Article(id=1295068227558724424, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1295068070071005445, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202510022, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760371200000, receivedDateStr=2025-10-14, revisedDate=1761840000000, revisedDateStr=2025-10-31, acceptedDate=1763395200000, acceptedDateStr=2025-11-18, onlineDate=1786697926654, onlineDateStr=2026-08-14, pubDate=1779638400000, pubDateStr=2026-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1786697926654, onlineIssueDateStr=2026-08-14, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1786697926654, creator=13701087609, updateTime=1786697926654, updator=13701087609, issue=Issue{id=1295068070071005445, tenantId=1146029695717560320, journalId=1210938733613449225, year='2026', volume='55', issue='5', pageStart='1', pageEnd='186', issueExtLink='null', onlineDate='null', pubDate='1779638400000', pubDateStr='2026-05-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1786697889106, creator='13701087609', updateTime=1786698835709, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1295072040462078420, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1295068070071005445, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1295072040462078421, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1295068070071005445, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=120, endPage=128, ext={EN=ArticleExt(id=1295068227768439625, articleId=1295068227558724424, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Peak shaving and economic performance of nuclear power unit combined with seawater desalination, columnId=1295068110525067814, journalTitle=Thermal Power Generation, columnName=Low-carbon thermal power and nuclear power generation technology, runingTitle=null, highlight=null, articleAbstract=

The operation of nuclear power combined with seawater desalination can enhance the operational flexibility of nuclear power plants, but its peak shaving performance and economy still need in-depth research. Therefore, taking the AP1000 nuclear power unit combined with the multi-effect distillation seawater desalination system as the research object, a thermodynamic- economic coupling simulation model of the system was established to analyze the peak shaving performance and economy of the system, and a seasonal differentiated operation strategy was proposed. The results show that the maximum peak shaving depth of the nuclear power unit after combined seawater desalination is 878.5 MW. Compared with independently operating nuclear power units, during the non-heating season, the strategy of “prioritizing output and then adjusting seawater desalination” was adopted. The number of operating condition switches increased from 4 to 6 times, forming a “electricity price dominance-dual-energy matching” model. The net income increased from 3.598 million yuan to 7.869 million yuan. During the heating season, the strategy of “prioritizing heating and then adjusting seawater desalination” was adopted. The working condition switching remained unchanged for 8 times, forming a coordinated production mode of “electricity-heat-water”. The net income increased from 3.045 million yuan to 6.835 million yuan, and the income structure was balanced. The operation of nuclear power plants combined with seawater desalination and the implementation of differentiated peak shaving strategies by season can simultaneously enhance the peak shaving capacity and economic benefits of nuclear power plants.

, authors=Kai LIANG1, Lingkai ZHU1, Han YUE2, Wei ZHENG1, Zhiqiang GONG1, Heng ZHANG2, 3, Ziwei ZHONG1, Panfeng SHANG1, Jiguang HUANG2, 3, authorsList=Kai LIANG, Lingkai ZHU, Han YUE, Wei ZHENG, Zhiqiang GONG, Heng ZHANG, Ziwei ZHONG, Panfeng SHANG, Jiguang HUANG, authorCompany=null, correspAuthors=Heng ZHANG, 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, fund=null), CN=ArticleExt(id=1295068231727862624, articleId=1295068227558724424, tenantId=1146029695717560320, journalId=1210938733613449225, language=CN, title=核电机组联合海水淡化调峰性能与经济性, columnId=1295068110713811496, journalTitle=热力发电, columnName=低碳火电及核电技术, runingTitle=null, highlight=null, articleAbstract=

核电机组联合海水淡化运行可增强核电站运行灵活性,其调峰性能及经济性有待深入研究。为此,以AP1000核电机组联合多效蒸馏海水淡化系统为研究对象,建立系统热力学-经济性耦合仿真模型,分析系统调峰性能及其经济性,并提出季节差异化运行策略。结果表明,核电机组联合海水淡化后的调峰深度最大为878.5 MW。相比独立运行核电机组,非供暖季采用“优先出力再调海水淡化”策略,工况切换次数从4次增至6次,形成“电价主导-双能适配”模式,系统净收益从359.8万元提升至786.4万元;供暖季采用“优先供暖再调海水淡化”策略,工况切换维持8次不变,形成“电-热-水”协同生产模式,系统净收益从304.5万元升至683.5万元且收益结构均衡。表明该运行策略可同时提升核电站调峰能力与经济效益。

