Article(id=1223190875837157962, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1664121600000, receivedDateStr=2022-09-26, revisedDate=1667577600000, revisedDateStr=2022-11-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561030586, onlineDateStr=2026-01-28, pubDate=1695571200000, pubDateStr=2023-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561030586, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561030586, creator=13701087609, updateTime=1769561030586, 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=55, endPage=59, ext={EN=ArticleExt(id=1223190876264976994, articleId=1223190875837157962, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Study on Rolling Prediction of Power Generation Plan in Dry Season of Dadu River Basin, columnId=1222925283431272938, journalTitle=Water Resources and Power, columnName=HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING, runingTitle=null, highlight=null, articleAbstract=

Under the situation of "abundant and dry electricity supply" in Sichuan Province, affected by complex market environment factors and intensified power supply competition, accurate prediction of power generation plan is the key to optimizing the power generation mode of hydropower in dry periods in the basin. This paper analyzed the power supply capacity of the main grid, new energy power, power regulation capacity of the national power grid, outsourcing power transmission capacity and power generation capacity of the provincial hydropower and thermal power, respectively, and established a forecasting method for the generation plan of the cascade hydropower stations in the Dadu River basin. The results show that the prediction accuracy of power supply in main grid is 91.8%, and the prediction accuracy of wind and solar power generation is 87.4%. The prediction accuracy of the medium and long-term overall power generation plan of the hydropower station belonging to Guoneng Dadu River Basin Hydropower Development Co., Ltd. is 90.5%. The model can effectively predict the electricity quantity of each power source and calculate the medium and long-term power generation plan of the cascade hydropower stations in the Dadu River basin, and intelligently generate the fluctuation process under different working conditions based on the prediction results, which provides technical support for the scientific dispatching and operation of the cascade hydropower stations.

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在四川省电力供给“丰裕枯余”形势下，受市场环境因素复杂、电源竞争加剧等影响，发电计划精准预测是优化流域水电枯期发电方式的关键。对此，分别针对四川省主网供电量、新能源电量、国调网调电量、外购外送电量及省调水电火电电量进行分析，建立了大渡河流域梯级电站发电计划预测方法。结果表明，主网供电预测精度为91.8%，风光发电量预测精度为87.4%，归属于国能大渡河流域水电开发有限公司电站中长期整体发电计划预测精度为90.5%。模型可有效预测各电源电量，进而计算大渡河流域梯级电站中长期发电计划，并基于预测结果智能生成不同工况下的消落过程，为梯级电站群科学调度运行提供技术支撑。

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曲田（1984-），女，硕士、工程师，研究方向为水文水资源，E-mail：362805700@qq.com

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曲田（1984-），女，硕士、工程师，研究方向为水文水资源，E-mail：362805700@qq.com

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