Article(id=1221455971579253702, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221455967863095805, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202211260, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1669132800000, receivedDateStr=2022-11-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769147397179, onlineDateStr=2026-01-23, pubDate=1677254400000, pubDateStr=2023-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769147397179, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769147397179, creator=13701087609, updateTime=1769147397179, 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=126, endPage=135, ext={EN=ArticleExt(id=1221455971809940434, articleId=1221455971579253702, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Analysis on configuration optimization and operation of multi-unit coupled electric boilers based on dispatch requirements, columnId=1221455970081882683, journalTitle=Thermal Power Generation, columnName=Application of thermal energy storage technology, runingTitle=null, highlight=null, articleAbstract=

With the structural upgrading of China's energy industry and the transformation of economic growth mode, clean energy power generation technology has been continuously and rapidly developed. As a high-power electric heating conversion device, electric boiler has become one of the effective technical solutions to enhance the operational flexibility of thermal power units and promote the reliable consumption of new energy power. In this paper, the allocation and accounting principles of electric boiler capacity and heat storage capacity in such projects are proposed. The calculation model of system optimization operation parameters and operation economy evaluation is established based on the principle of maximizing the economic benefits of enterprises, taking into account the factors of grid peak shaving depth dispatching limits. Combined with the actual situation of the multi-unit system of a thermal power plant in Northeast China, the optimal configuration capacity of the electric boiler of the project is 300 MW, and the heat storage capacity is 17 MW·h. At the same time, the optimal operation scheme of the whole plant is proposed. The results show that, under the condition of 30% peak shaving depth limit, the case power plant can add about 139.53 million Yuan in each heating season, and the project investment payback period is about 5 years. In the foreseeable future, the project will have long-term profitability.

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电锅炉作为大功率电热转换装置，成为提升火电机组运行灵活性、促进新能源电力可靠消纳的有效技术方案之一。提出了此类项目中电锅炉容量、储热容量的配置核算原则，并以企业经济效益最大化为原则，考虑电网调峰深度调度限值因素，建立系统优化运行参数、运营经济性评价计算模型。结合东北地区某热电厂多机组系统的实际情况，综合分析得出了该项目最佳的电锅炉配置容量为300 MW，储热容量为17 MW·h，同时提出全厂优化运行方案。结果表明：案例电厂在30%调峰深度限值工况下，每个采暖季可新增调峰收益约13 953万元，项目投资回收期约5年；在可预见的未来，该项目将具备长期盈利能力。

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李明皓（1987），男，硕士，高级工程师，主要研究方向为储热系统设计与节能环保技术，liminghao@tpri.com.cn。

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李明皓（1987），男，硕士，高级工程师，主要研究方向为储热系统设计与节能环保技术，liminghao@tpri.com.cn。

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