Article(id=1263533683051512136, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.02.010, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740412800000, receivedDateStr=2025-02-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779179505359, onlineDateStr=2026-05-19, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779179505359, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779179505359, creator=13701087609, updateTime=1779179505359, updator=13701087609, issue=Issue{id=1263533678655881533, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='2', pageStart='1', pageEnd='163', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1779179504312, creator=13701087609, updateTime=1779179563167, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263533925629047616, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263533925629047617, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=87, endPage=94, ext={EN=ArticleExt(id=1263533685366767962, articleId=1263533683051512136, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Optimized scheduling of port microgrids based on MOPSO-GRA algorithm, columnId=1240655021705065434, journalTitle=Navigation of China, columnName=Port and Waterway Engineering, runingTitle=null, highlight=null, articleAbstract=

With the advancement of global ports' green and low-carbon transformation, port microgrids, as key carriers for integrating high-penetration renewable energy, face the challenge of balancing heterogeneous optimization objectives in practical operation. Existing optimal scheduling methods based on the traditional Multi-Objective Particle Swarm Optimization (MOPSO) algorithm often rely on empirically determined conversion coefficients when coordinating economic and energy-consumption objectives. This approach suffers from strong subjectivity and lacks sufficient criteria for screening the Pareto solution set, making it difficult to consistently obtain globally optimal scheduling schemes. To address these issues, this paper proposes a method that introduces Grey Relational Analysis (GRA) into the traditional MOPSO algorithm to evaluate the Pareto solution set and thereby derive the optimal scheduling scheme. First, considering the high penetration of renewable energy and the source-load characteristics of port microgrids, a multi-objective optimization scheduling model is established, aiming to minimize comprehensive operational costs and maximize the local consumption rate of wind and solar power. Second, within the MOPSO framework, GRA is introduced as a decision-making tool to objectively evaluate the Pareto-optimal solution set generated during iterations, thereby accurately selecting the scheduling scheme with the best overall performance. The effectiveness of the proposed algorithm is verified using typical daily measured data from the Chuanshan Port microgrid demonstration project at Ningbo-Zhoushan Port. The results show that, compared to the scheduling algorithm based on traditional MOPSO, the proposed method significantly improves the consumption of renewable energy while maintaining system economic efficiency, achieving a 5.82% increase in the local consumption rate of wind and solar power and an approximately 9% reduction in the system's comprehensive operational costs, providing a feasible technical pathway for the effective utilization of high-density new energy in ports.

, correspAuthors=Hang Yu, 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, authorCompany=null, fund=null, authors=null, authorsList=Hang Yu, Wenlong Li, Xujing Tang, Hang Wu, Tian Wang, Wei Guo), CN=ArticleExt(id=1263533685987524962, articleId=1263533683051512136, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于MOPSO-GRA算法的港口微电网优化调度, columnId=1240655021902197734, journalTitle=中国航海, columnName=水运工程, runingTitle=null, highlight=null, articleAbstract=

随着全球港口绿色低碳转型的深入推进，港口微电网作为集成高比例可再生能源的关键载体，在其实际运行中面临异质优化目标难以兼顾的挑战。现有基于传统多目标粒子群算法的优化调度方法，在协调经济与消纳目标时，常依赖经验设定转化系数，存在主观性强、Pareto解集筛选依据不足的问题，难以稳定获得全局最优调度方案。针对上述问题，本文提出一种通过在传统多目标粒子群算法（MOPSO）中引入灰色关联度分析（GRA）算法对Pareto解集进行评价从而得到最优调度的方法。首先，基于港口微电网高新能源渗透率与源荷特性，构建了以最小化综合运行成本与最大化风光本地自消纳率为核心的多目标优化调度模型。其次，在MOPSO算法框架内，引入GRA分析作为决策层工具，对迭代产生的Pareto最优解集进行客观评价，从而准确地遴选出综合性能最优的调度方案。本文基于宁波舟山港穿山港区微电网示范工程典型日实测数据，对该算法的有效性进行验证，结果表明，相较于传统MOPSO的调度算法，所提算法在维持系统运行经济性的同时，显著提升了新能源消纳水平，其中风光本地自消纳率提高了5.82%，系统综合运行成本降低了约9%，为港口高密度新能源有效利用提供可行的技术路径。

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喻航（1991—），男，博士，研究方向为船舶新能源控制与优化调度。

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喻航（1991—），男，博士，研究方向为船舶新能源控制与优化调度。

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