Article(id=1221467206781682343, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221467200582500783, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202209238, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1663603200000, receivedDateStr=2022-09-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769150075860, onlineDateStr=2026-01-23, pubDate=1684944000000, pubDateStr=2023-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769150075860, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769150075860, creator=13701087609, updateTime=1769150075860, updator=13701087609, issue=Issue{id=1221467200582500783, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='5', pageStart='1', pageEnd='166', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769150074382, creator=13701087609, updateTime=1769157444393, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1221498112716226774, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221467200582500783, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1221498112716226775, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221467200582500783, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=92, endPage=99, ext={EN=ArticleExt(id=1221467207029146284, articleId=1221467206781682343, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Optimization of automatic generation control response performance of flywheel-thermal power system based on load forecasting, columnId=1221467202000175547, journalTitle=Thermal Power Generation, columnName=Thermal energy and science research, runingTitle=null, highlight=null, articleAbstract=

As the proportion of new energy power generation continues to increase, the stability of grid frequency is severely challenged, and the role of conventional thermal power units in grid frequency regulation has become increasingly prominent. However, the adjustment rate and accuracy of some thermal power units are difficult to meet the demand of grid load fluctuations. Therefore, a response performance optimization strategy for flywheel-thermal power system automatic generation control based on load forecasting was proposed. Firstly, the load is predicted, using the tree-based pipeline optimization tool TPOT library to automatically machine learning to match and train the load regression prediction model, and the automatic generation control day-ahead planned value is introduced into the training data to reduce the prediction error. Then, according to the load prediction value and the current flywheel system, with the optimization goal of minimizing the regulation rate of thermal power units, the flywheel energy storage system is acted firstly in load distribution, and the state of charge of the flywheel is adjusted meanwhile. Finally, a simulation experiment is carried out based on the actual operation data of a power plant in Hubei, and the experimental results prove that the proposed method can effectively improve the frequency modulation performance of thermal power units.

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随着新能源发电占比的不断增加，电网频率的稳定性受到严峻挑战，传统火电机组参与电网调频的作用愈发突出，然而部分火电机组的调节速率和精度难以满足电网负荷变动需求。为此，提出一种基于负荷预测的飞轮-火电系统自动发电控制响应性能优化策略。首先对负荷进行预测，采用基于树的管道优化工具TPOT库自动机器学习搭配并训练负荷回归预测模型，在训练数据中引入自动发电控制日前计划值以减少预测误差；然后根据负荷预测值以及飞轮系统的当前荷电状态，以火电机组调节速率最小化为优化目标，在负荷分配中优先动作飞轮储能系统，并调整飞轮荷电状态；最后基于湖北某火电厂实际运行数据进行仿真实验，实验结果证明了所提方法能够有效改善火电机组的调频性能。

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魏乐（1976），女，博士，教授，主要研究方向为新能源发电系统故障诊断、火电厂智能优化控制，weile@ncepu.edu.cn。

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魏乐（1976），女，博士，教授，主要研究方向为新能源发电系统故障诊断、火电厂智能优化控制，weile@ncepu.edu.cn。

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