Dynamic simulation of GEH-IES with distributed parameter characteristics for hydrogen-blending transportation

  • Dengji ZHOU , 1 ,
  • Jiarui HAO 2 ,
  • Wang XIAO 3 ,
  • Chen WANG 2 ,
  • Chongyuan SHUI 2 ,
  • Xingyun JIA 2 ,
  • Siyun YAN 2
Expand
  • 1. Key Laboratory of Power Machinery and Engineering of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China; Sichuan Research Institute, Shanghai Jiao Tong University, Chengdu 610042, China
  • 2. Key Laboratory of Power Machinery and Engineering of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
  • 3. West Pipeline Company, National Petroleum and Natural Gas Pipe Network Group Co., Ltd., Urumqi 830012, China; Institute of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830046, China
ZhouDJ@sjtu.edu.cn

Received date: 11 Jul 2023

Accepted date: 10 Oct 2023

Published date: 15 Aug 2024

Copyright

2023 Higher Education Press 2023

Abstract

For the purpose of environment protecting and energy saving, renewable energy has been distributed into the power grid in a considerable scale. However, the consuming capacity of the power grid for renewable energy is relatively limited. As an effective way to absorb the excessive renewable energy, the power to gas (P2G) technology is able to convert excessive renewable energy into hydrogen. Hydrogen-blending natural gas pipeline is an efficient approach for hydrogen transportation. However, hydrogen-blending natural gas complicates the whole integrated energy system (IES), making it more problematic to cope with the equipment failure, demand response and dynamic optimization. Nevertheless, dynamic simulation of distribution parameters of gas–electricity–hydrogen (GEH) energy system, especially for hydrogen concentration, still remains a challenge. The dynamics of hydrogen-blending IES is undiscovered. To tackle the issue, an iterative solving framework of the GEH-IES and a cell segment-based method for hydrogen mixing ratio distribution are proposed in this paper. Two typical numerical cases studying the conditions under which renewables fluctuate and generators fail are conducted on a real-word system. The results show that hydrogen blending timely and spatially influences the flow parameters, of which the hydrogen mixing ratio and gas pressure loss along the gas pipeline are negatively correlated and the response to hydrogen mixing ratio is time-delayed. Moreover, the hydrogen-blending amount and position also have a significant impact on the performance of the compressor.

Cite this article

Dengji ZHOU , Jiarui HAO , Wang XIAO , Chen WANG , Chongyuan SHUI , Xingyun JIA , Siyun YAN . Dynamic simulation of GEH-IES with distributed parameter characteristics for hydrogen-blending transportation[J]. Frontiers in Energy, 2024 , 18(4) : 506 -524 . DOI: 10.1007/s11708-023-0914-4

Acknowledgements

This work was supported by the Science and Technology Department of Ningxia Hui Autonomous Region, China (Grant No. 2022ZDYF1483) and Chinese–German Center for Research Promotion (Grant No. GZ1577).

Competing interests

The authors declare that they have no competing interests.
1
Wang C, Lv C, Li P. . Modeling and optimal operation of community IESs: A case study from China. Applied Energy, 2018, 230: 1242–1254

DOI

2
Qin C, Yan Q, He G. IESs planning with electricity, heat and gas using particle swarm optimization. Energy, 2019, 188: 116044

DOI

3
Huang D, Zhou D, Jia X. . A mixed integer optimization method with double penalties for the complete consumption of renewable energy in distributed energy systems. Sustainable Energy Technologies and Assessments, 2022, 52: 102061

DOI

4
Sinsel S R, Riemke R L, Hoffmann V H. Challenges and solution technologies for the integration of variable renewable energy sources—A review. Renewable Energy, 2020, 145: 2271–2285

DOI

5
ReynoldsRSlagerW. Pipeline transportation of hydrogen. In: Nejat Veziroğlu T, ed. Hydrogen Energy. New York: Springer, 1975, 533–543

6
Mohammad A K, Sumeray C, Richmond M. . Assessing the sustainability of liquid hydrogen for future hypersonic aerospace flight. Aerospace, 2022, 9(12): 801

DOI

7
Meng B, Guo F, Hu L. . Wind abandonment analysis of multi-energy systems considering gas–electricity coupling. Electric Power Engineering Technology, 2019, 38(6): 2–8 (in Chinese)

8
Haeseldonckx D, D’haeseleer W. The use of the natural gas pipeline infrastructure for hydrogen transport in a changing market structure. International Journal of Hydrogen Energy, 2007, 32(10–11): 1381–1386

DOI

9
Hanley E S, Deane J, Gallachóir B Ó. The role of hydrogen in low carbon energy futures—A review of existing perspectives. Renewable & Sustainable Energy Reviews, 2018, 82: 3027–3045

