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Article(id=1236345974076002953, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345965947449499, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202501011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736870400000, receivedDateStr=2025-01-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772697450417, onlineDateStr=2026-03-05, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772697450417, onlineIssueDateStr=2026-03-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772697450417, creator=13701087609, updateTime=1772697450417, updator=13701087609, issue=Issue{id=1236345965947449499, tenantId=1146029695717560320, journalId=1210938733613449225, year='2025', volume='54', issue='6', pageStart='1', pageEnd='210', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772697448479, creator=13701087609, updateTime=1772697609456, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236346641175859638, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345965947449499, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236346641175859639, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345965947449499, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=204, endPage=210, ext={EN=ArticleExt(id=1236345974424130206, articleId=1236345974076002953, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Electricity-carbon coupling modeling and coordinated control strategy for coal-fired power units with amine decarbonization, columnId=1236345969239978183, journalTitle=Thermal Power Generation, columnName=System integration, sequestration and policy- economic analysis, runingTitle=null, highlight=null, articleAbstract=

When thermal power units employ amine-based carbon capture, electro-carbon coupling exists. To enhance the load flexibility tracking performance of decarbonized units, a variable-load control strategy based on electric-carbon coordination is proposed. Using existing data, an electric-carbon coordinated control system model for drum boiler thermal power units was established through system identification. The response time scales of reboiler load to power generation load and carbon capture rate were analyzed. Based on this, a dual-control loop for power generation load was designed, incorporating both decarbonization steam extraction and fuel quantity regulation. Furthermore, to address the effect of long time scales on carbon capture rate, the transient quantities of reboiler load variation throughout the process were reconstructed, and a flexible power generation load control method based on electric-carbon synergy was proposed. Simulation tests on a 300 MW unit demonstrated that, compared with the conventional coordinated control strategies, the proposed strategy ensures performance metrics for thermal load and carbon capture rate while improving both the load variation control rate and AGC performance metrics by an average of 100% or more.

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火电机组采用胺法脱碳时存在电碳耦合，为提升脱碳机组的负荷灵活跟踪性能，提出了一种基于电碳协调的变负荷控制策略。基于已有数据，以辨识方式建立了汽包炉火电机组的电碳协调控制系统模型；分析了再沸器负荷对发电负荷和碳捕集率的响应时间尺度，并基于此设计了基于脱碳抽汽与燃料量双重控制的发电负荷控制回路；进一步，为克服长时间尺度对碳捕集率的影响，重构了全程再沸器负荷变化瞬态量，提出了基于电碳协同的发电负荷灵活控制方法。以某300 MW机组为例进行了仿真测试，结果表明：相较传统协调控制策略，所提控制策略可在保证机组热负荷和碳捕集率性能指标的同时，使机组的变负荷控制速率及AGC性能指标平均提升1倍以上。

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王玮（1986），男，博士，教授，主要研究方向为发电过程智能优化及协同控制技术，。
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孔德安（1987），男，副高级工程师，主要研究方向为热工自动化及网源协调技术，。

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孔德安（1987），男，副高级工程师，主要研究方向为热工自动化及网源协调技术，。

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孔德安（1987），男，副高级工程师，主要研究方向为热工自动化及网源协调技术，。

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figureFileBig=L9gaLTaeTSSJLm3OMnwrRw==, tableContent=null), ArticleFig(id=1236390486764737366, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345974076002953, language=EN, label=Fig.10, caption=The unit load output at 3.0% THA/min, figureFileSmall=Nl0h48v6jZM1YoObj2zyEg==, figureFileBig=mnTSTMXm8P5A6TO+ZAbrEQ==, tableContent=null), ArticleFig(id=1236390486844429145, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345974076002953, language=CN, label=图10, caption=3.0%THA/min机组负荷输出, figureFileSmall=Nl0h48v6jZM1YoObj2zyEg==, figureFileBig=mnTSTMXm8P5A6TO+ZAbrEQ==, tableContent=null), ArticleFig(id=1236390486924120924, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345974076002953, language=EN, label=Tab.1, caption=