, authors=梁凯1, 祝令凯1, 岳涵2, 郑威1, 巩志强1, 张衡2, 3, 钟子威1, 商攀峰1, 黄吉光2, 3, authorsList=梁凯, 祝令凯, 岳涵, 郑威, 巩志强, 张衡, 钟子威, 商攀峰, 黄吉光, authorCompany=null, correspAuthors=张衡, authorNote=

梁凯(1996),男,博士,工程师,主要研究方向为能源系统优化,

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张衡(1991),男,博士,副教授,主要研究方向为能源系统节能与优化,
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梁凯(1996),男,博士,工程师,主要研究方向为能源系统优化,

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tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068227558724424, language=CN, label=图11, caption=12月供暖海水淡化运行场景下机组出力情况, figureFileSmall=0UgLyE32qNoQDl++L3NQ2g==, figureFileBig=JXhGYEveCVOJR/+fMJBRYg==, tableContent=null), ArticleFig(id=1295068240116470713, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068227558724424, language=EN, label=Tab.1, caption=

Design parameters of AP1000 unit under 100%Pe THA condition

, figureFileSmall=null, figureFileBig=null, tableContent=
序号蒸汽压力/MPa温度/℃流量/(t·h–1
1主蒸汽5.380268.66 799.0
21级抽汽3.290239.0324.5
32级抽汽2.850231.1391.0
43级抽汽1.730205.1333.3
54级抽汽0.981179.0420.9
6高压缸排汽0.981179.1324.5
7低压缸进汽0.932257.04 510.7
85级抽汽0.370161.6223.0
96级抽汽0.16386.4164.5
107级抽汽0.03270.9317.7
118级抽汽0.01351.7994.6
), ArticleFig(id=1295068240196162490, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068227558724424, language=CN, label=表1, caption=

AP1000机组100%PeTHA工况设计参数

, figureFileSmall=null, figureFileBig=null, tableContent=
序号蒸汽压力/MPa温度/℃流量/(t·h–1
1主蒸汽5.380268.66 799.0
21级抽汽3.290239.0324.5
32级抽汽2.850231.1391.0
43级抽汽1.730205.1333.3
54级抽汽0.981179.0420.9
6高压缸排汽0.981179.1324.5
7低压缸进汽0.932257.04 510.7
85级抽汽0.370161.6223.0
96级抽汽0.16386.4164.5
107级抽汽0.03270.9317.7
118级抽汽0.01351.7994.6
), ArticleFig(id=1295068240301020091, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068227558724424, language=EN, label=Tab.2, caption=

Calculation errors under different working conditions

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工况设计参数计算结果相对误差/%
功率/MW主蒸汽功率/MW主蒸汽功率主蒸汽
压力/MPa温度/℃压力/MPa温度/℃压力温度
100%Pe THA1 253.005.38268.6001 253.845.39268.560.070.18-0.01
90%Pe THA1 127.705.59271.0001 127.635.59271.10-0.0100.04
75%Pe THA939.755.90274.500940.755.90274.400.110-0.04
50%Pe THA626.506.39279.726626.986.38279.730.08-0.160
40%Pe THA501.206.58281.673501.456.56281.670.05-0.300
30%Pe THA375.906.78283.677375.346.75283.68-0.15-0.440
), ArticleFig(id=1295068240376517564, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068227558724424, language=CN, label=表2, caption=

不同工况计算误差

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工况设计参数计算结果相对误差/%
功率/MW主蒸汽功率/MW主蒸汽功率主蒸汽
压力/MPa温度/℃压力/MPa温度/℃压力温度
100%Pe THA1 253.005.38268.6001 253.845.39268.560.070.18-0.01
90%Pe THA1 127.705.59271.0001 127.635.59271.10-0.0100.04
75%Pe THA939.755.90274.500940.755.90274.400.110-0.04
50%Pe THA626.506.39279.726626.986.38279.730.08-0.160
40%Pe THA501.206.58281.673501.456.56281.670.05-0.300
30%Pe THA375.906.78283.677375.346.75283.68-0.15-0.440
), ArticleFig(id=1295068240435237821, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068227558724424, language=EN, label=Tab.3, caption=

Key parameters setting

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序号关键参数数值
1单位发电成本cp/(元·(kW·h)–11.5
2固定成本b/(万元·h–11
3海水淡化效率ηw6.86
4淡水售价rw/(元·t–13.2(参考山东某市)
), ArticleFig(id=1295068240569455550, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068227558724424, language=CN, label=表3, caption=

