Optimal Power Flow for Load Margin Improvement using Evolutionary Programming

N. Aminudin, T. Rahman, I. Musirin
{"title":"Optimal Power Flow for Load Margin Improvement using Evolutionary Programming","authors":"N. Aminudin, T. Rahman, I. Musirin","doi":"10.1109/SCORED.2007.4451418","DOIUrl":null,"url":null,"abstract":"Optimal power flow (OPF) has been profoundly identified as one of the main issues in power system operation and planning. Solving the OPF problem is the fundamental aspect in the determination of electricity prices and congestion management. It involves a large dimension nonlinear, non-convex and highly constrained optimization problem which requires a reliable technique in order to provide global optimal solution. This paper presents the application of evolutionary programming (EP) technique in OPF for load margin enhancement with consideration of operational cost and loss reduction. In realizing the effectiveness of the proposed technique, validation was conducted on the IEEE-26 reliability test system. The results obtained from the pre-optimization and post-optimization are compared. The results revealed that the proposed OPF using EP has successfully improved the load margin taking the pre-optimization results as reference.","PeriodicalId":443652,"journal":{"name":"2007 5th Student Conference on Research and Development","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 5th Student Conference on Research and Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCORED.2007.4451418","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

Abstract

Optimal power flow (OPF) has been profoundly identified as one of the main issues in power system operation and planning. Solving the OPF problem is the fundamental aspect in the determination of electricity prices and congestion management. It involves a large dimension nonlinear, non-convex and highly constrained optimization problem which requires a reliable technique in order to provide global optimal solution. This paper presents the application of evolutionary programming (EP) technique in OPF for load margin enhancement with consideration of operational cost and loss reduction. In realizing the effectiveness of the proposed technique, validation was conducted on the IEEE-26 reliability test system. The results obtained from the pre-optimization and post-optimization are compared. The results revealed that the proposed OPF using EP has successfully improved the load margin taking the pre-optimization results as reference.
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
基于进化规划的负荷裕度优化潮流
最优潮流(OPF)已成为电力系统运行和规划中的主要问题之一。解决OPF问题是电价确定和拥堵管理的基础。它涉及到一个大尺寸的非线性、非凸和高度约束的优化问题,需要一种可靠的技术来提供全局最优解。本文将进化规划(EP)技术应用于OPF中,以提高负荷裕度,同时考虑运行成本和减少损耗。为了验证该技术的有效性,在IEEE-26可靠性测试系统上进行了验证。对优化前后的结果进行了比较。结果表明,在参考预优化结果的基础上,基于EP的OPF成功地提高了负荷裕度。
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
Analysis of Network Communication Attacks Secure Transport Protocols for DDoS Attack Resistant Communication Analysis of Partial Discharge Measurement Data Using a Support Vector Machine In Silico Information Processing for DNA Computing Readout Method based on DNA Engine Opticon 2 System Study on Stability and Performances of DTC Due to Stator Resistance Variation
×
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