Simulation parameters of a certain 300 MW unit

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项目数值
负荷惯性时间常数TCP/s12
负荷静态增益KCP0.175
制粉动态时间Tf/s15
汽包蓄热能力Cb/(MJ·MW–1)3 300
锅炉功率系数K12.37
汽轮机功率增益K20.269
差压拟合比例K33.5×10–4
THA工况最大抽汽质量流量/(t·h–1)517.4
50%THA工况最大抽汽质量流量/(t·h–1)211.4
脱碳抽汽焓值/(kJ·kg–1)3 155.5
再沸器温度/℃117
), ArticleFig(id=1236390487003812705, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345974076002953, language=CN, label=表1, caption=

某300 MW机组仿真参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
负荷惯性时间常数TCP/s12
负荷静态增益KCP0.175
制粉动态时间Tf/s15
汽包蓄热能力Cb/(MJ·MW–1)3 300
锅炉功率系数K12.37
汽轮机功率增益K20.269
差压拟合比例K33.5×10–4
THA工况最大抽汽质量流量/(t·h–1)517.4
50%THA工况最大抽汽质量流量/(t·h–1)211.4
脱碳抽汽焓值/(kJ·kg–1)3 155.5
再沸器温度/℃117
), ArticleFig(id=1236390487100281700, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345974076002953, language=EN, label=Tab.2, caption=

Control performance of different control strategies

, figureFileSmall=null, figureFileBig=null, tableContent=
变负荷斜坡输入控制策略IAEITAEKp
1.5%THA/min传统控制策略2.746×1031.863×1060.144
本文策略8.000×1021.280×1050.311
3.0%THA/min传统控制策略2.777×1031.783×1060.173
本文策略1.003×1022.474×1050.353
), ArticleFig(id=1236390487217722216, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345974076002953, language=CN, label=表2, caption=

不同控制策略控制性能对比

, figureFileSmall=null, figureFileBig=null, tableContent=
变负荷斜坡输入控制策略IAEITAEKp
1.5%THA/min传统控制策略2.746×1031.863×1060.144
本文策略8.000×1021.280×1050.311
3.0%THA/min传统控制策略2.777×1031.783×1060.173
本文策略1.003×1022.474×1050.353
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基于电碳协同的胺法脱碳火电机组灵活控制策略
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孔德安 1 , 赖延春 2 , 朱清 3 , 王晓宇 1 , 王玮 2
热力发电 | 系统集成、封存及政策经济分析 2025,54(6): 204-210
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热力发电 | 系统集成、封存及政策经济分析 2025, 54(6): 204-210
基于电碳协同的胺法脱碳火电机组灵活控制策略
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孔德安1 , 赖延春2, 朱清3, 王晓宇1, 王玮2
作者信息
  • 1.国网新疆电力有限公司电力科学研究院，新疆　乌鲁木齐　830011
  • 2.华北电力大学控制与计算机工程学院，北京　102206
  • 3.国网新疆电力有限公司，新疆　乌鲁木齐　830011

    • 孔德安（1987），男，副高级工程师，主要研究方向为热工自动化及网源协调技术，。

通讯作者:

王玮（1986），男，博士，教授，主要研究方向为发电过程智能优化及协同控制技术，。
Electricity-carbon coupling modeling and coordinated control strategy for coal-fired power units with amine decarbonization
Dean KONG1 , Yanchun LAI2, Qing ZHU3, Xiaoyu WANG1, Wei WANG2
Affiliations
  • 1.State Grid Xinjiang Company Limited Electric Power Research Institute, Urumqi 830011, China
  • 2.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
  • 3.State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830011, China
出版时间: 2025-06-25 doi: 10.19666/j.rlfd.202501011
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摘要
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火电机组采用胺法脱碳时存在电碳耦合，为提升脱碳机组的负荷灵活跟踪性能，提出了一种基于电碳协调的变负荷控制策略。基于已有数据，以辨识方式建立了汽包炉火电机组的电碳协调控制系统模型；分析了再沸器负荷对发电负荷和碳捕集率的响应时间尺度，并基于此设计了基于脱碳抽汽与燃料量双重控制的发电负荷控制回路；进一步，为克服长时间尺度对碳捕集率的影响，重构了全程再沸器负荷变化瞬态量，提出了基于电碳协同的发电负荷灵活控制方法。以某300 MW机组为例进行了仿真测试，结果表明：相较传统协调控制策略，所提控制策略可在保证机组热负荷和碳捕集率性能指标的同时，使机组的变负荷控制速率及AGC性能指标平均提升1倍以上。