关键参数设置

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序号关键参数数值
1单位发电成本cp/(元·(kW·h)–11.5
2固定成本b/(万元·h–11
3海水淡化效率ηw6.86
4淡水售价rw/(元·t–13.2(参考山东某市)
), ArticleFig(id=1295068240636564415, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068227558724424, language=EN, label=Tab.4, caption=

Peak shaving performance and synergistic benefits in different scenarios

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场景切换次数收益/万元
6月基准运行4359.8
6月海水淡化运行6786.4
12月基准运行10304.5
12月海水淡化运行8423.9
12月供暖海水淡化运行8683.5
), ArticleFig(id=1295068240825308096, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068227558724424, language=CN, label=表4, caption=

不同场景下调峰性能与协同效益

, figureFileSmall=null, figureFileBig=null, tableContent=
场景切换次数收益/万元
6月基准运行4359.8
6月海水淡化运行6786.4
12月基准运行10304.5
12月海水淡化运行8423.9
12月供暖海水淡化运行8683.5
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核电机组联合海水淡化调峰性能与经济性
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梁凯 1 , 祝令凯 1 , 岳涵 2 , 郑威 1 , 巩志强 1 , 张衡 2, 3 , 钟子威 1 , 商攀峰 1 , 黄吉光 2, 3
热力发电 | 低碳火电及核电技术 2026,55(5): 120-128
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热力发电 |低碳火电及核电技术 2026 , 55 (5) : 120 -128
核电机组联合海水淡化调峰性能与经济性
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梁凯1 , 祝令凯1, 岳涵2, 郑威1, 巩志强1, 张衡2, 3 , 钟子威1, 商攀峰1, 黄吉光2, 3
作者信息
  • 1.国网山东省电力公司电力科学研究院,山东 济南 250013
  • 2.华北电力大学能源动力与机械工程学院,北京 102206
  • 3.华北电力大学热电生产过程污染物监测与控制北京市重点实验室,北京 102206
通讯作者:
张衡(1991),男,博士,副教授,主要研究方向为能源系统节能与优化,
作者简介:

梁凯(1996),男,博士,工程师,主要研究方向为能源系统优化,

Peak shaving and economic performance of nuclear power unit combined with seawater desalination
Kai LIANG1 , Lingkai ZHU1, Han YUE2, Wei ZHENG1, Zhiqiang GONG1, Heng ZHANG2, 3 , Ziwei ZHONG1, Panfeng SHANG1, Jiguang HUANG2, 3
Affiliations
  • 1.State Grid Shandong Electric Power Company Electric Power Science Research Institute, Jinan 250013, China
  • 2.School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
  • 3.Beijing Key Laboratory of Pollutant Monitoring and Control in Thermoelectric Production Process, North China Electric Power University, Beijing 102206, China
出版时间: 2026-05-25 doi: 10.19666/j.rlfd.202510022
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核电机组联合海水淡化运行可增强核电站运行灵活性,其调峰性能及经济性有待深入研究。为此,以AP1000核电机组联合多效蒸馏海水淡化系统为研究对象,建立系统热力学-经济性耦合仿真模型,分析系统调峰性能及其经济性,并提出季节差异化运行策略。结果表明,核电机组联合海水淡化后的调峰深度最大为878.5 MW。相比独立运行核电机组,非供暖季采用“优先出力再调海水淡化”策略,工况切换次数从4次增至6次,形成“电价主导-双能适配”模式,系统净收益从359.8万元提升至786.4万元;供暖季采用“优先供暖再调海水淡化”策略,工况切换维持8次不变,形成“电-热-水”协同生产模式,系统净收益从304.5万元升至683.5万元且收益结构均衡。表明该运行策略可同时提升核电站调峰能力与经济效益。

AP1000  /  核电机组  /  热电联产  /  海水淡化  /  调峰

The operation of nuclear power combined with seawater desalination can enhance the operational flexibility of nuclear power plants, but its peak shaving performance and economy still need in-depth research. Therefore, taking the AP1000 nuclear power unit combined with the multi-effect distillation seawater desalination system as the research object, a thermodynamic- economic coupling simulation model of the system was established to analyze the peak shaving performance and economy of the system, and a seasonal differentiated operation strategy was proposed. The results show that the maximum peak shaving depth of the nuclear power unit after combined seawater desalination is 878.5 MW. Compared with independently operating nuclear power units, during the non-heating season, the strategy of “prioritizing output and then adjusting seawater desalination” was adopted. The number of operating condition switches increased from 4 to 6 times, forming a “electricity price dominance-dual-energy matching” model. The net income increased from 3.598 million yuan to 7.869 million yuan. During the heating season, the strategy of “prioritizing heating and then adjusting seawater desalination” was adopted. The working condition switching remained unchanged for 8 times, forming a coordinated production mode of “electricity-heat-water”. The net income increased from 3.045 million yuan to 6.835 million yuan, and the income structure was balanced. The operation of nuclear power plants combined with seawater desalination and the implementation of differentiated peak shaving strategies by season can simultaneously enhance the peak shaving capacity and economic benefits of nuclear power plants.

