Gas Network Aggregate Modeling and Identification Method for Integrated Energy System Operation

doi:10.12096/j.2096-4528.pgt.24118

Abstract

Abstract:

Objectives 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 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 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 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.

Key words: natural gas, integrated energy system, new energy consumption, gas network, aggregate model, parameter estimation, online calibration

CLC Number:

TK 01

Yong SUN, Zihang GAO, Zeyin HOU, Dexin LI, Yao WANG, Haifeng ZHANG, Shuai LU. Gas Network Aggregate Modeling and Identification Method for Integrated Energy System Operation[J]. Power Generation Technology, 2025, 46(2): 274-283.

Figures/Tables 10

Fig. 1 Schematic diagram of a natural gas pipeline

Fig. 2 Schematic diagram of state variable relationship at gas network node

Fig. 3 Schematic diagram of rolling modeling process with online calibration for gas network aggregate model

Fig. 4 Schematic diagram of gas network structure

Tab. 1 Experimental setup for validation cases of gas network aggregate model

参数	实验编号
参数	1	2
乘性高斯噪声的标准差/%	0.1	0.1
模型阶数	10	10
是否进行数据预处理	否	是

Fig. 5 Comparison between fitted curve of model estimated based on unprocessed data and actual curve

Fig. 6 Comparison between fitted curve of model estimated based on preprocessed data and actual curve

Tab. 2 Effect of data preprocessing on goodness-of-fit indicators of model

所属映射	节点编号	是否数据预处理	E_MAPE	R²
气体压强	2	否	0.001 6	0.94
	2	是	0.001 0	0.98
	3	否	0.001 9	0.95
	3	是	0.001 4	0.97
气体质量流量	2	否	0.140	-2.90
	2	是	0.028	0.83
	3	否	0.065	0.74
	3	是	0.036	0.93

Fig. 7 Comparison of fitted curves between calibrated and uncalibrated models

Tab. 3 Effect of online calibration on goodness-of-fit indicators

所属映射	节点编号	E_MAPE		R²
所属映射	节点编号	未校正	校正	未校正	校正
气体压强	2	0.009 7	0.004 1	0.61	0.96
气体压强	3	0.50	0.073	-45	0.66
气体质量流量	2	0.27	0.077	-5.4	0.77
气体质量流量	3	0.12	0.003 7	-2.1	0.97

