE 33 nodes so that active power loss is minimized, and the voltage profile is improved. The performance of the applied method is evaluated by comparing its results to those from some previous methods, including the Genetic Algorithm (GA), the Bacterial Foraging Optimization Algorithm (BFOA), and the Backtracking Search Optimization Algorithm (BSOA). As a result, it proved that the proposed method is better than others in terms of processing time, voltage profile, and minimization system capacity loss. In addition, the main contribution of the study is to give detailed solutions for operators in installing how many PVs in the power system can satisfy economic and technical aspects. ","PeriodicalId":33374,"journal":{"name":"Journal of Advanced Engineering and Computation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing the Efficiency of Photovoltaic Distributed Generation in the Distribution System\",\"authors\":\"Ly Huu Pham, Tai Thanh Phan, Van Thanh Ngoc Nguyen, Khoa Dang Tran Phan, Phung Hai Nguyen\",\"doi\":\"10.55579/jaec.202481.440\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article studies the influence of distributed generation (DG), specifically the influence of photovoltaic (PV) in the distribution system. The particle swarm optimization algorithm (PSO) will be applied to determine the best capacity and location of PV on a test system of EEE 33 nodes so that active power loss is minimized, and the voltage profile is improved. The performance of the applied method is evaluated by comparing its results to those from some previous methods, including the Genetic Algorithm (GA), the Bacterial Foraging Optimization Algorithm (BFOA), and the Backtracking Search Optimization Algorithm (BSOA). As a result, it proved that the proposed method is better than others in terms of processing time, voltage profile, and minimization system capacity loss. In addition, the main contribution of the study is to give detailed solutions for operators in installing how many PVs in the power system can satisfy economic and technical aspects. \",\"PeriodicalId\":33374,\"journal\":{\"name\":\"Journal of Advanced Engineering and Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advanced Engineering and Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55579/jaec.202481.440\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Engineering and Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55579/jaec.202481.440","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文研究了分布式发电(DG)的影响,特别是光伏(PV)在配电系统中的影响。粒子群优化算法(PSO)将用于确定 EEE 33 节点测试系统中光伏的最佳容量和位置,从而使有功功率损耗最小,电压曲线得到改善。通过与遗传算法(GA)、细菌觅食优化算法(BFOA)和回溯搜索优化算法(BSOA)等以往方法的结果进行比较,对所应用方法的性能进行了评估。结果证明,所提出的方法在处理时间、电压曲线和最小化系统容量损失方面都优于其他方法。此外,该研究的主要贡献在于为运营商提供了详细的解决方案,帮助他们在电力系统中安装多少光伏发电设备才能满足经济和技术方面的要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Optimizing the Efficiency of Photovoltaic Distributed Generation in the Distribution System
This article studies the influence of distributed generation (DG), specifically the influence of photovoltaic (PV) in the distribution system. The particle swarm optimization algorithm (PSO) will be applied to determine the best capacity and location of PV on a test system of EEE 33 nodes so that active power loss is minimized, and the voltage profile is improved. The performance of the applied method is evaluated by comparing its results to those from some previous methods, including the Genetic Algorithm (GA), the Bacterial Foraging Optimization Algorithm (BFOA), and the Backtracking Search Optimization Algorithm (BSOA). As a result, it proved that the proposed method is better than others in terms of processing time, voltage profile, and minimization system capacity loss. In addition, the main contribution of the study is to give detailed solutions for operators in installing how many PVs in the power system can satisfy economic and technical aspects. 
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
审稿时长
12 weeks
期刊最新文献
Optimizing the Efficiency of Photovoltaic Distributed Generation in the Distribution System Optimal renewable-integrated economic load dispatch for a large-scale power system using One-to-One Optimization Algorithm Design and Simulation of a High Performance 5G mm-Wave MIMO Antenna Array for Mobile Applications An Analysis of Sliding Mode Speed Controller for a Differential Drive Wheel Mobile Robot STUDY OF K2SiF6:Mn4+@SiO2 PHOSPHOR FOR WHITE LEDS WITH HIGH ANGULAR COLOR UNIFORMITY
×
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