DOI

10
Smit R, Weeda M, De Groot A. Hydrogen infrastructure development in The Netherlands. International Journal of Hydrogen Energy, 2007, 32(10–11): 1387–1395

DOI

11
Saedi I, Mhanna S, Mancarella P. Integrated electricity and gas system modelling with hydrogen injections and gas composition tracking. Applied Energy, 2021, 303: 117598

DOI

12
Zhao J, Chen L, Wang Y. . A review of system modeling, assessment and operational optimization for IESs. Science China. Information Sciences, 2021, 64(9): 1–23

DOI

13
Shen F, Ju P, Shahidehpour M. . Singular perturbation for the dynamic modeling of IESs. IEEE Transactions on Power Systems, 2020, 35(3): 1718–1728

DOI

14
Ma S, Sun S, Wu H. . Decoupling optimization of IES based on energy quality character. Frontiers in Energy, 2018, 12(4): 540–549

DOI

15
Tabkhi F, Azzaro-Pantel C, Pibouleau L. . A mathematical framework for modelling and evaluating natural gas pipeline networks under hydrogen injection. International Journal of Hydrogen Energy, 2008, 33(21): 6222–6231

DOI

16
Agaie B, Khan I, Yacoob Z. . A novel technique of reduce order modelling without static correction for transient flow of non-isothermal hydrogen–natural gas mixture. Results in Physics, 2018, 10: 532–540

DOI

17
Guandalini G, Colbertaldo P, Campanari S. Dynamic modeling of natural gas quality within transport pipelines in presence of hydrogen injections. Applied Energy, 2017, 185: 1712–1723

DOI

18
Guandalini G, Colbertaldo P, Campanari S. Dynamic quality tracking of natural gas and hydrogen mixture in a portion of natural gas grid. Energy Procedia, 2015, 75: 1037–1043

DOI

19
Cheli L, Guzzo G, Adolfo D. . Steady-state analysis of a natural gas distribution network with hydrogen injection to absorb excess renewable electricity. International Journal of Hydrogen Energy, 2021, 46(50): 25562–25577

DOI

20
Elaoud S, Hadj-Taïeb E. Transient flow in pipelines of high-pressure hydrogen–natural gas mixtures. International Journal of Hydrogen Energy, 2008, 33(18): 4824–4832

DOI

21
Liu B, Liu S, Guo S. . Economic study of a large-scale renewable hydrogen application utilizing surplus renewable energy and natural gas pipeline transportation in China. International Journal of Hydrogen Energy, 2020, 45(3): 1385–1398

DOI

22
ITMPower. HyDeploy: UK gas grid injection of hydrogen in full operation. 2020-6-1, available at the website of ITM Power

23
Suzuki T, Kawabata S, Tomita T. Present status of hydrogen transport systems utilizing existing natural gas supply infrastructures in Europe and the USA. The Institute of Energy Economics (Japan) Report, 2006

24
TiekstraGKoopmanF. The NATURALHY project: First step in assessing the potential of the existing natural gas network for hydrogen delivery. In: International Gas Union Research Conference, Paris, France, 2008

25
Colbertaldo P, Guandalini G, Campanari S. Modelling the integrated power and transport energy system: The role of power-to-gas and hydrogen in long-term scenarios for Italy. Energy, 2018, 154: 592–601

DOI

26
Fang R. Life cycle cost assessment of wind power–hydrogen coupled IES. International Journal of Hydrogen Energy, 2019, 44(56): 29399–29408

DOI

27
Pan G, Gu W, Qiu H. . Bi-level mixed-integer planning for electricity–hydrogen IES considering levelized cost of hydrogen. Applied Energy, 2020, 270: 115176

DOI

28
He J, Wu Y, Wu M. . Two-stage configuration optimization of a novel standalone renewable IES coupled with hydrogen refueling. Energy Conversion and Management, 2022, 251: 114953

DOI

29
Fang R. Multi-objective optimized operation of IES with hydrogen storage. International Journal of Hydrogen Energy, 2019, 44(56): 29409–29417

DOI

30
Li N, Zhao X, Shi X. . IESs with CCHP and hydrogen supply: A new outlet for curtailed wind power. Applied Energy, 2021, 303: 117619

DOI

31
Hajimiragha A, Canizares C, Fowler M, et al. Optimal energy flow of IESs with hydrogen economy considerations. In: 2007 iREP Symposium—Bulk Power System Dynamics and Control-VII. Revitalizing Operational Reliability. Charleston, SC, USA, 2007

32
Liu J, Xu Z, Wu J. . Optimal planning of distributed hydrogen-based multi-energy systems. Applied Energy, 2021, 281: 116107