AP1000  /  nuclear power unit  /  heat and power cogeneration  /  seawater desalination  /  peak shaving
梁凯, 祝令凯, 岳涵, 郑威, 巩志强, 张衡, 钟子威, 商攀峰, 黄吉光. 核电机组联合海水淡化调峰性能与经济性. 热力发电, 2026 , 55 (5) : 120 -128 . DOI: 10.19666/j.rlfd.202510022
Kai LIANG, Lingkai ZHU, Han YUE, Wei ZHENG, Zhiqiang GONG, Heng ZHANG, Ziwei ZHONG, Panfeng SHANG, Jiguang HUANG. Peak shaving and economic performance of nuclear power unit combined with seawater desalination[J]. Thermal Power Generation, 2026 , 55 (5) : 120 -128 . DOI: 10.19666/j.rlfd.202510022
在能源可持续发展和应对气候变化的要求下,新能源产业发展迅速[1]。自“碳达峰、碳中和”目标提出以来,我国新能源装机占比持续提升[2]。然而,风能和太阳能等新能源的波动性和间歇性,给电力系统稳定运行带来巨大挑战。新能源大规模并网背景下,一些地区被迫“弃风”“弃光”,如何消纳多余的新能源电力成为一个重要课题[3]。核能是一种可大规模替代化石燃料的稳定零碳基荷能源之一,但存在负荷跟随能力有限、难以应对频繁变负荷等问题[4]。同时,核电的平均发电成本较高,在电力市场环境下易受电价波动影响[5]。为此,有必要对核电机组的调峰技术进行研究,提升核电灵活性。
目前,核电机组运行灵活性提升可采用联合储能、储热[6]、制氢、海水淡化[7-8]运行的方式。丁煊涛等[9]系统性分析了储热、机械储能、电化学储能等技术与核电联合的模式。Li等人[10]将核电站与低温储热技术集成,使核电站的输出功率比降低至31%。Sui等人[11]提出一种带储热的核电调度策略,可使核电站的削峰深度达到33%。Chen等人[12]提出核电站、海水淡化和电解槽的集成系统来实现削峰,可产生37.86 MW的输出氢能,系统效率为37.44%。我国淡水资源并不丰富且核电站大多沿海建造,核能联合海水淡化运行具有地理优势同时经济效益可观。因此,核电联合海水淡化调峰技术有望成为提升核电站运行灵活性的经济有效方案。
当前主流的海水淡化技术包括多效蒸馏(multi-effect distillation,MED)[13]、多级闪蒸(multi-stag flash,MSF)[14]和反渗透(reverse osmosis,RO)[15]等。Kim等人[16]提出高温气冷堆与MED系统联合运行方案,并采用高效换热器对系统进行优化,可提高产水量258%,降低淡化水成本9%。Ghazaie等人[17]针对小型模块反应堆,提出了使用循环冷却水作为海水淡化供水的方案,相比直接采用海水可使淡化总成本降低约6.5%~7.5%。Wu等人[18]分别采用核电机组二回路的主蒸汽、入口再热蒸汽和出口再热蒸汽作为热源与MED海水淡化联合运行,集成系统的输出功率比可分别降低至12%、65%和61%。Naserbegi等人[19]将核电站联合MED海水淡化系统与塔式太阳能电站结合,提高海水淡化蒸汽的温度,太阳能用于核蒸汽再加热和提供海水淡化系统所需的热量,优化后核电站的总功率和效率分别从47 MW和27%提高到70 MW和30%。目前核能联合海水淡化运行相关研究主要关注系统耦合方式、调峰深度等,在现阶段我国电力市场改革的背景下,核能联合海水淡化调峰及其经济性仍待进一步研究。
基于此,本文以AP1000核电机组联合MED海水淡化系统为对象展开研究,旨在探明核电机组联合海水淡化运行在现货市场背景下的运行特征与经济性,以期为核电机组灵活性提升与多元化经营提供路线参考和理论支撑。研究考虑设备维护、运行等成本及供电、供热、淡水收益,构建热力学-经济性耦合仿真模型,分析AP1000联合MED海水淡化运行的调峰性能与经济性,并提出季节差异化运行策略。
