郭越, 徐飞, 陈磊, 郝玲, 郑旭升, 倪平, 常东锋, 林琳. 适用于一次调频能力在线监测的锅炉蓄热系数计算方法[J]. 中国电机工程学报, 2024, 44(5): 1872-1880. DOI: 10.13334/j.0258-8013.pcsee.223063
引用本文: 郭越, 徐飞, 陈磊, 郝玲, 郑旭升, 倪平, 常东锋, 林琳. 适用于一次调频能力在线监测的锅炉蓄热系数计算方法[J]. 中国电机工程学报, 2024, 44(5): 1872-1880. DOI: 10.13334/j.0258-8013.pcsee.223063
GUO Yue, XU Fei, CHEN Lei, HAO Ling, ZHENG Xusheng, NI Ping, CHANG Dongfeng, LIN Lin. Identification of Boiler Heat Storage Coefficient for Online Monitoring of Primary Frequency Regulation Capability of Steam Unit[J]. Proceedings of the CSEE, 2024, 44(5): 1872-1880. DOI: 10.13334/j.0258-8013.pcsee.223063
Citation: GUO Yue, XU Fei, CHEN Lei, HAO Ling, ZHENG Xusheng, NI Ping, CHANG Dongfeng, LIN Lin. Identification of Boiler Heat Storage Coefficient for Online Monitoring of Primary Frequency Regulation Capability of Steam Unit[J]. Proceedings of the CSEE, 2024, 44(5): 1872-1880. DOI: 10.13334/j.0258-8013.pcsee.223063

适用于一次调频能力在线监测的锅炉蓄热系数计算方法

Identification of Boiler Heat Storage Coefficient for Online Monitoring of Primary Frequency Regulation Capability of Steam Unit

  • 摘要: 锅炉蓄热系数是衡量火电机组一次调频能力的重要参数,实际运行中随着机组工况变化,但目前仿真中多忽略变工况下的蓄热系数变化对调频能力的影响。为准确掌握机组运行中真实的一次调频能力,需要在线确定蓄热系数。基于IEEE标准模型的框架,提出适用于一次调频能力在线监测的锅炉蓄热系数计算方法。建立考虑工质流动与传热过程的锅炉热力系统动态模型。以某1 000 MW超临界机组为算例,结果表明所提出的变工况锅炉蓄热系数算法能更准确地描述一次调频过程中热力参数的动态变化。一次调频60 s内积分电量相较于IEEE标准模型减小62.4%。且低负荷下蓄热系数大,蓄热释放速率慢。该方法能根据现场实时数据确定当前工况下的蓄热系数,可应用于一次调频能力在线监测。

     

    Abstract: The boiler model has become an essential parameter to evaluate the primary frequency regulation ability (PFR), and it varies with the different load in practical engineering. Based on the IEEE standard model structure, a calculation method of boiler heat storage coefficient suitable for on-line monitoring of PFR capacity is proposed. By establishing the dynamic model of flow and heat transfer, the boiler heat storage coefficient is calculated online according to the valve step simulation. Taking a 1 000 MW supercritical unit as an example, the results show that the heat storage coefficient obtained by this method can describe the dynamic changes of thermal parameters during the process of PFR more accurately. Compared with the IEEE standard model, the integrated power in 60 seconds is reduced by 62.4% using the proposed model. Heat storage coefficient changes with the working conditions. Under low load, the heat storage coefficient is large and the heat storage releases slowly. This method can identify the heat storage coefficient under the current working condition according to the on-site real-time data, and it can be applied to the on-line monitoring of PFR capacity.

     

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