Article(id=1223193063225414048, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223193053830169317, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221380, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1657036800000, receivedDateStr=2022-07-06, revisedDate=1659974400000, revisedDateStr=2022-08-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561552101, onlineDateStr=2026-01-28, pubDate=1674576000000, pubDateStr=2023-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561552101, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561552101, creator=13701087609, updateTime=1769561552101, 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=82, endPage=86, ext={EN=ArticleExt(id=1223193064806666718, articleId=1223193063225414048, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Risk-based Optimal Combination Model of Medium and Long-term Contract Electricity for Cascade Hydropower Stations, columnId=1222925283431272938, journalTitle=Water Resources and Power, columnName=HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING, runingTitle=null, highlight=null, articleAbstract=

In view of the uncertain factors of natural inflow and electricity price during participating in bilateral transactions at different time scales for cascade hydropower plants, there are problems of market share determination, water resources utilization and profit risk since electricity allocated irrationally. Therefore, this paper presented a risk-based optimal combination model of medium- and long-term contract electricity for cascade hydropower stations. To realize the goal of the minimum profit risk under a given confidence, the monthly inflow process was randomly generated by annual inflow data. Three electricity price functions were fitted by different proportions of hydropower stations in the market. The combination of power output and contract electricity of cascade hydropower plants were solved by the loop-Iteration method of hybrid progressive optimization with successive approximation method. The model was demonstrated using the hydropower plants in the southwest region. The results show that proposed model can determine the declaration strategy between monthly and annual bilateral contract electricity, and reduce the profit risk. It can provide reliable basis for bilateral transactions at different time scales of cascade hydropower plants.

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针对梯级水电站群参与不同时间尺度的合约电量交易时天然来水和交易电价的不确定性因素导致不同市场份额难以确定、水资源利用不充分及不同时间尺度电量分配不合理导致收益存在较大风险问题，提出了考虑收益风险的梯级水电站群中长期合约电量最优组合模型，该模型采用给定置信度下整个梯级发电收益风险最小为目标，其求解首先由多年来水数据生成随机月尺度来水过程，然后根据梯级水电站占市场比重不同模拟出三种电价函数，采用逐步优化和逐次逼近混合算法对电站出力和合约电量组合循环迭代求解。最后以西南地区某流域梯级水电站为例进行验证。结果表明，所提出的优化调度模型能够合理制定年度和月度双边合约电量申报策略，有效降低收益风险，为梯级水电站群参与不同时间尺度的双边交易市场提供可靠依据。

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郝振凯（1999-），男，硕士研究生，研究方向为电力市场，E-mail：71969317@qq.com

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郝振凯（1999-），男，硕士研究生，研究方向为电力市场，E-mail：71969317@qq.com

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