Article(id=1221455970077692809, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221455967863095805, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202209188, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1661961600000, receivedDateStr=2022-09-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769147396820, onlineDateStr=2026-01-23, pubDate=1677254400000, pubDateStr=2023-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769147396820, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769147396820, creator=13701087609, updateTime=1769147396820, updator=13701087609, issue=Issue{id=1221455967863095805, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='2', pageStart='1', pageEnd='161', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769147396292, creator=13701087609, updateTime=1769147501806, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1221456410462834874, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221455967863095805, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1221456410462834875, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221455967863095805, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=146, endPage=153, ext={EN=ArticleExt(id=1221455970723615642, articleId=1221455970077692809, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Design and performance analysis of deep peak shaving system of thermal power units coupled with molten salt heat storage, columnId=1221455970081882683, journalTitle=Thermal Power Generation, columnName=Application of thermal energy storage technology, runingTitle=null, highlight=null, articleAbstract=

In order to improve the flexibility of thermal power units, enhance the peak shaving capability of thermal power units coupled with molten salt heat storage system, and reduce project investment, a variety of electric heating molten salt energy storage systems coupled with thermal power plant were proposed. The thermal performance, peak shaving capacity and molten salt consumption of a 350 MW unit were analyzed with EBSILON software. An optimal system for deep peak shaving was proposed. The results show that, during the heat charging process, the electric heating system demonstrates a higher cycling efficiency as 33.2%, which enables the lowest power generation load to be reduced to below 25%. The flow rate of molten salt in this system is only 6.6%~31.2% to that of a steam heating system. During the heat discharging process, the water is drawn from the inlets of No.1 and No.2 high pressure heater. The water temperature is 182.4~242.7 ℃. The molten salt-condensed water heat exchanger is self-anti-condensation. The cycling efficiency during the heat discharging process is 32.7%~33.9% at different unit loads, indicating unit load has little effect on the cycling efficiency of the peaking shaving system. The research results can guide the engineering application of the deep peak shaving technology of thermal power units coupled with molten salt heat storage.

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为提高火电机组的运行灵活性，提高火电机组耦合熔盐储热系统的调峰能力，降低工程投资，引入了电加热熔盐储热方式，提出了多种火电机组耦合熔盐储热系统，基于EBSILON软件分析了某350 MW机组在不同耦合系统中的热力性能、调峰能力和熔盐用量，提出了最优的火电机组耦合熔盐储热深度调峰工艺系统。结果表明：储热过程电加热熔盐系统循环热效率为33.2%，机组最低发电负荷可降低至25%以下，单位调峰深度熔盐流量为抽汽蓄热系统的6.6%~31.2%；释热过程由1号和2号高压加热器入口混合取水，取水温度为182.4~242.7 ℃，熔盐-凝结水换热器具有自防凝功能；不同机组负荷下，释热过程循环热效率在32.7%~33.9%，机组负荷对调峰系统循环热效率影响不大。研究结果可指导火电机组耦合熔盐储热深度调峰技术的工程应用。

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邹小刚（1991），男，硕士，高级工程师，主要研究方向为新型储能技术，xiaogangz2008@163.com。

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邹小刚（1991），男，硕士，高级工程师，主要研究方向为新型储能技术，xiaogangz2008@163.com。

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