1.国网吉林省电力有限公司,吉林省 长春市 130021
2.东南大学电气工程学院,江苏省 南京市 211189
孙勇(1980),男,博士,正高级工程师,主要从事电网发展规划、电力调度控制等方面的研究工作,sunyong_hit@163.com;
高子航(1999),男,硕士研究生,主要从事综合能源系统方面的研究,g18551638378@163.com;
侯泽胤(2000),男,硕士研究生,主要从事综合能源系统、隐私保护技术方面的研究,hzy17301571262@173.com;
陆帅(1993),男,博士,讲师,主要从事电力及综合能源系统、优化理论,以及先进计算技术等领域的研究,本文通信作者,shuai.lu.seu@outlook.com。
收稿:2024-06-23,
修回:2024-09-12,
纸质出版:2025-04-30
移动端阅览
孙勇,高子航,侯泽胤等.面向综合能源系统运行的气网聚合建模及辨识方法[J].发电技术,2025,46(02):274-283.
SUN Yong,GAO Zihang,HOU Zeyin,et al.Gas Network Aggregate Modeling and Identification Method for Integrated Energy System Operation[J].Power Generation Technology,2025,46(02):274-283.
孙勇,高子航,侯泽胤等.面向综合能源系统运行的气网聚合建模及辨识方法[J].发电技术,2025,46(02):274-283. DOI: 10.12096/j.2096-4528.pgt.24118.
SUN Yong,GAO Zihang,HOU Zeyin,et al.Gas Network Aggregate Modeling and Identification Method for Integrated Energy System Operation[J].Power Generation Technology,2025,46(02):274-283. DOI: 10.12096/j.2096-4528.pgt.24118.
目的
2
天然气网络是综合能源系统的重要组成部分,可为综合能源系统的运行提供可观的灵活性。建立精确的气网模型是综合能源系统运行控制的基础,然而现有的气网机理模型复杂度较高,部分参数在实际工程中不可辨识。为此,提出一种面向综合能源系统运行的气网聚合建模及辨识方法。
方法
2
以气网为研究对象,提出了气网聚合模型的概念并推导了其表达式,设计了对应的离线参数估计方法。由于气网聚合模型的推导依赖于特定的工况,当工况发生变化时,模型参数亦会随之改变,离线估计模型会产生不可忽略的误差。针对该问题,提出了基于遗忘因子递推最小二乘法的模型滚动校正方法。
结果
2
在气体流速变化较小的情况下,离线参数估计方法具有较好准确性;但是在流速快速变化情况下,离线估计无法精准描述气网运行状态,滚动校正可以实现对模型的精确拟合。
结论
2
所提气网聚合模型离线估计与在线滚动校正方法可为综合能源系统的运行分析与控制提供实用、高效的流体网络建模技术。
Objectives
2
Natural gas networks are important components of integrated energy systems
which can provide considerable flexibility for the operation of the integrated energy system. Developing an accurate gas network model is the foundation for the operation and control of integrated energy systems. However
existing mechanism models for gas networks show high complexity
with certain parameters being unidentifiable in practical engineering applications. Therefore
this study proposes a gas network aggregate modeling and identification method specifically for integrated energy system operation.
Methods
2
Taking the gas network as the research object
the concept of the gas network aggregate model is proposed
its formula is derived
and a corresponding offline parameter estimation method is designed. Since the derivation of the gas network aggregate model depends on specific operating conditions
the model parameters vary with condition changes
leading to non-negligible errors in the offline estimation model. To address this issue
a rolling calibration method based on forgetting factor recursive least squares is proposed.
Results
2
Under small gas flow variations
the offline parameter estimation method demonstrates satisfactory accuracy. However
under rapid flow variations
the offline estimation fails to accurately describe the operation status of the gas network
and rolling calibration can achieve accurate fitting of the model.
Conclusions
2
The proposed gas network aggregate model integrating offline estimation and online rolling calibration can provide a practical and efficient fluid network modeling solution for the operation analysis and control of integrated energy systems.
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