碳捕集电厂  /  胺法脱碳  /  电碳协同  /  再沸器负荷  /  协调控制  /  灵活控制

When thermal power units employ amine-based carbon capture, electro-carbon coupling exists. To enhance the load flexibility tracking performance of decarbonized units, a variable-load control strategy based on electric-carbon coordination is proposed. Using existing data, an electric-carbon coordinated control system model for drum boiler thermal power units was established through system identification. The response time scales of reboiler load to power generation load and carbon capture rate were analyzed. Based on this, a dual-control loop for power generation load was designed, incorporating both decarbonization steam extraction and fuel quantity regulation. Furthermore, to address the effect of long time scales on carbon capture rate, the transient quantities of reboiler load variation throughout the process were reconstructed, and a flexible power generation load control method based on electric-carbon synergy was proposed. Simulation tests on a 300 MW unit demonstrated that, compared with the conventional coordinated control strategies, the proposed strategy ensures performance metrics for thermal load and carbon capture rate while improving both the load variation control rate and AGC performance metrics by an average of 100% or more.

carbon capture power plants  /  amine decarbonization  /  electro-carbon coordination  /  reboiler load  /  coordinated control  /  flexible control
孔德安, 赖延春, 朱清, 王晓宇, 王玮. 基于电碳协同的胺法脱碳火电机组灵活控制策略. 热力发电, 2025 , 54 (6) : 204 -210 . DOI: 10.19666/j.rlfd.202501011
Dean KONG, Yanchun LAI, Qing ZHU, Xiaoyu WANG, Wei WANG. Electricity-carbon coupling modeling and coordinated control strategy for coal-fired power units with amine decarbonization[J]. Thermal Power Generation, 2025 , 54 (6) : 204 -210 . DOI: 10.19666/j.rlfd.202501011
正文
