Article(id=1223204293772165214, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222536, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1667577600000, receivedDateStr=2022-11-05, revisedDate=1670947200000, revisedDateStr=2022-12-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1769564229671, onlineDateStr=2026-01-28, pubDate=1684944000000, pubDateStr=2023-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769564229671, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769564229671, creator=13701087609, updateTime=1769564229671, updator=13701087609, issue=Issue{id=1223204286050452333, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='5', 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=1769564227831, creator=13701087609, updateTime=1769567742010, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223219026013323264, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223219026013323265, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=216, endPage=220, ext={EN=ArticleExt(id=1223204294187401345, articleId=1223204293772165214, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Research on Optimal Scheduling of Maximum Power Supply Capacity of Provincial Power Grid, columnId=1222940986234364233, journalTitle=Water Resources and Power, columnName=ELECTRICAL ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

After a high proportion of new energy is connected to the grid, the grid security and stability are significantly increased by multiple uncertainties. In order to quickly form a power adjustment plan to maintain the source-gridload dynamic balance under the scenario of power gap caused by large fluctuations of new energy, it is urgent to quantitatively evaluate the maximum power supply capacity of the grid. In this paper, the maximum power supply capacity and the minimum section margin of important sections are taken as the optimization objective functions, and the maximum power supply capacity assessment scenario of provincial power grids is modeled with power balance, slice reserve capacity, and section margin as the constraints, and the multi-stage constrained multi-objective evolution (CMOEA-MS) algorithm and the fast non-dominated ranking genetic algorithm NSGA-Ⅱ with elite strategy are used to solve the model, respectively. The quality of the solution sets of the two algorithms is evaluated in term of three indexes of convergence, uniformity and extensiveness of the solution sets. The simulation results of example show that the CMOEA-MS model has larger super volume values and better performance in solving the model, and can effectively improve the maximum power supply capacity of the provincial grid.

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高比例新能源接入电网后，电网安全稳定受多重不确定性因素的影响显著提升，为在新能源大幅度波动造成功率缺口的场景下快速形成功率调整方案以维持源—网—荷动态平衡，亟需量化评估电网的最大供电能力。为此，以供电能力最大、重要断面最小断面裕度最大作为优化目标函数，以电量平衡、片区备用容量、断面裕度作为约束条件建立省级电网最大供电能力评估场景模型，分别采用多阶段的约束多目标进化（CMOEA-MS）算法和带精英策略的快速非支配排序遗传算法NSGA-Ⅱ求解模型，并基于解集的收敛性、均匀性、广泛性3个指标对两种算法的解集质量进行评价。算例仿真结果表明，CMOEA-MS算法在求解所提模型时超体积值更大、性能更好，能有效提高省网的最大供电能力。

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黄牧涛（1973-），女，博士、研究员，研究方向为电力系统多能协调优化调度，E-mail：huangmutao@hust.edu.cn

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黄牧涛（1973-），女，博士、研究员，研究方向为电力系统多能协调优化调度，E-mail：huangmutao@hust.edu.cn

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