当前大型核电机组主要涵盖压水堆、气冷堆、沸水堆等堆型,其中压水堆在我国应用最为广泛。本文以第三代压水堆AP1000核电机组为例展开研究。AP1000核电机组为纯凝气式压水堆机组,额定功率1 253 MW,该机组相关设计参数见表1
AP1000热电联产联合MED海水淡化系统如图1所示,汽轮机采用“1台高压缸+3台低压缸”结构,无调节级且为六排汽型式;回热加热系统包含2台高压加热器、4台低压加热器及1台除氧器。高压缸与低压缸之间设汽水分离再热器,可分离高压缸排汽中液态水,并将分离后的饱和蒸汽再热;再热后的蒸汽继续进入低压缸膨胀做功。
在核电厂冬季供暖场景中,系统从低压缸入口抽取蒸汽作为供暖热源,实现热电联产。该抽汽方案具备两方面优势:1)蒸汽参数可匹配供暖换热需求,无需额外增设参数调节设备;2)低压缸入口抽汽改造难度低,且允许抽汽量能满足大规模供暖需求。具体流程为:抽取的蒸汽首先进入供暖换热器,与供暖循环水完成热量传递后凝结,凝结水经处理后返回核电机组汽水系统,保障二回路工质平衡;被加热的供暖循环水则通过二级换热站,向居民及工业场所输送热量,实现对外供暖功能。
在供暖功能基础上,核电厂二回路联合MED海水淡化系统,形成电-热-水联产。海水淡化系统同样以低压缸入口抽汽为热源,抽汽先进入背压式汽轮机做功发电,做功后的乏汽作为中间回路热源,在中间回路换热器中加热回路给水,完成换热后乏汽返回二回路。被加热的中间回路给水进入闪蒸罐,经减温减压处理,生成参数为69.1 ℃、0.03 MPa的蒸汽,作为MED首效蒸发器的热源蒸汽。蒸汽凝结后,与闪蒸罐排水一同流入凝结水箱,经供给泵再次送入中间回路换热器,维持循环。海水则依次通过淡化水预热器、浓盐水预热器、冷凝器预热,随后作为进料海水,平行进入各效蒸发器参与淡化过程,剩余海水直接排放入海。
本研究考虑核电机组联合海水淡化运行的夏季无供暖和冬季供暖工况,供暖时低压缸入口抽汽需同时满足供暖与海水淡化需求,通过流量分配调控,优先保障供暖负荷,剩余抽汽量用于海水淡化,实现电-热-水联产。
采用Ebsilon软件搭建AP1000核电机组联合MED海水淡化系统仿真模型。首先建立AP1000机组模型,100%Pe THA工况仿真模型如图2所示。选取机组30%Pe~100%Pe THA范围6个运行工况进行验证,仿真计算结果与设计参数对比见表2,其中最大相对误差仅为0.44%,表明模型计算精度较高,可用于后续模拟研究。
搭建的AP1000热电联产联合MED海水淡化系统仿真模型如图3所示。以核电低压缸抽汽为热源,分别进入供暖系统(用于居民/工业供热)和海水淡化系统(驱动MED多效蒸馏制水)。研究采用技术成熟且能效优越的12效MED系统,系统以“蒸汽梯级利用”为核心设计理念,整体由12个串联的蒸发器单元、预热器、冷凝器及相关辅助设备(如海水进料泵、淡水收集罐、浓盐水排放泵)构成。
本文研究核电机组联合海水淡化的最优运行策略及经济性,在满足机组运行约束与市场规则的前提下实现收益最大化。采用已发布的山东省2024年电力现货市场平均电价曲线(图4),价格低谷期均出现在午时,形成午间低谷的主要原因是山东电网光伏装机占比较高,6月因夏季高温用电多,其电价峰值高于12月。构建了混合整数规划模型,模型核心在于确定各时段的运行工况、出力及抽汽量,平衡市场收益与运行成本,并考虑工况切换限制与连续运行要求。采用ε约束法求解多目标优化问题(具体是实现收益最大化和切换次数最小化,平衡经济性与稳定性),通过参数切换次数K(1~15次)生成帕累托解集。筛选非支配解时,保留收益更高且切换次数更少的方案,最终通过二进制变量阈值(δt,w>0.9)确定各时段最优工况。
max Profit=t=1T[λtPt+rwηwStcpPtb]
式中:λt为时段t现货电价,元/(MW·h);Pt为机组出力,MW;rw为淡水售价;ηw为海水淡化效率;St为抽汽量,t/h;cp为单位发电成本,元/(kW·h);b为每时段固定成本,元/h,指核电机组联合海水淡化系统在单位时间内(1 h)固定发生、与实时出力调整、抽汽量变化及淡水产量波动无关的成本总和。表3列举了关键参数设置。
1)功率约束
核电出力须处于当前工况的最小功率和最大功率之间:
Pt,min(w)PtPt,max(w),tT,wW
式中:Pt,max为当前工况下最大功率,MW;Pt,min为当前工况下最小功率,MW。
