Optimization of integrated energy system operation containing hydrogen-compressed natural gas based on multiple scenarios and uncertainties

doi:10.19799/j.cnki.2095-4239.2023.0958

Abstract

Abstract:

Rational utilization of hydrogen energy can enhance the flexibility of optimizing integrated energy systems and effectively mitigate the impacts of source and load uncertainty on system operations. Employing hydrogen-compressed natural gas (HCNG) as fuel and incorporating it into cogeneration combined heat and power systems can significantly enhance economic efficiency and hydrogen energy utilization. This paper proposes an optimization framework for the operation of integrated energy systems incorporating HCNG, considering multiple scenarios and uncertainties. The approach begins by establishing an integrated energy system topology that utilizes HCNG as fuel. It then addresses the uncertainties of sources, loads, and scenario probabilities through a three-stage robust optimization model aimed at minimizing system operating costs. The main and subproblems are decomposed using the Column and Constraint Generation (C & CG) algorithm. The Karush-Kuhn-Tucker (KKT) conditions and the Big M method are applied to transform these into a single-layer optimization problem. Comparative analysis of system operation results and robust models demonstrates that HCNG can optimize system operations and reduce costs effectively. The proposed model adeptly handles the discrepancies between actual and predicted scenario probabilities, thus preventing extreme outcomes during system optimization.

Key words: integrated energy system containing hydrogen compressed natural gas, robust optimization model, multi stage optimization, multi scenario

CLC Number:

TK 91

Yangyang XIONG, Aiqing YU, Yufei WANG, Hua XUE. Optimization of integrated energy system operation containing hydrogen-compressed natural gas based on multiple scenarios and uncertainties[J]. Energy Storage Science and Technology, 2024, 13(6): 1888-1899.