References 22

1	FANG J， ZENG Q， AI X，et al ．Dynamic optimal energy flow in the integrated natural gas and electrical power systems[J]．IEEE Transactions on Sustainable Energy，2018，9(1)：188-198． doi:10.1109/tste.2017.2717600
2	HAFSI Z， ELAOUD S， MISHRA M ．A computational modelling of natural gas flow in looped network：effect of upstream hydrogen injection on the structural integrity of gas pipelines[J]．Journal of Natural Gas Science and Engineering，2019，64：107-117． doi:10.1016/j.jngse.2019.01.021
3	QI F， SHAHIDEHPOUR M， WEN F，et al ． Decentralized privacy-preserving operation of multi-area integrated electricity and natural gas systems with renewable energy resources[J]．IEEE Transactions on Sustainable Energy，2020，11(3)：1785-1796． doi:10.1109/tste.2019.2940624
4	YANG J， ZHANG N， BOTTERUD A，et al ．Situation awareness of electricity-gas coupled systems with a multi-port equivalent gas network model[J]．Applied Energy，2020，258：114029． doi:10.1016/j.apenergy.2019.114029
5	LIU W， LI P， YANG W，et al ．Optimal energy flow for integrated energy systems considering gas transients[J]．IEEE Transactions on Power Systems，2019，34(6)：5076-5079． doi:10.1109/tpwrs.2019.2929685
6	SUKHAREV M G， KULALAEVA M A ．Identification of model flow parameters and model coefficients with the help of integrated measurements of pipeline system operation parameters[J]．Energy，2021，232：120864． doi:10.1016/j.energy.2021.120864
7	YIN X， WEN K， HUANG W，et al ．A high-accuracy online transient simulation framework of natural gas pipeline network by integrating physics-based and data-driven methods[J]．Applied Energy，2023，333：120615． doi:10.1016/j.apenergy.2022.120615
8	FAN D， GONG J， ZHANG S，et al ．A transient composition tracking method for natural gas pipe networks[J]．Energy，2021，215：119131． doi:10.1016/j.energy.2020.119131
9	杨宇玄，高栋梁，陈一鸣，等．考虑碳捕集和气网混氢的气电耦合系统低碳经济调度[J]．中国电力，2023，56(10)：1-10．
	YANG Y X， GAO D L， CHEN Y M，et al ．Low carbon economic dispatch of gas electricity coupling system considering carbon capture and hydrogen mixing in gas grid[J]．Electric Power，2023，56(10)：1-10．
10	陈茂云，郑建勇，梅飞，等．考虑压缩机不同运行状态的IES气网潮流分布式计算方法[J]．电力工程技术，2023，42(4)：122-132．
	CHEN M Y， ZHENG J Y， MEI F，et al ．Distributed algorithm for the power flow calculation of gas network in integrated energy system considering different operation states of compressors[J]．Electric Power Engineering Technology，2023，542(4)：122-132．
11	GABRIELAITIENE I， BØHM B， SUNDEN B ．Modelling temperature dynamics of a district heating system in naestved，Denmark：a case study[J]．Energy Conversion and Management，2007，48(1)：78-86． doi:10.1016/j.enconman.2006.05.011
12	RÍOS-MERCADO R Z， BORRAZ-SÁNCHEZ C ．Optimization problems in natural gas transportation systems：a state-of-the-art review[J]．Applied Energy，2015，147：536-555． doi:10.1016/j.apenergy.2015.03.017
13	WOLDEYOHANNES A D， MAJID M A A ．Simulation model for natural gas transmission pipeline network system[J]．Simulation Modelling Practice and Theory，2011，19(1)：196-212． doi:10.1016/j.simpat.2010.06.006
14	CHACZYKOWSKI M ．Transient flow in natural gas pipeline：the effect of pipeline thermal model[J]．Applied Mathematical Modelling，2010，34(4)：1051-1067． doi:10.1016/j.apm.2009.07.017
15	蒋一博．气-电区域综合能源系统协调优化运行策略研究[D]．武汉：武汉大学，2019．
	JIANG Y B ．Study on coordinated and optimized operation strategy of comprehensive energy system in gas-electricity region[D]．Wuhan：Wuhan University，2019．
16	GOURLAY A R， MORRIS J L ．Finite-difference methods for nonlinear hyperbolic systems[J]．Mathematics of Computation，1968：28-39． doi:10.2307/2004760
17	YAO S， GU W， LU S，et al ．Dynamic optimal energy flow in the heat and electricity integrated energy system[J]．IEEE Transactions on Sustainable Energy，2021，12(1)：179-190． doi:10.1109/tste.2020.2988682
18	BOYD S P， VANDENBERGHE L ．Convex optimization[M]．Cambridge，UK：Cambridge Press，2004． doi:10.1017/cbo9780511804441
19	ZHANG S， GU W， ZHANG X，et al ．Dynamic modeling and simulation of integrated electricity and gas systems[J]．IEEE Transactions on Smart Grid，2023，14(2)：1011-1026． doi:10.1109/tsg.2022.3203485
20	KJAER A P， HEATH W P， WELLSTEAD P E ．Identification of cross-directional behaviour in web production：techniques and experience[J]．Control Engineering Practice，1995，3(1)：21-29． doi:10.1016/0967-0661(94)00060-t
21	BADONI M， SINGH A， SINGH B ．Variable forgetting factor recursive least square control algorithm for DSTATCOM[J]．IEEE Transactions on Power Delivery，2015，30(5)：2353-2361． doi:10.1109/tpwrd.2015.2422139
22	BROSCH A， WALLSCHEID O， BOCKER J ．Long-term memory recursive least squares online identification of highly utilized permanent magnet synchronous motors for finite-control-set model predictive control[J]．IEEE Transactions on Power Electronics，2023，38(2)：1451-1467． doi:10.1109/tpel.2022.3206598

