Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System

Aashish Kumar Bohre, G. Agnihotri, Manisha Dubey, S. Kalambe
{"title":"Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System","authors":"Aashish Kumar Bohre, G. Agnihotri, Manisha Dubey, S. Kalambe","doi":"10.1155/2015/297436","DOIUrl":null,"url":null,"abstract":"The optimal planning (sizing and siting) of the distributed generations (DGs) by using butterfly-PSO/BF-PSO technique to investigate the impacts of load models is presented in this work. The validity of the evaluated results is confirmed by comparing with well-known Genetic Algorithm (GA) and standard or conventional particle swarm optimization (PSO). To exhibit its compatibility in terms of load management, an impact of different load models on the size and location of DG has also been presented in this work. The fitness evolution function explored is the multiobjective function (FMO), which is based on the three significant indexes such as active power loss, reactive power loss, and voltage deviation index. The optimal solution is obtained by minimizing the multiobjective fitness function using BF-PSO, GA, and PSO technique. The comparison of the different optimization techniques is given for the different types of load models such as constant, industrial, residential, and commercial load models. The results clearly show that the BF-PSO technique presents the superior solution in terms of compatibility as well as computation time and efforts both. The algorithm has been carried out with 15-bus radial and 30-bus mesh system.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adv. Artif. Intell.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2015/297436","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

The optimal planning (sizing and siting) of the distributed generations (DGs) by using butterfly-PSO/BF-PSO technique to investigate the impacts of load models is presented in this work. The validity of the evaluated results is confirmed by comparing with well-known Genetic Algorithm (GA) and standard or conventional particle swarm optimization (PSO). To exhibit its compatibility in terms of load management, an impact of different load models on the size and location of DG has also been presented in this work. The fitness evolution function explored is the multiobjective function (FMO), which is based on the three significant indexes such as active power loss, reactive power loss, and voltage deviation index. The optimal solution is obtained by minimizing the multiobjective fitness function using BF-PSO, GA, and PSO technique. The comparison of the different optimization techniques is given for the different types of load models such as constant, industrial, residential, and commercial load models. The results clearly show that the BF-PSO technique presents the superior solution in terms of compatibility as well as computation time and efforts both. The algorithm has been carried out with 15-bus radial and 30-bus mesh system.
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
负荷模型对配电系统分布式发电优化规划的影响
本文采用蝴蝶-粒子群算法和bf -粒子群算法研究了不同负荷模型对分布式发电机组的影响,并对分布式发电机组进行了最优规划(规模和选址)。通过与遗传算法(GA)和标准粒子群算法(PSO)的比较,验证了评价结果的有效性。为了展示其在负荷管理方面的兼容性,本工作还介绍了不同负荷模型对DG的大小和位置的影响。所探索的适应度进化函数是基于有功损耗、无功损耗和电压偏差指标三个重要指标的多目标函数(FMO)。利用BF-PSO、遗传算法和粒子群算法对多目标适应度函数进行最小化,得到最优解。针对恒负荷、工业负荷、住宅负荷和商业负荷等不同类型的负荷模型,比较了不同的优化技术。结果清楚地表明,BF-PSO技术在兼容性、计算时间和工作量方面都具有优越的解决方案。该算法在15总线径向和30总线网格系统中进行。
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
iWordNet: A New Approach to Cognitive Science and Artificial Intelligence Natural Language Processing and Fuzzy Tools for Business Processes in a Geolocation Context Method for Solving LASSO Problem Based on Multidimensional Weight Selection and Configuration of Sorption Isotherm Models in Soils Using Artificial Bees Guided by the Particle Swarm Weighted Constraint Satisfaction for Smart Home Automation and Optimization
×
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