摘要
为增加可再生能源发电量消纳,火电机组参与电网调峰已趋于高频化、常态化,频繁的负荷波动导致机组能耗水平升高、控制品质降低、设备寿命损耗严重,亟需对其变负荷过程瞬态特性进行深入的研究。对火电机组瞬态特性研究中使用的模型、仿真工具及相关研究成果进行梳理,分析存在的问题,归纳未来研究重点,提出技术应用发展方向。当前研究对仿真手段依赖严重,瞬态模型缺乏实践验证,极少有研究在瞬态特性评价基础上进一步提出控制策略,难以实现成果转化。未来推进火电机组瞬态过程评价的精确性和求解的快速性是理论研究的重点,将模型相关研究成果与智能发电技术有机融合是技术应用发展方向。
In order to increase the consumption of renewable energy for electricity generation, using thermal power unit in peak shaving in power grid has become high frequency and normal. Frequent load fluctuation leads to higher energy consumption, lower control quality and serious equipment life loss. Therefore, it is urgently necessary to carry out an in-depth research on the transient characteristics of thermal power unit with variable load. In this paper, the models, simulation tools and related results in the research on transient characteristics of thermal power unit were sorted out. The existing problems, future research focuses and direction of the technology development and application were put forward. Currently, the researches rely heavily on simulation methods, and the transient models were lack of practical verification. There are only few researches that further propose control strategies based on transient characteristic evaluation. Thus, it is difficult to realize the conversion of achievements. In the future, the focus of the theoretical research is to promote the accuracy and rapidity of transient process evaluation for thermal power unit. The direction of the technology application and development is to organically integrate the research results of the models with intelligent power generation technology.
作者
谢天
梁凌
张婷
李庚达
崔青汝
陈保卫
胡文森
XIE Tian;LIANG Ling;ZHANG Ting;LI Gengda;CUI Qingru;CHEN Baowei;HU Wensen(Guodian New Energy Technology Research Institute,Changping District,Beijing 102209,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2020年第19期6238-6245,共8页
Proceedings of the CSEE
基金
北京市科协金桥工程种子基金(ZZ19003)
国家能源集团公司科技创新项目(GJNY-19-06-1)。
关键词
火电机组
瞬态特性
瞬态模型
仿真工具
能耗分析
寿命预测
控制优化
thermal power unit
transient characteristics
transient models
simulation tools
energy consumption analysis
residual life prediction
control optimization