Article(id=1223193059983213452, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223193053830169317, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20220208, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1644163200000, receivedDateStr=2022-02-07, revisedDate=1649347200000, revisedDateStr=2022-04-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561551327, onlineDateStr=2026-01-28, pubDate=1674576000000, pubDateStr=2023-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561551327, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561551327, creator=13701087609, updateTime=1769561551327, updator=13701087609, issue=Issue{id=1223193053830169317, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='1', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769561549861, creator=13701087609, updateTime=1769567790701, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223219229885857794, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223193053830169317, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223219229885857795, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223193053830169317, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=87, endPage=91, ext={EN=ArticleExt(id=1223193061992284207, articleId=1223193059983213452, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Research on Monthly Generation Dispatching Function for Luntan Hydropower Plant Considering Downstream Water Demand, columnId=1222925283431272938, journalTitle=Water Resources and Power, columnName=HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING, runingTitle=null, highlight=null, articleAbstract=

In order to optimize the dispatching mode of Luntan hydropower plant, the process of socio-economic and ecological water demand in the downstream were analyzed. Considering the downstream water demand, the monthly generation dispatching function were extracted from the results of optimal generation dispatching by use of stepwise regression method. The results show that the dispatching of Luntan reservoir plays a great role in the downstream water demand process. The main period of water replenishment is from July to the following February, which directly changes the process of power generation diversion flow of Luntan hydropower plant. The NSE coefficients of the generation dispatching function of Luntan hydropower plant based on multiple linear regression model and BP neural network are greater than 0.90 in most months. Considering the advantages of simple structure and convenient implementation, the generation dispatching function based on multiple linear regression model is selected for Luntan hydropower plant. Compared with the results of actual dispatching and dispatching diagram of Luntan hydropower plant, the generation capacity of the dispatching function is increased by 338.4×10⁴ kW·h/month and 183.8×10⁴ kW·h/month from March 2017 to February 2019, respectively. As a result, the water level of Luntan Reservoir can be significantly increased by use of the dispatching function. The research can provide a support for the generation optimal dispatching process of Luntan hydropower plant, and provide a useful reference for many other hydropower plants to improve the practical way of the generation dispatching.

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为优化伦潭水电站调度运行方式，分析水库下游社会经济和河道内生态环境需水过程，基于发电优化调度结果采用逐步回归法提取考虑下游需水的逐月发电调度函数。结果表明，伦潭水库对下游需水过程发挥较大调蓄补给作用，主要补水时段为7月至次年2月，直接影响了伦潭水电站的发电引水流量过程；基于多元线性回归模型和BP神经网络提取的伦潭水电站发电调度函数大部分月份的纳什效率系数均在0.90以上，考虑模型结构简单且操作方便，伦潭水电站选取基于多元线性回归模型的发电调度函数；对于2017年3月至2019年2月，伦潭水电站发电调度函数较实际发电调度和发电调度图可分别增加发电量338.4×10⁴、183.8×10⁴kW·h/月，调度函数可明显提高伦潭水库的发电运行水位。研究成果可为伦潭水电站实际发电调度提供技术支撑，也可为其他水电站完善发电调度运行方式提供参考。

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温天福（1981-），男，博士、高级工程师，研究方向为水文水资源，E-mail：wen-tianfu@whu.edu.cn

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温天福（1981-），男，博士、高级工程师，研究方向为水文水资源，E-mail：wen-tianfu@whu.edu.cn

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