Figures/Tables 18

Fig. 1

Fig. 2

Table 1

Fig. 3

Fig. 4

Table 2

Table 3

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Table 4

Fig. 11

Fig. 12

Fig. 13

Fig. 14

References 19

1	人民日报. 建设更高水平开放型经济新体制推动能耗双控逐步转向碳排放双控[N/OL]. 人民日报. (2023-07-12)[2023-12-05]. doi: 10.28655/n.cnki.nrmrb.2023.006899.
2	JACOBSON M Z, DELUCCHI M A, BAUER Z A F, et al. 100% Clean and renewable wind, water, and sunlight all-sector energy roadmaps for 139 countries of the world[J]. Joule, 2017, 1(1): 108-121.
3	潘光胜, 顾伟, 张会岩, 等. 面向高比例可再生能源消纳的电氢能源系统[J]. 电力系统自动化, 2020, 44(23): 1-10.
	PAN G S, GU W, ZHANG H Y, et al. Electricity and hydrogen energy system towards accomodation of high proportion of renewable energy[J]. Automation of Electric Power Systems, 2020, 44(23): 1-10.
4	GUANDALINI G, CAMPANARI S, ROMANO M C. Power-to-gas plants and gas turbines for improved wind energy dispatchability: Energy and economic assessment[J]. Applied Energy, 2015, 147: 117-130.
5	AZMI A N, DAHLBERG I N, KOLHE M L, et al. Impact of increasing penetration of photovoltaic (PV) systems on distribution feeders[C]// 2015 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE). IEEE, 2015: 70-74.
6	张涛, 郭玥彤, 李逸鸿, 等. 计及电气热综合需求响应的区域综合能源系统优化调度[J]. 电力系统保护与控制, 2021, 49(1): 52-61.
	ZHANG T, GUO Y T, LI Y H, et al. Optimization scheduling of regional integrated energy systems based on electric-thermal-gas integrated demand response[J]. Power System Protection and Control, 2021, 49(1): 52-61.
7	林楷东, 陈泽兴, 张勇军, 等. 含P2G的电-气互联网络风电消纳与逐次线性低碳经济调度[J]. 电力系统自动化, 2019, 43(21): 23-33.
	LIN K D, CHEN Z X, ZHANG Y J, et al. Wind power accommodation and successive linear low-carbon economic dispatch of integrated electricity-gas network with power to gas[J]. Automation of Electric Power Systems, 2019, 43(21): 23-33.
8	陈锦鹏, 胡志坚, 陈颖光, 等. 考虑阶梯式碳交易机制与电制氢的综合能源系统热电优化[J]. 电力自动化设备, 2021, 41(9): 48-55.
	CHEN J P, HU Z J, CHEN Y G, et al. Thermoelectric optimization of integrated energy system considering ladder-type carbon trading mechanism and electric hydrogen production[J]. Electric Power Automation Equipment, 2021, 41(9): 48-55.
9	QUARTON C J, SAMSATLI S. Should we inject hydrogen into gas grids? Practicalities and whole-system value chain optimisation[J]. Applied Energy, 2020, 275: 115172.
10	邱玥, 周苏洋, 顾伟, 等. "碳达峰、碳中和" 目标下混氢天然气技术应用前景分析[J]. 中国电机工程学报, 2022, 42(4): 1301-1321.
	QIU Y, ZHOU S Y, GU W, et al. Application prospect analysis of hydrogen enriched compressed natural gas technologies under the target of carbon emission peak and carbon neutrality[J]. Proceedings of the CSEE, 2022, 42(4): 1301-1321.
11	NANTHAGOPAL K, SUBBARAO R, ELANGO T, et al. Hydrogen enriched compressed natural gas (HCNG): A futuristic fuel for internal combustion engines[J]. Thermal Science, 2011, 15(4): 1145-1154.
12	DE SANTOLI L, BASSO G L, BRUSCHI D. Energy characterization of CHP (combined heat and power) fuelled with hydrogen enriched natural gas blends[J]. Energy, 2013, 60: 13-22.
13	崔杨, 郭福音, 仲悟之, 等. 多重不确定性环境下的综合能源系统区间多目标优化调度[J]. 电网技术, 2022, 46(8): 2964-2975.
	CUI Y, GUO F Y, ZHONG W Z, et al. Interval multi-objective optimal dispatch of integrated energy system under multiple uncertainty environment[J]. Power System Technology, 2022, 46(8): 2964-2975.
14	邱革非, 何超, 骆钊, 等. 考虑源、荷不确定性的工业园区电-气互联综合能源系统模糊优化调度[J]. 电力自动化设备, 2022, 42(5): 8-14.
	QIU G F, HE C, LUO Z, et al. Fuzzy optimal scheduling of integrated electricity and natural gas system in industrial park considering source-load uncertainty[J]. Electric Power Automation Equipment, 2022, 42(5): 8-14.
15	VERÁSTEGUI F, LORCA Á, OLIVARES D E, et al. An adaptive robust optimization model for power systems planning with operational uncertainty[J]. IEEE Transactions on Power Systems, 2019, 34(6): 4606-4616.
16	单福州, 李晓露, 宋燕敏, 等. 基于改进两阶段鲁棒优化的区域综合能源系统经济调度[J]. 电测与仪表, 2018, 55(23): 103-108.
	SHAN F Z, LI X L, SONG Y M, et al. Economic dispatching of regional integrated energy system based on improvement two-stage robust optimization[J]. Electrical Measurement & Instrumentation, 2018, 55(23): 103-108.
17	LI Y, ZOU Y, TAN Y, et al. Optimal stochastic operation of integrated low-carbon electric power, natural gas, and heat delivery system[J]. IEEE Transactions on Sustainable Energy, 2018, 9(1): 273-283.
18	PAN G S, GU W, LU Y P, et al. Optimal planning for electricity-hydrogen integrated energy system considering power to hydrogen and heat and seasonal storage[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2662-2676.
19	NING C, YOU F Q. Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era[J]. Computers & Chemical Engineering, 2018, 111: 115-133.

设备	参数	数值
CHP	最小输出功率/kW	0
	最大输出功率/kW	2000
	最小掺氢比	0
	最大掺氢比	20%
	最小气体流量/(m³/h)	0
	最大气体流量/(m³/h)	20
	烟气转换效率	85%

氢能模块	最大功率/kW	1000
氢能模块	电解槽转换效率	90%

储氢罐	容量/m³	300

GB	最大产热功率/kW	1000
GB	最大耗气量	8730

储电设备	储电量/kW	720
储电设备	转换效率	95%

时段类型	时段	购电电价/(元/kWh)	售电电价/(元/kWh)
谷时	00:00—7:00 22:00—24:00	0.40	0.20
平时	07:00—11:00 14:00—18:00	0.75	0.40
峰时	11:00—14:00 18:00—22:00	1.20	0.60

迭代次数	迭代上界/元	迭代下界/元	收敛间隙
1	7555.12341	6195.01195	0.21955
2	7566.53691	7409.36638	0.02121
3	7572.63231	7563.16054	0.00125
4	7568.98818	7565.41972	4.71681E-4
5	7566.40209	7565.73077	8.87314E-5