DOI

33
Zhou D, Yan S, Huang D. . Modeling and simulation of the hydrogen blended gas−electricity IES and influence analysis of hydrogen blending modes. Energy, 2022, 239: 121629

DOI

34
Liu J, Sun W, Harrison G P. The economic and environmental impact of power to hydrogen/power to methane facilities on hybrid power–natural gas energy systems. International Journal of Hydrogen Energy, 2020, 45(39): 20200–20209

DOI

35
Chen X, Ma L, Zhou C. . Improved resistance to hydrogen environment embrittlement of warm-deformed 304 austenitic stainless steel in high-pressure hydrogen atmosphere. Corrosion Science, 2019, 148: 159–170

DOI

36
Zhou D, Li T, Huang D. . The experiment study to assess the impact of hydrogen blended natural gas on the tensile properties and damage mechanism of X80 pipeline steel. International Journal of Hydrogen Energy, 2021, 46(10): 7402–7414

DOI

37
Zhou C, Ye B, Song Y. . Effects of internal hydrogen and surface-absorbed hydrogen on the hydrogen embrittlement of X80 pipeline steel. International Journal of Hydrogen Energy, 2019, 44(40): 22547–22558

DOI

38
Hardie D, Charles E, Lopez A. Hydrogen embrittlement of high strength pipeline steels. Corrosion Science, 2006, 48(12): 4378–4385

DOI

39
WeiTZhouDYaoQ, . Simulation model uncertainty quantification and model calibration for natural gas compressor units. In: ASME International Mechanical Engineering Congress and Exposition, Salt Lake City, Utah, USA, 2019

40
MaSZhouDZhangH, . Modeling and optimal operation of a network of energy hubs system with distributed energy resources. In: Turbo Expo: Power for Land, Sea, and Air, Charlotte, North Carolina, USA, 2017

41
Wang C, Yan S, Shao R, et al. Coordinative optimization operation on the gas-electricity IESs. In: 2021 6th Asia Conference on Power and Electrical Engineering, Chongqing, China, 2021

42
Yan S, Li T, Huang D, et al. Dynamic modeling and simulation of the large-scale regional integrated electricity and natural gas system. In: 2020 4th International Conference on Smart Grid and Smart Cities, Osaka, Japan, 2020

43
YangDYanSZhouD, . Reinforcement learning methods on optimization problems of natural gas pipeline networks. In: 2020 4th International Conference on Smart Grid and Smart Cities, Osaka, Japan, 2020

44
Liu C, Shahidehpour M, Wang J. Coordinated scheduling of electricity and natural gas infrastructures with a transient model for natural gas flow. Chaos, 2011, 21(2): 025102

DOI

45
Kiuchi T. An implicit method for transient gas flows in pipe networks. International Journal of Heat and Fluid Flow, 1994, 15(5): 378–383

DOI

46
Zhou D, Huang D, Hao J. . Vibration-based fault diagnosis of the natural gas compressor using adaptive stochastic resonance realized by generative adversarial networks. Engineering Failure Analysis, 2020, 116: 104759

DOI

47
Zhou D, Wei T, Huang D. . Reliability assessment based on multisource information fusion method for high pressure natural gas compressors. Journal of Pressure Vessel Technology, 2021, 143(3): 031702

DOI

48
Zhou D, Zhang H, Li Y G. . A dynamic reliability-centered maintenance analysis method for natural gas compressor station based on diagnostic and prognostic technology. Journal of Engineering for Gas Turbines and Power, 2016, 138(6): 061601

DOI

49
Pan Z, Guo Q, Sun H. Interactions of district electricity and heating systems considering time-scale characteristics based on quasi-steady multi-energy flow. Applied Energy, 2016, 167: 230–243

DOI

50
Dormand J R, Prince P J. A family of embedded Runge-Kutta formulae. Journal of Computational and Applied Mathematics, 1980, 6(1): 19–26

DOI

51
Liu K, Biegler L T, Zhang B. . Dynamic optimization of natural gas pipeline networks with demand and composition uncertainty. Chemical Engineering Science, 2020, 215: 115449

DOI

52
MohringJHoffmannJHalfmannT, . Automated model reduction of complex gas pipeline networks. In: PSIG Annual Meeting, California, USA, 2004

53
Bagajewicz M, Valtinson G. Computation of natural gas pipeline hydraulics. Industrial & Engineering Chemistry Research, 2014, 53(26): 10707–10720

DOI

54
Zhou D, Ma S, Huang D. . An operating state estimation model for IESs based on distributed solution. Frontiers in Energy, 2020, 14(4): 801–816

DOI

Outlines

/