2)连续时间约束
核电满功率运行持续时间为6 h,通过二进制变量γt,100表征连续运行状态;最小功率运行持续时间为4 h,通过二进制变量γt,50表征连续运行状态:
i=05δt+i,100%6γt,100,t[1,T5]
δt,100%s=max(1,t5)min(t,T5)γs,100,tT
i=03δt+i,50%4γt,50,t[1,T3]
γ10,50δ10,50%
式中:δti%为二进制状态变量(取0或1),用于标识核电机组在时段t是否处于i%额定功率工况。
3)线性关系约束
抽汽流量和核电出力之间存在线性关系:
StPtbt,cat,c+M(1Selt,wc)
StPtbt,cat,cM(1Selt,wc)
式中:at,cbt,c为抽汽流量与核电出力线性关系的系数,用于量化二者线性关联;M为一个足够大的正数(大M法),用于在逻辑变量控制下实现约束的激活或松弛,确保模型在不同工况下约束的有效性;Selt,wc为二进制逻辑变量(取值为0或1),用于标识时段t是否选择“抽汽-出力线性关联模式”。
抽汽流量不能超出当前工况的最大允许抽汽流量:
Stmaxs+M(1Selt,wc)
4)切换次数约束
切换次数增加会增加机组损耗,所以限制切换次数,通过二进制变量σt统计相邻时段的工况切换次数,限制总切换次数不超过K次:
i=1IσiK,σi={1,δi,wδi1,w,wW0,否则
本研究的整体逻辑框架如图5所示,依托Ebsilon软件对热力系统建模,依次进行经济性分析建模、调峰运行特性研究以及供暖季与非供暖季策略分析,进而对比不同场景下的经济性。
为科学评估核电联合海水淡化系统在不同运行场景(非供暖季与供暖季)及配置模式(有无海水淡化、有无供暖)下的综合性能,从调峰能力和经济性进行系统评价。
1)调峰深度
系统出力调节的最大范围计算公式为:
P=Pmax,tPmin,t
式中:Pmax,tPmin,t分别为为单日内机组最大和最小出力,MW。调峰深度越大,说明系统覆盖负荷区间越广,应对峰谷差的能力越强。
2)造水比
系统造水比GOR适用于海水淡化系统评价[20]
GOR=GD
式中:G为产品淡水流量,t/h;D为蒸汽流量,t/h。
图6为AP1000联合MED海水淡化系统的发电功率与海水淡化产量的关系。图6中线ACBD分别表示核电机组在100%Pe和30%Pe THA运行条件下的发电量,AB表示没有海水淡化系统机组的峰值调峰能力,面积ABCD表示当前核电机组的调峰能力。由图6可见,随着产水量的增加,机组的发电量减少,但最初的调峰能力基本保持不变。当产水量为3 653.8 t/h,即抽汽量为500.6 t/h时,峰值调峰能力可达878.5 MW。当产水量足够大,使低压汽轮机的乏汽流量达到最小允许值后,机组的峰值调峰能力急剧下降。
以非供暖季的6月运行场景为例进行分析,具体包括:1)6月基准运行,代表核电机组独立运行的传统调峰模式;2)6月海水淡化运行,代表核电机组联合海水淡化系统的“电-水协同”多能调峰模式。
图7为6月基准运行场景下核电机组出力情况。由图7可见,6月基准运行时,系统以满足基础电力负荷为核心,工况切换次数仅4次。100%Pe工况凭借高功率输出优势,在电价相对平稳或负荷需求较高的时段(如00:00—05:00、15:00—23:00)持续运行,依托其稳定的额定出力1 253.8 MW保障电力供应;30%Pe工况则适配电价低谷、负荷需求低迷时段(如06:00—14:00),以375.3 MW额定出力维持基础运行,形成高负荷工况保供电稳定、低负荷工况适配低谷需求的基础调节模式,出力调节围绕工况额定值小幅波动,体现出较为单一的电力供应导向,面对负荷突发波动,缺乏灵活调整空间。
图8为6月海水淡化运行场景下机组出力情况。由图8可见,核电联合海水淡化系统可兼顾电力调峰与水资源生产双重目标,工况切换次数增至6次,但可以通过抽汽用于海水淡化实现出力与负荷的更高精度匹配。高电价时段(如00:00—05:00、15:00—23:00),100%Pe工况持续运行,出力不再局限于额定值,而是精准匹配负荷需求(如00:00时刻出力1 077.6 MW、01:00时刻出力1 058.3 MW),通过抽汽供给海水淡化实现动态调整出力,分别抽汽951.3、1 050.0 t,产出淡水6 409.1、7 074.1 t;低电价时段(如06:00—14:00),30%Pe、75%Pe、90%Pe等工况灵活切换,出力在300.7~868.4 MW区间动态调整(如06:00时刻出力382.8 MW、10:00时刻出力868.4 MW),既适配低谷电力需求,又通过抽汽量优化(如09:00时刻抽汽量550.6 t/h、11:00时刻抽汽量1 085.4 t/h)保障海水淡化连续运行,构建起“电-水协同”的动态调节机制,实现能量在多需求场景下的合理分配。