不确定优化模型	求解时间/s	IES成本/元
随机优化模型^[17]	1239.85	6374.02
两阶段鲁棒模型^[18] (只考虑最优场景)	36.27	6872.35
两阶段鲁棒模型^[18] (只考虑最恶劣场景)	37.31	8153.42
忽略场景概率不确定性^[19] 的三阶段鲁棒模型	45.19	7254.61
本文提出的三阶段鲁棒模型	64.70	7565.42

[1]	Siyan LIU, Genxiang ZHONG, Qing GE. Hydrogen fuel cell stack on-line intelligent monitoring system based on FDC [J]. Energy Storage Science and Technology, 2024, 13(6): 2030-2038.
[2]	Hongwei KAN, Xiaobin WU, Liang HE, Xinjian ZHANG, Guanfei XING. Influence of operating conditions on CV test of low temperature PEM water electrolysis single cells and mechanism analysis [J]. Energy Storage Science and Technology, 2024, 13(5): 1653-1657.
[3]	Minjie BAO, Xiaoli YU, Rui HUANG, Junxuan CHEN, Xiaoyang CHEN, Wenbin ZHI. High-power fuel cell modeling and voltage uniformity analysis [J]. Energy Storage Science and Technology, 2024, 13(3): 952-962.
[4]	Yiqiang WANG, Luqiang LIU, Zhicheng ZHANG, Ruonan HUI. Feasibility of "West-to-East Hydrogen Transmission" through chemical hydrogen storage media [J]. Energy Storage Science and Technology, 2024, 13(3): 1050-1058.
[5]	Chenxi LIANG, Zhenbin WANG, Mingjin ZHANG, Cunhua MA, Ning LIANG. Research progress on magnesium-based solid hydrogen storage nanomaterials [J]. Energy Storage Science and Technology, 2024, 13(3): 788-824.
[6]	Qiquan ZENG, Maji LUO, Yinlong YANG, Qingze HUANG. Life prediction of fuel cells based on the LSTM-UPF hybrid method [J]. Energy Storage Science and Technology, 2024, 13(3): 963-970.
[7]	Na CHEN, Anqi LI, Zixiang GUO, Yuzhe ZHANG, Xue QIN. Research progress on the construction and optimization of Prussian blue material structure for sodium-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(11): 3340-3351.
[8]	Qili LIN, Hongxun QI, Jingjing HUANG, Bingcheng ZHANG, Zhen CHEN, Zhenkun XIAO. Levelized cost of combined hydrogen production by water electrolysis with alkaline-proton exchange membrane [J]. Energy Storage Science and Technology, 2023, 12(11): 3572-3580.
[9]	Shuang SONG, Fu LI, Xisheng TANG. Research progress on the safety-state assessment of lithium-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(11): 3545-3555.
[10]	Yi WANG, Xuebing CHEN, Yuanxi WANG, Jieyun ZHENG, Xiaosong LIU, Hong LI. Overview of multilevel failure mechanism and analysis technology of energy storage lithium-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(7): 2079-2094.
[11]	Mingrui LIU, Kai DING, Wei WANG, Jin SUN. Research progress of hydrogen storage materials based on physical adsorption [J]. Energy Storage Science and Technology, 2023, 12(6): 1804-1814.
[12]	Yubo QI, Da GAO, Xianling ZHENG. Economic analysis of SPE hydrogen production technology in China [J]. Energy Storage Science and Technology, 2022, 11(12): 4038-4047.
[13]	Han ZHOU, Zhengyu LI, Junhui XU, Liuping CHEN, Linghui GONG. Analyses and prospects for the coupled generation of renewable energy and the salt-cavern hydrogen-storage technology in China [J]. Energy Storage Science and Technology, 2022, 11(12): 4059-4066.
[14]	Yalin XIONG, Wei LIU, Pengbo GAO, Binqi DONG, Mingsheng ZHAO. Research on the hydrogen energy demand and carbon-reduction path in China's synthetic ammonia industry to achieve the “carbon peak” and “carbon neutrality” goals [J]. Energy Storage Science and Technology, 2022, 11(12): 4048-4058.
[15]	Bin XU, Rui WANG, Wei SU, Guangli HE, Ping MIAO. Research progress and prospect of key materials of proton exchange membrane water electrolysis [J]. Energy Storage Science and Technology, 2022, 11(11): 3510-3520.