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碳捕集、利用与封存（carbon capture, utilization and storage，CCUS）是目前实现化石能源低碳化利用的唯一技术选择[1]。截至2023年底，我国煤电装机容量约11.7亿千瓦，约占全国电力总装机的40%，发电用煤约占全国煤炭消费总量的60%，碳排放量约占全国碳排放总量的40%。因此，在火电领域实施CCUS也是我国实现“双碳”目标的必由之路[2]。
目前，碳捕集技术主要分为燃烧前捕集[3]、富氧燃烧[4]和燃烧后捕集[5-7]3种。其中，燃烧后胺法捕集技术工艺成熟，在火电机组中得到广泛应用。然而，该技术需要从汽轮机低压缸抽取部分蒸汽作为热源用于单乙醇胺（monoethanolamine，MEA）溶剂再生，该部分抽汽会降低燃煤机组10%～20%的瞬时功率，电碳在一定程度上存在耦合现象[8-10]。因此，建立碳捕集电厂的电碳耦合特性模型并设计协调控制策略具有重要现实意义。
目前，针对碳捕集电厂的建模与控制已有较多研究[11]。王立健等[12]以某600 MW火电机组为例，分析了机组效率和碳捕集率的变工况耦合特性；王玮等[13]建立了碳捕集电厂电碳耦合特性静态机理模型，并采用辨识方法分析了发电侧和碳捕集侧受脱碳抽汽流量影响的动态响应时间尺度；靳义净[14]基于Fluent建立了某电厂的脱碳系统数学模型并设计了系统运行优化策略；吴啸等[15]为燃煤电站机炉系统和捕集系统分别设计了预测控制器，实现了两套系统间的相互协调和深度结合；陈显浩等[16]提出了可再生能源背景下超临界燃煤电站-碳捕集系统的整体优化调度方法，提升了机组消纳弃风弃光的性能，降低了碳捕集成本；李小飞等[17]基于Aspen Dynamics建立了胺法脱碳系统的动态模型并设计了计及变工况状态下碳捕集率稳定运行的比例控制策略；Jairo等人[18]考虑机组响应电网负荷需求和维持碳捕集率的平衡关系，设计了一种基于预测控制的碳捕集电厂协调控制策略；Arce等人[19-21]以模型预测控制为中心，以降低能耗或运行成本为目标，解决了碳捕集联合系统多变量、强耦合系统的优化问题。
然而，现有研究多是针对碳捕集系统控制策略的优化改进，考虑电碳耦合的全系统协调控制策略研究较少。对此，本文以某300 MW燃煤机组为对象，建立其电碳协调控制系统模型，设计基于再沸器负荷主动响应与自主恢复的发电负荷灵活控制策略，并提出计及再沸器负荷主动响应与自主恢复的发电负荷灵活控制策略。所提策略在保持再沸器负荷稳定的同时，显著提升了机组的变负荷速率与AGC调节性能。
1　电碳耦合建模
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1.1　系统结构与工作原理
采用胺法脱碳的火电机组能质转化流程如图1所示。
锅炉尾部烟气经脱硫脱硝处理后被送至吸收塔底部，与吸收塔顶流下的MEA贫液溶剂逆向接触并反应；提纯后的烟气通过吸收塔顶排入大气，而吸收CO2的MEA富液则被送入解析塔中进行CO2的析出，此过程中，部分中压缸排汽被抽至再沸器为CO2析出提供热量。解析完的MEA溶液会继续被送入吸收塔，完成MEA溶液的循环利用；提纯后的CO2气体经过解析塔顶部排出；由于还有少量CO2吸附在高温高压的蒸汽中，解析塔顶部另设有冷凝器将这部分CO2分离出来；提纯后的CO2会被封存、储存和后续利用。
因此，采用胺法脱碳的火电机组电碳耦合特性主要由脱碳抽汽决定，其一般来源于中压缸排汽，通过调节脱碳抽汽蝶阀开度可以改变进入碳捕集系统的脱碳抽汽流量（直接影响再沸器负荷，进而影响脱碳效率）和进入低压缸做功的蒸汽流量（直接影响发电负荷）[22]。考虑到脱碳抽汽蝶阀开度决定机组的电碳负荷分配，首先需要通过碳捕集机组的非线性模型揭示再沸器负荷（即脱碳抽汽流量）扰动对发电负荷的响应特性，然后建立碳捕集电厂电碳协调控制系统模型。
1.2　再沸器负荷扰动响应特性
1.2.1　再沸器负荷扰动响应特性
如图1所示，脱碳抽汽通常来源于中压缸排汽，文献[23]以某300 MW机组为例建立的供热抽汽对机组发电负荷的动态模型具有较高精度，且抽汽也同样来源于中压缸排汽，模型具有一致性。采用该模型进行仿真测试，测试前机组负荷稳定在235 MW，中压缸排汽抽汽流量为400 t/h，仿真测试相关参数变化情况如图2所示。