无海水淡化的6月基准运行场景下,核电机组独立运行调峰,虽能通过工况切换(实际切换次数4次)与机组出力调整(375.3~1 253.8 MW)适配负荷需求波动,但因缺乏多能协同,能源梯级利用效率受限,总净收益仅359.8万元。
引入海水淡化后,调峰拓展至“电力-水资源”协同维度,抽汽量动态调配提升能源梯级利用效率。高电价时段,100%Pe工况抽汽适配海水淡化高需求(18:00时刻抽汽831.1 t,产水量5 599.3 t),电力收益与水资源效益协同增长;低电价时段,低负荷工况配合抽汽(09:00时刻抽汽550.6 t,产水量3 758.5 t),能满足基础用电同时维持海水淡化运行,总净收益提升至786.4万元,较基准场景增加1倍以上。
以供暖季的12月运行场景为例进行分析,具体包括:1)12月基准运行,代表核电机组独立运行的传统调峰模式;2)12月海水淡化运行,代表核电联合海水淡化系统的“电-水协同”多能调峰模式;3)12月供暖海水淡化运行,代表核电联合海水淡化系统的“电-热-水协同”多能调峰模式。对于12月海水淡化运行和12月供暖海水淡化运行2个场景,系统需兼顾调峰和供暖,优化策略为优先保障供暖,再调节海水淡化负荷。
供暖季(以12月为典型场景)中,核电联合海水淡化系统需兼顾电力调峰、海水淡化及供暖需求,工况切换与出力调节呈现多元目标驱动特征。图9为12月基准运行场景下机组出力情况。由图9可见,无海水淡化、无供暖时,系统工况切换次数达10次,工况选择分散(涵盖30%Pe~100%Pe工况),如00:00—05:00时段在50%Pe、90%Pe工况间切换,10:00—14:00时段以30%Pe、40%Pe工况适配低谷负荷,出力围绕各工况额定值(627.0、1 127.6 MW等)波动,以单纯电力调峰为核心,调节逻辑受电价、负荷双重影响但场景单一。
图10为12月海水淡化运行场景下机组出力情况。由图10可见,引入海水淡化后,工况切换次数降至8次,调节逻辑向“电力-水资源”协同倾斜。
高电价、高负荷时段(如15:00—23:00),100%Pe工况持续运行,出力动态适配负荷(如16:00时刻出力1 078.4 MW、17:00时刻出力1 139.0 MW),并通过抽汽量优化(15:00时刻抽汽1 403.0 t、16:00时刻抽汽947.2 t)保障海水淡化运行;低电价、低负荷时段(如00:00—09:00),75%Pe、90%Pe工况灵活切换,出力在723.3~988.5 MW区间调整(如00:00、02:00时刻出力分别为723.3、988.5 MW),协同抽汽需求(00:00、02:00时刻抽汽分别为1 085.4、761.0 t),构建“电力供应-海水淡化”双需求调节机制。
图11为12月供暖海水淡化运行场景下机组出力情况。冬季供暖运行场景下,系统需平衡电力、水资源与热能供应,工况切换维持8次,但调节逻辑进一步复杂化。
图11可见,多能联合下,高需求时段(如00:00—09:00),75%Pe、90%Pe工况成为主力,出力在732.2~987.5 MW动态调整(如00:00时刻出力732.2 MW、07:00时刻出力987.5 MW),抽汽量拆分为供暖与海水淡化两部分(00:00时刻供暖抽汽737.0 t、海水淡化抽汽352.0 t),实现“电力-供暖-水资源”协同;高电价时段(15:00—23:00),100%Pe工况持续运行,出力适配负荷同时(15:00时刻出力1 008.9 MW、16:00时刻出力1 025.9 MW),抽汽优先保障供暖,再协同海水淡化需求(15:00时刻供暖抽汽814.0 t、海水淡化抽汽438.8 t),形成多能互补的调峰模式。
无海水淡化、无供暖时,系统调峰聚焦电力维度,虽工况切换频繁(10次),但因缺乏多能协同,能源利用效率有限,调峰深度达878.5 MW,但收益仅304.5万元,对市场收益贡献单一。
