Modelling of hydrogen blending into the UK natural gas network driven by a solid oxide fuel cell for electricity and district heating system

被引:16
作者
Samanta, Samiran [1 ]
Roy, Dibyendu [1 ]
Roy, Sumit [1 ]
Smallbone, Andrew [1 ]
Roskilly, Anthony Paul [1 ]
机构
[1] Univ Durham, Dept Engn, Durham DH1 3LE, England
关键词
Solid Oxide Fuel Cell (SOFC); District heating; Hydrogen economy; Artificial Neural Network; Cogeneration; PERFORMANCE-EMISSION CHARACTERISTICS; ENERGY; POWER; IMPACT; SOFC; IDENTIFICATION; COMBUSTION; MIXTURES;
D O I
10.1016/j.fuel.2023.129411
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
A thorough investigation of the thermodynamics and economic performance of a cogeneration system based on solid oxide fuel cells that provides heat and power to homes has been carried out in this study. Additionally, different percentages of green hydrogen have been blended with natural gas to examine the techno-economic performance of the suggested cogeneration system. The energy and exergy efficiency of the system rises steadily as the hydrogen blending percentage rises from 0% to 20%, then slightly drops at 50% H2 blending, and then rises steadily again until 100% H2 supply. The system's minimal levelised cost of energy was calculated to be 4.64 & POUND;/kWh for 100% H2. Artificial Neural Network (ANN) model was also used to further train a sizable quantity of data that was received from the simulation model. Heat, power, and levelised cost of energy estimates using the ANN model were found to be extremely accurate, with coefficients of determination of 0.99918, 0.99999, and 0.99888, respectively.
引用
收藏
页数:11
相关论文
共 54 条
[1]   Hydrogen as an energy vector [J].
Abdin, Zainul ;
Zafaranloo, Ali ;
Rafiee, Ahmad ;
Merida, Walter ;
Lipinski, Wojciech ;
Khalilpour, Kaveh R. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2020, 120
[2]   Steady state analysis of gas networks with distributed injection of alternative gas [J].
Abeysekera, M. ;
Wu, J. ;
Jenkins, N. ;
Rees, M. .
APPLIED ENERGY, 2016, 164 :991-1002
[3]   Soft computing analysis of a compressed air energy storage and SOFC system via different artificial neural network architecture and tri-objective grey wolf optimization [J].
Alirahmi, Seyed Mojtaba ;
Mousavi, Seyedeh Fateme ;
Ahmadi, Pouria ;
Arabkoohsar, Ahmad .
ENERGY, 2021, 236 (236)
[4]  
[Anonymous], 2021, HYDROGEN INSIGHTS 20
[5]  
[Anonymous], 2022, Storage Water Tanks Cost
[6]   Climate change mitigation and the role of technological change: Impact on selected headline targets of Europe's 2020 climate and energy package [J].
Bel, Germa ;
Joseph, Stephan .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 82 :3798-3807
[7]   Life cycle environmental impact comparison of solid oxide fuel cells fueled by natural gas, hydrogen, ammonia and methanol for combined heat and power generation [J].
Bicer, Yusuf ;
Khalid, Farrukh .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2020, 45 (05) :3670-3685
[8]   A combined heat and green hydrogen (CHH) generator integrated with a heat network [J].
Burrin, Dominic ;
Roy, Sumit ;
Roskilly, Anthony Paul ;
Smallbone, Andrew .
ENERGY CONVERSION AND MANAGEMENT, 2021, 246
[9]   Economic analysis of CO2 capture from natural gas combined cycles using Molten Carbonate Fuel Cells [J].
Campanari, S. ;
Chiesa, P. ;
Manzolini, G. ;
Bedogni, S. .
APPLIED ENERGY, 2014, 130 :562-573
[10]   An experimental based ANN approach in mapping performance emission characteristics of a diesel engine operating in dual-fuel mode with LPG [J].
Chakraborty, Amitav ;
Roy, Sumit ;
Banerjee, Rahul .
JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING, 2016, 28 :15-30