Article(id=1309639759278998374, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639744087229134, 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=1735488000000, receivedDateStr=2024-12-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172050542, onlineDateStr=2026-09-23, pubDate=1768838400000, pubDateStr=2026-01-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172050542, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172050542, creator=13701087609, updateTime=1790172050542, updator=13701087609, issue=Issue{id=1309639744087229134, tenantId=1146029695717560320, journalId=1146119893612605453, year='2026', volume='44', issue='1', pageStart='1', pageEnd='142', issueExtLink='null', onlineDate='null', pubDate='1768838400000', pubDateStr='2026-01-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790172046920, creator='13701087609', updateTime=1790231697005, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309889934790590725, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639744087229134, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309889934790590726, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639744087229134, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=122, endPage=130, ext={EN=ArticleExt(id=1309639759446770535, articleId=1309639759278998374, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=The cooperative scheduling model for frequency and power balancing in heterogeneous virtual power plants based on target cascade analysis, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Against the backdrop of global energy structure transformation and rapid development of renewable energy, how to achieve efficient aggregation and optimized scheduling of heterogeneous virtual power plants (VPPs) has become an important challenge. A heterogeneous VPPs frequency and power coordination scheduling model based on target cascade analysis method is proposed by improving the deep integration of clustering algorithm and control system. Introducing a learning mechanism to optimize the clustering process of photovoltaic battery energy storage systems (PVpBESSs) to meet the energy capacity requirements of a large number of power sources. A distributed control system for PVpBESSs was designed and validated on IEEE 13 node distributed photovoltaic power and energy storage systems. The experimental results show that compared with traditional scheduling methods, the scheduling efficiency has been improved by 18%. The model stabilizes the voltage deviation within 1% and maintains the frequency deviation at around 50 Hz; At the same time, the distribution of battery charging status is evenly maintained at 30%~90%, and efficient collaborative scheduling of photovoltaic and energy storage resources is achieved. Compared to methods that do not use learning mechanisms, the proposed model has strong practicality and potential for expansion, providing a new path for the practical application of virtual power plants.

, authors=Tao Wang¹, Jiangnan Li², Wenmeng Zhao¹, Mingyu Ou², Tian Mao¹, Jiru Jiang², Ye Guo³, authorsList=Tao Wang, Jiangnan Li, Wenmeng Zhao, Mingyu Ou, Tian Mao, Jiru Jiang, Ye Guo, 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=1309639759677457256, articleId=1309639759278998374, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=基于目标级联分析的异构虚拟电厂频率与电量协同调度模型, columnId=0, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

在全球能源结构转型与可再生能源快速发展的背景下，如何实现异构虚拟电厂的高效聚合与优化调度成为一项重要挑战。通过改进聚类算法与控制系统的深度集成，文章提出了一种基于目标级联分析法的异构虚拟电厂频率与电量协同调度模型。首先，引入学习机制优化光伏-电池储能系统的聚类过程，满足大量电源的能量容量需求；然后，设计了光伏-电池储能系统的分布式控制系统，并在IEEE 13节点分布式光伏电源与储能系统上进行了验证。实验结果表明，与传统调度方法相比，该模型调度效率提高了18%，电压偏差控制在1%以内，频率偏差维持在50 Hz左右；同时，电池充电状态的分布均衡地维持在30%~90%，实现了光伏与储能资源的高效协同调度。与未采用学习机制的方法相比，该模型具备较强的实用性和拓展潜力，为虚拟电厂的实际应用提供了新路径。

, authors=王滔¹, 李江南², 赵文猛¹, 欧鸣宇², 毛田¹, 蒋季儒², 郭烨³, authorsList=王滔, 李江南, 赵文猛, 欧鸣宇, 毛田, 蒋季儒, 郭烨, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=vtPTr2pNXq9EKgxJ/dNUHA==, magXml=85MOPfy591fEPbz5klTVyQ==, pdfUrl=null, pdf=nVo1pytRxvrSHC7CdLxNQw==, pdfFileSize=799049, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=tOTi6hMcsi7OXtrDS04pYA==, mapNumber=null, fund=null)}, authors=[Author(id=1309639760218522483, tenantId=1146029695717560320, journalId=1146119893612605453, articleId=1309639759278998374, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=wangtao@csg.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1309639761908826997, tenantId=1146029695717560320, journalId=1146119893612605453, articleId=1309639759278998374, authorId=1309639760218522483, language=EN, stringName=Tao Wang, firstName=Tao, middleName=null, lastName=Wang, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Electric Power Research Institute, China Southern Power Grid, Guangzhou 510530, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1309639762001101686, tenantId=1146029695717560320, journalId=1146119893612605453, articleId=1309639759278998374, authorId=1309639760218522483, language=CN, stringName=王滔, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．南方电网科学研究院有限责任公司，广东 广州　510530, bio={"content":"

王滔（1991-），男，硕士，工程师，研究方向为虚拟电厂、电力系统优化。E-mail: wangtao@csg.cn

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王滔（1991-），男，硕士，工程师，研究方向为虚拟电厂、电力系统优化。E-mail: wangtao@csg.cn

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