由图2可知，热负荷抽汽蝶阀动作后，机组抽汽流量即时响应，约20 s后机组发电负荷达到新的稳态，稳定在245 MW。由此可知：当再沸器负荷发生扰动变化时，发电负荷的响应速度和幅度均发生可观的变化，且响应型式与一阶惯性系统高度吻合，其微分方程模型可描述为：
TCPdΔNE(t)dt+ΔNE(t)=KCPΔDCC(t)
式中：ΔNE为机组发电负荷增量；ΔDCC为脱碳抽汽流量变化量；TCP为惯性时间常数，反映系统的响应速度；KCP为脱碳抽汽流量对机组发电功率的静态增益系数，反映再沸器热负荷与发电负荷的静态转换关系。
利用最小二乘法[24]对图2系统的结构参数TCP和KCP进行辨识，结果为：KCP=-0.162，TCP=12 s。
1.2.2　再沸器负荷对脱碳效率的影响特性
文献[22]给出了某300 MW机组再沸器负荷发生扰动时脱碳效率的响应曲线，其响应趋势与图2完全一致，辨识结果显示再沸器负荷对脱碳效率影响的响应时间常数为20~30 min。
1.2.3　电碳协同的可行性分析
脱碳抽汽阀门开度可以作为控制变量，对再沸器负荷和发电负荷进行灵活调配。由于脱碳过程具有较大的惯性，再沸器负荷发生扰动时，脱碳效率响应时间是发电负荷响应时间的百倍级别，在该时间尺度下通过能量协同调控实现对发电负荷的灵活调节，同时不对脱碳效率产生大的影响。而且，脱碳抽汽阀门开度变化对主蒸汽压力等参数也不会产生影响，因此通过电碳协同控制提升机组的运行灵活性具有较强可行性。
1.3　考虑碳捕集过程的发电负荷控制系统模型
在原有纯凝机组机炉协调控制系统模型的基础上，引入碳捕集过程再沸器负荷变化对发电负荷的影响特性，可得到考虑碳捕集过程的发电负荷控制系统模型，如图3所示。
其中纯凝发电机组的模型可用下式[25]描述：
rB(s)=e−τsTfs+1μB(s)
K1rB−K2pTμt=Cddpddt
pT=pd−K3(K1rB)1.5
TtdNEdt=K2pTμT−NE
式中：Tf为制粉过程的动态时间；rB为进入炉膛的实际燃料量；uB为燃料量指令；τ为系统延迟时间；Cb为汽包的蓄热能力；pd为汽包压力；pT为主蒸汽压力；K1为锅炉功率系数；K2为汽轮机功率增益；μT为汽轮机主蒸汽阀门开度；K3为差压拟合比例；Tt为汽轮机动态时间。
2　控制策略设计
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2.1　设计思路
脱碳抽汽流量在短时间尺度内的变化对脱碳效率基本不会产生影响，但却可以显著提升电负荷的响应速率。因此，可考虑先通过再沸器负荷快速转换来快速响应电负荷需求，在此过程中，为克服对脱碳效率的影响，需监测再沸器负荷响应电负荷的能量，并发送给燃料量指令，使得再沸器负荷在调节周期内总量保持平衡。
针对碳捕集机组电热协调与快速响应电网负荷的双重需求，控制策略设计思路遵循以下原则：
1）AGC响应初期，再沸器负荷即时响应发电负荷，提升负荷响应速率；
2）调节过程中，燃料量接收到的指令应同步满足发电负荷和再沸器负荷需求；
3）燃料量跟上后，再沸器负荷应能自主恢复，且在调节过程中再沸器负荷的总能量较其主动响应电负荷时无损失。
2.2　基于双重控制的能量平衡控制回路
考虑到图3所示负荷控制系统为“3入2出”系统，从能量平衡角度看，再沸器负荷与发电负荷的能量来源均为燃料量，二者具有耦合关系，且发电负荷响应需设计为快响应回路、再沸器负荷为慢响应需求回路等特点，可设计一条适应发电负荷快速响应的脱碳抽汽控制、燃料量控制双重控制回路，如图4所示，其结构和功能如下：
1）脱碳抽汽控制回路为主控制回路，其信号输入为负荷指令与实际负荷的偏差。回路可利用脱碳抽汽流量变化对机组发电负荷的快速响应特性实现对负荷偏差的快速调节。
2）燃料量控制回路作为副控制回路，其信号输入为脱碳抽汽流量指令与实际值偏差，其中脱碳抽汽实际值来源于上述控制回路的控制器输出。
2.3　全程再沸器负荷瞬态重构
从原理上看，上述双重控制结构可以使机组进入稳态后再沸器负荷恢复到原设计状态，但未考虑在调节过程用以支撑发电负荷快速调节的部分，当超出碳捕集率惯性响应时间后，会对碳捕集率产生实质性影响。为此，基于脱碳抽汽流量实际值重构一个可表征调节全过程的再沸器负荷变化瞬态量：
ΔD¯CC=∫t1t2ΔDCCdtt2−t1
式中：ΔD¯CC为全程再沸器负荷变化瞬态量；t1和t2分别为起始时间和当前时间。
2.4　基于电碳协同的机组灵活控制策略
设计了如图5所示的机组快速变负荷控制策略。
其控制过程如下：