引入海水淡化后,调峰拓展至“电力-水资源”协同维度,抽汽量动态调配提升能源梯级利用效率。高电价时段,100%Pe工况抽汽适配海水淡化高需求(18:00时刻抽汽903.7 t),电力收益与水资源效益协同增长;低电价时段,低负荷工况配合抽汽(09:00时刻抽汽1 275.5 t),满足基础用电同时维持海水淡化运行,总净收益提升至423.9万元,调峰收益维度拓宽。调峰深度因海水淡化抽汽对出力的柔性约束,调整为1 253.8 MW-331.8 MW=922.0 MW(12:00时刻),抽汽量分配适配性增强。
供暖运行场景下,多能协同效益进一步凸显。系统通过抽汽量拆分(供暖与海水淡化),在满足电力调峰(14:00时,最大出力1 253.8 MW-最小出力289.34 MW)的同时,保障热能与水资源稳定供应。总净收益跃升至683.5万元,较仅海水淡化场景提升61.2%,验证了多能联合对调峰收益的增益。
非供暖季与供暖季在工况切换和调节目标上差异明显。非供暖季以“电力-水资源”协同为核心,无海水淡化时工况切换4次,引入海水淡化后增至6次,切换主要受电价与电力负荷驱动,100%Pe工况出力在969.7~1 253.8 MW波动,抽汽量随电价灵活调整,形成“电价主导-双能适配”模式;供暖季需叠加热能需求,无海水淡化无供暖时切换10次,引入海水淡化及供暖后维持8次,工况选择受“电价-电力负荷-供暖需求”三重驱动,75%Pe、90%Pe工况成为主力,100%Pe工况集中在高电价时段且出力适配供暖抽汽,形成“多能联合-三流协同”模式。不同场景下调峰性能与协同效益见表4
非供暖季引入海水淡化后出力范围为300.7~1 137.2 MW,更聚焦中高负荷区间,响应速度快,净收益从359.8万元增至786.4万元,增幅118.7%,水资源生产增益显著。供暖季调峰深度略宽,为289.34~1 253.8 MW,但受供暖抽汽刚性约束,电力灵活性受限,总收益683.5万元,较非供暖季海水淡化运行场景降低13.1%,但收益结构更均衡(电力65%、供暖25%、海水淡化10%),抗市场波动能力更强。
本文提出核电机组联合海水淡化运行系统,结合电力现货市场价格,分析系统在不同运行场景(非供暖季与供暖季)及配置模式(有无海水淡化、有无供暖)下的调峰性能与经济性。通过热力系统仿真与优化分析得出以下结论。
1)核电机组联合海水淡化系统非供暖季采用“优先出力再调海水淡化”策略,调峰深度可达878.5 MW。
2)核电机组联合海水淡化运行经济性提升显著,非供暖季净收益从359.8万元增至786.4万元;供暖季总收益683.5万元,虽较非供暖季略有降低,但收益结构更均衡。
3)系统多场景适应性强,调峰性能与经济性提升,并可同时满足电力、供暖、淡水三类需求,为核电机组灵活运行提供了可行路径。
  • 国网山东省电力公司科技项目资助(520626230037)
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doi: 10.19666/j.rlfd.202510022
  • 接收时间:2025-10-14
  • 首发时间:2026-08-14
  • 出版时间:2026-05-25
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  • 收稿日期:2025-10-14
  • 修回日期:2025-10-31
  • 录用日期:2025-11-18
基金
Science and Technology Project of State Grid Shandong Electric Power Company(520626230037)
国网山东省电力公司科技项目资助(520626230037)
作者信息
    1.国网山东省电力公司电力科学研究院,山东 济南 250013
    2.华北电力大学能源动力与机械工程学院,北京 102206
    3.华北电力大学热电生产过程污染物监测与控制北京市重点实验室,北京 102206

通讯作者:

张衡(1991),男,博士,副教授,主要研究方向为能源系统节能与优化,
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