Article(id=1271501733713879252, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1271501633826530070, articleNumber=PA20260121_9nxFtqfP, orderNo=null, doi=10.19666/j.rlfd.202504083, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744041600000, receivedDateStr=2025-04-08, revisedDate=1750262400000, revisedDateStr=2025-06-19, acceptedDate=1750780800000, acceptedDateStr=2025-06-25, onlineDate=1781079236677, onlineDateStr=2026-06-10, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1781079236676, onlineIssueDateStr=2026-06-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1781079236676, creator=admin, updateTime=1781079236676, updator=admin, issue=Issue{id=1271501633826530070, tenantId=1146029695717560320, journalId=1210938733613449225, year='2026', volume='55', issue='1', pageStart='1', pageEnd='186', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=1, specialIssue=null, createTime=1781079212860, creator=ztmeta, updateTime=1781079304307, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1271502017525657824, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1271501633826530070, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1271502017529852129, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1271501633826530070, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=102, endPage=112, ext={EN=ArticleExt(id=1271501734951198935, articleId=1271501733713879252, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Optimization of equipment capacity in renewable energy hydrogen production park based on electrolyzer efficiency and cost model, columnId=null, journalTitle=Thermal Power Generation, columnName=null, runingTitle=null, highlight=null, articleAbstract=To address the intermittent and unstable power output issues in hydrogen production from renewable energy sources such as wind and solar power, it is crucial to achieve the optimal configuration of green power hydrogen production equipment. The discrete combinatorial optimization algorithms and multi-objective shuffled frog leaping algorithms are study introduced to conduct optimization research on the planning of parks with pure photovoltaic, pure wind power, and photovoltaic-wind power hybrid systems for renewable energy generation. Models of electrolyzer system efficiency, operating power, cost, and capacity are constructed. The results show that in a hybrid system with a photovoltaic capacity of 2.60 MW and a wind power capacity of 3.80 MW, the lowest hydrogen levelized cost is 17.83 yuan/kg, and the full-load operating hours of the electrolyzer are approximately 3 400 hours. After optimization by the multi-objective shuffled frog leaping algorithm, the optimal configuration is a photovoltaic capacity of 1.50 MW and a wind power capacity of 0.55 MW, with a maximum hydrogen production of 2 949.62 kg. The photovoltaic-wind power hybrid system can not only reduce the hydrogen levelized cost but also increase the full-load operating time, providing a theoretical reference for the scientific planning of hydrogen production from renewable energy in the future., 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, authorCompany=null, fund=null, authors=null, authorsList=null), CN=ArticleExt(id=1271501734875701462, articleId=1271501733713879252, tenantId=1146029695717560320, journalId=1210938733613449225, language=CN, title=基于电解槽效率和成本模型的可再生能源制氢园区设备容量优化, columnId=null, journalTitle=热力发电, columnName=null, runingTitle=null, highlight=null, articleAbstract=为了解决风、光等可再生能源发电制氢中电力输出的间歇性与不稳定性问题，实现绿电制氢设备的最优配置非常重要。研究引入离散组合优化算法与多目标蛙跳优化算法，针对纯光伏、纯风电和光伏-风电混合系统可再生能源发电的园区规划展开优化，构建电解槽系统效率与运行功率、成本与容量的模型。结果显示：在光伏容量2.60 MW和风电容量3.80 MW的混合系统中，氢平准化成本最低为17.83元/kg，电解槽满负荷小时数约3 400 h；经多目标蛙跳优化算法优化后，最优配置为光伏容量1.50 MW、风电容量0.55 MW，其最大制氢量2 949.62 kg。光伏-风电混合系统既能降低氢平准化成本，又能增加满负荷运行时间，可为未来可再生能源制氢的科学规划提供理论参考。, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, 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