1）当机组接收到的AGC指令发生变化时，机组发电负荷的主控制回路脱碳抽汽回路主动响应，脱碳抽汽流量发生变化，机组负荷迅速往指令方向调节。
2）同时，构造的全程再沸器负荷变化瞬态量也发生变化，偏离设计值0，并作用于副控制回路，锅炉燃料量开始动作，弥补发电负荷与再沸器负荷所需的总能量需求。
3）当燃料量作用反映到发电负荷后，脱碳抽汽PID作用于脱碳抽汽流量使其逐渐恢复，直至发电负荷与再沸器负荷分别达到平衡。此时，脱碳抽汽流量不仅恢复到原来的平衡状态，反映全程再沸器负荷能量特征的构造信号也恢复到原状态，保证了整个调节过程中再沸器负荷的稳定。
3　仿真试验
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3.1　阶跃扰动测试
以某300 MW碳捕集机组为例进行仿真实验。该机组仿真参数见表1[25]。
机组处于平衡状态时，各参数为：Ne=241 MW，pT=16.67 MPa，Qreb=211.48 MW，η=90%。对控制系统施加+10 MW的阶跃扰动，各输出的仿真结果图6—图8所示。
引入上升时间Tr作为表征阶跃响应下机组初期变负荷性能的定量指标对控制效果进行分析，结果表明，传统控制策略Tr为126.73 s，而本文所提控制策略Tr为475.58 s，采用该控制策略可使机组初期负荷响应速度提升73.35%。同时，长期来看机组500 s内就可以稳定在负荷给定值，相比传统控制策略调节时间缩短了约一半；且其初期的变负荷速率较传统控制策略提升了约一倍。
3.2　斜坡扰动测试
为进一步验证本文快速变负荷控制策略的优越性，将发电负荷阶跃输入改为斜坡信号（该信号更接近限幅限速后的实际电网指令AGC信号），深入分析在不同变负荷速率输入下各控制策略的性能。图9和图10分别为机组在1.5%THA/min和3.0%THA/min负荷爬坡速率下的输出响应曲线。
为定量分析不同控制策略的控制效果和不同AGC指令下的性能指标，使用IAE、ITAE和AGC考核指标来定量评估控制效果。2种变负荷速率下的控制性能指标如表2所示。
由表2可以直观地看出，本文所提控制策略在IAE、ITAE、AGC性能指标方面均显著优于传统控制策略。其中，变负荷速率设定为额定负荷的1.5%/min时，AGC性能综合评价指标Kp由0.144提升到0.311，提升了1.16倍；变负荷速率设定为额定负荷的3.0%/min时，AGC性能综合评价指标Kp由0.173提升到0.353，提升了1.04倍。
4　结语
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本文提出了一种基于电热协同的碳捕集电厂灵活控制方法，首先基于再沸器负荷对发电负荷和碳捕集率的响应时间尺度差异，设计了基于脱碳抽汽与燃料量双重控制的发电负荷控制回路；进一步，为克服长时间尺度对碳捕集率的影响，重构了全程再沸器负荷变化瞬态量，设计了基于电碳协同的发电负荷灵活控制策略。以某300 MW机组为例进行了仿真测试，结果表明：相较传统协调控制策略，所提控制策略可在保证机组热负荷和碳捕集率性能指标的同时，使机组的变负荷控制速率及AGC性能指标平均提升1倍以上。
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2025年第54卷第6期
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doi: 10.19666/j.rlfd.202501011
  • 接收时间：2025-01-15
  • 首发时间：2026-03-05
  • 出版时间：2025-06-25
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  • 收稿日期：2025-01-15
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    1.国网新疆电力有限公司电力科学研究院，新疆　乌鲁木齐　830011
    2.华北电力大学控制与计算机工程学院，北京　102206
    3.国网新疆电力有限公司，新疆　乌鲁木齐　830011

通讯作者:

王玮（1986），男，博士，教授，主要研究方向为发电过程智能优化及协同控制技术，。
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