[1]	Hui HOU, Yan WANG, Chao LIU, Wei ZHANG, Yangjun ZHOU, Zhengmao LI, Zhengtian LI, Xiangning LIN. Optimization Scheduling of Cold-Heat-Electricity Integrated Energy System Under Pumped Storage Gray Start [J]. Power Generation Technology, 2025, 46(2): 209-218.
[2]	Langbo HOU, Hao SUN, Heng CHEN, Yue GAO. Optimization Scheduling of Integrated Energy Systems in Communities Based on Demand Response and Stackelberg Game [J]. Power Generation Technology, 2025, 46(2): 219-230.
[3]	Kai MA, Zhi YUAN, Ji LI. Low-Carbon Economic Scheduling of Integrated Energy System Considering Diversified Utilization of Hydrogen Energy [J]. Power Generation Technology, 2025, 46(2): 263-273.
[4]	Weiquan TU, Hui LI, Zheng WAN. Research on Short-Term Forecasting of Multiple Loads in Integrated Energy Systems Based on Composite Correlation Coefficients [J]. Power Generation Technology, 2025, 46(1): 9-18.
[5]	Chengping HU, Ming FAN, Aiwang LIU, Yunhui SHI. Multi-Area Interconnected Integrated Energy System Planning Considering Cloud Energy Storage [J]. Power Generation Technology, 2024, 45(4): 641-650.
[6]	Zhenyu ZHAO, Geriletu BAO, Xinxin LI. Optimization and Scheduling of Integrated Energy Systems With Carbon Capture and Storage-Power to Gas Based on Information Gap Decision Theory [J]. Power Generation Technology, 2024, 45(4): 651-665.
[7]	Haibin YU, Yuchen ZHANG, Yangyang LIU, Zengjie LU, Jinde WENG. Optimal Dispatching Bidding Strategy of Multi-Agent Virtual Power Plant Participating in Electricity Market Under Carbon Trading Mechanism [J]. Power Generation Technology, 2023, 44(5): 634-644.
[8]	Meng KANG, Yiqing ZHONG, Xin SHI, Gangcheng WEN, Fang FANG. Research on Two-stage Optimization Approach of Community Integrated Energy System Considering Load Supply Reliability [J]. Power Generation Technology, 2023, 44(1): 25-35.
[9]	Lingjie FENG, Rongrong ZHAI, Yicun GUO, Ning MA, Jiaxin FU. Study on the Comprehensive Performance of Natural Gas Combined Cycle Plant Integrated With Carbon Capture System [J]. Power Generation Technology, 2022, 43(4): 584-592.
[10]	Qingping RAO, Jiangang HAO, Yunshan BAI. Analysis on the Development Path of Natural Gas Power Generation in China Under the Background of Carbon Emission Target [J]. Power Generation Technology, 2022, 43(3): 468-475.
[11]	Yanzhang HUANG, Yuhao ZHOU, Wenguan ZHENG, Mingxiao WANG. Research on New Integrated Energy System With Multi-power Combined Supply of Industrial Parks [J]. Power Generation Technology, 2021, 42(6): 734-740.
[12]	Chao LEI, Tao LI. Key Technologies and Development Status of Hydrogen Energy Utilization Under the Background of Carbon Neutrality [J]. Power Generation Technology, 2021, 42(2): 207-217.
[13]	Lu QU,Bin OUYANG,Zhichang YUAN,Shuqing ZHANG,Rong ZENG. Analysis of Steady-state Characteristics of Thermal Subsystem in Integrated Energy System [J]. Power Generation Technology, 2020, 41(3): 237-244.
[14]	Meng JIANG,Yu HUANG,Weihan LIAO,Jianlian ZHANG,Youwen ZHANG,Chuangxin GUO. Multi-objective Optimization of Electricity-Gas-Heat Integrated Energy System Based on Improved NSGA-Ⅱ Algorithm [J]. Power Generation Technology, 2020, 41(2): 131-136.
[15]	Yunhui SHI,Chuangxin GUO,Xiao DING. Integrated Energy System Economic Dispatch Based on Affine Adjustable Robust Optimization [J]. Power Generation Technology, 2020, 41(2): 118-125.