Article(id=1223190868899775186, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221876, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1664208000000, receivedDateStr=2022-09-27, revisedDate=1666540800000, revisedDateStr=2022-10-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561028932, onlineDateStr=2026-01-28, pubDate=1695571200000, pubDateStr=2023-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561028932, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561028932, creator=13701087609, updateTime=1769561028932, updator=13701087609, issue=Issue{id=1223190866320278179, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='9', 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=1769561028318, creator=13701087609, updateTime=1769562015483, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223195006870082478, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223195006870082479, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=51, endPage=54, ext={EN=ArticleExt(id=1223190872615928744, articleId=1223190868899775186, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Reservoir Operation Based on Runoff Forecast Considering Output Characteristics of Regional Small Hydropower, columnId=1222925283431272938, journalTitle=Water Resources and Power, columnName=HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING, runingTitle=null, highlight=null, articleAbstract=

As an important part of rural energy, small hydropower plays an important role in driving rural economic development and promoting energy saving and emissions reduction. However, there is a lack of scientific guidance in the process of optimal operation of small hydropower stations in most areas. This paper takes the Mengdong River basin as an example to establish a cascade optimal operation model of small hydropower stations based on runoff simulation. The Xinanjiang (XAJ) model is combined with Manning's formula to establish a semi-distributed hydrological model for simulating the influence of reservoir operation on runoff. The successive approximation dynamic programming method is used to achieve optimal operation by considering the actual operating characteristics of small hydropower stations. The results demonstrate that the semi-distributed hydrological model has a good performance in the river basin. The changing pattern of simulated and measured runoff processes is basically consistent, and the Nash-Sutcliffe efficiency coefficient and coefficient of determination between the measured and simulated series are 0.86. The optimal operation model can reduce the spillage water effectively, which improves the utilization efficiency of water energy. Meanwhile, the optimal operation can match the actual intra-day features of small hydropower, indicating its feasibility in applications.

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小水电作为农村能源的重要组成部分，为带动农村经济发展，促进节能减排作出了重大贡献。但目前大部分地区小水电在优化运行的过程中缺乏科学指导。为此，以猛洞河流域为例，建立了基于径流模拟的梯级小水电优化调度模型。首先，结合新安江模型和曼宁公式，构建半分布式水文模型模拟水库运行对河道径流过程的影响；其次，基于逐次逼近动态规划法，结合小水电实际运行特征，求得最优调度方案。结果表明，构建的半分布式水文模型在该流域拟合效果较好，模拟与实测径流过程的变化趋势基本一致，序列间纳什效率系数和决定系数均为0.86；优化调度模型可有效减少电站弃水，提升流域水资源利用效率，且优化调度过程能充分符合小水电在日内的运行规律，具有实际应用价值。

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宋兴荣（1976-），男，高级工程师，研究方向为电力系统，E-mail：songxr@hn.sgcc.com.cn

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宋兴荣（1976-），男，高级工程师，研究方向为电力系统，E-mail：songxr@hn.sgcc.com.cn

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