水电短期调度的模拟退火方法

K. Wong, Y. W. Wong, Yunbei Yu
{"title":"水电短期调度的模拟退火方法","authors":"K. Wong, Y. W. Wong, Yunbei Yu","doi":"10.1109/ANN.1993.264327","DOIUrl":null,"url":null,"abstract":"This paper develops a hydro-scheduling algorithm based on the simulated annealing technique for a two-interval schedule horizon. In the algorithm, the load balance constraint, the total water discharge constraint and the constraint on the operation limits of the equivalent thermal generator are fully accounted for. The performance of the algorithm is demonstrated through its application to a test system. The results are presented and are compared to a conventional method.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A simulated annealing approach to short-term hydro scheduling\",\"authors\":\"K. Wong, Y. W. Wong, Yunbei Yu\",\"doi\":\"10.1109/ANN.1993.264327\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper develops a hydro-scheduling algorithm based on the simulated annealing technique for a two-interval schedule horizon. In the algorithm, the load balance constraint, the total water discharge constraint and the constraint on the operation limits of the equivalent thermal generator are fully accounted for. The performance of the algorithm is demonstrated through its application to a test system. The results are presented and are compared to a conventional method.<<ETX>>\",\"PeriodicalId\":121897,\"journal\":{\"name\":\"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-04-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANN.1993.264327\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANN.1993.264327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

提出了一种基于模拟退火技术的双区间调度算法。该算法充分考虑了负荷平衡约束、总放水量约束和等效热力发生器运行极限约束。通过对测试系统的应用,验证了该算法的性能。给出了计算结果,并与传统方法进行了比较。
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
A simulated annealing approach to short-term hydro scheduling
This paper develops a hydro-scheduling algorithm based on the simulated annealing technique for a two-interval schedule horizon. In the algorithm, the load balance constraint, the total water discharge constraint and the constraint on the operation limits of the equivalent thermal generator are fully accounted for. The performance of the algorithm is demonstrated through its application to a test system. The results are presented and are compared to a conventional method.<>
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
期刊最新文献
An adaptive fuzzy logic controller for AC-DC power systems Discrimination of partial discharge from noise in XLPE cable lines using a neural network Automation, with neural network based techniques, of short-term load forecasting at the Belgian national control centre Maximum electric power demand prediction by neural network Restoring current signals in real time using feedforward neural nets
×
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