Article(id=1309639403602014216, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639371255542472, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1735056000000, receivedDateStr=2024-12-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171965742, onlineDateStr=2026-09-23, pubDate=1763568000000, pubDateStr=2025-11-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171965742, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171965742, creator=13701087609, updateTime=1790171965742, updator=13701087609, issue=Issue{id=1309639371255542472, tenantId=1146029695717560320, journalId=1146119893612605453, year='2025', volume='43', issue='11', pageStart='1421', pageEnd='1562', issueExtLink='null', onlineDate='null', pubDate='1763568000000', pubDateStr='2025-11-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171958030, creator='13701087609', updateTime=1790231751141, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309890161824063505, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639371255542472, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309890161824063506, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639371255542472, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1537, endPage=1546, ext={EN=ArticleExt(id=1309639403773980681, articleId=1309639403602014216, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=Load frequency control strategy for new energy distribution network considering dispatchable resources of source load storage, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

With the integration of dispatchable resources such as distributed photovoltaics and energy storage into the distribution network, the demand for dynamic frequency regulation capability in the system has significantly increased, posing comprehensive challenges to load frequency stability control. To better mitigate frequency fluctuations in the new energy distribution network system, a new energy distribution network load frequency control strategy considering source load storage dispatchable resources is proposed. Firstly, based on the frequency response characteristics of distributed photovoltaic, energy storage equipment, and demand side interruptible loads, a load frequency control model for the new energy distribution network was established; Then, based on model predictive control, frequency adjustment was achieved without difference, and the control parameters were adaptively adjusted using deep learning algorithms. A multidimensional index system was constructed to comprehensively evaluate the control performance of the proposed control strategy; Finally, the effectiveness of the proposed method was verified through Matlab/Simulink simulation.

, authors=Xijun Wang¹, Wei Wang¹, Yinsong Li¹, Jin Hou¹, Long Lü², authorsList=Xijun Wang, Wei Wang, Yinsong Li, Jin Hou, Long Lü, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309639404025638922, articleId=1309639403602014216, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=考虑源-荷-储可调度资源的新能源配电网负荷频率控制策略, columnId=0, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

随着分布式光伏和储能等可调度资源接入配电网，系统对动态频率调节能力的需求大幅提升，给负荷频率稳定控制带来了综合性挑战。为更好地平抑新能源配电网系统的频率波动，文章提出了考虑源-荷-储可调度资源的新能源配电网负荷频率控制策略。首先，根据分布式光伏、储能设备以及需求侧可中断负荷的频率响应特性，建立了新能源配电网负荷频率控制模型；然后，基于模型预测控制实现了频率的无差调节，结合深度学习算法对控制参数进行自适应调整，并构建多维指标体系以全面评估所提控制策略的控制性能；最后，通过Matlab/Simulink仿真验证了所提控制策略的有效性。

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王喜军（1968-），男，高级工程师，主要从事新能源及储能并网关键技术研究。E-mail: dapeng6310@163.com

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王喜军（1968-），男，高级工程师，主要从事新能源及储能并网关键技术研究。E-mail: dapeng6310@163.com

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