Author
Listed:
- Chen, Yiyu
- Lu, Junming
- Huo, Yongchen
- Xu, Jianghai
- Liu, Yuanli
- Zhan, Zizhang
- Huang, Taiming
- Wan, Zhongmin
Abstract
Under the “dual carbon” goals, biomass energy, as a carbon-neutral renewable energy source, offers significant benefits in gasification technology. However, its fuel composition exhibits substantial variability, posing a severe challenge to the adaptability of energy systems. This research, based on the principle of thermal self-balance, designed a hybrid power generation system combining SOFC, GT, and SE, with high fuel tolerance. Key factors affecting carbon deposits and system performance were found using sensitivity analysis. The improved multi-objective red fox optimization algorithm was used to optimize the system's equipment capacity and operating parameters from the perspectives of energy efficiency, environmental impact, and economics. Results indicated rated energy efficiency at 70.7 % and exergy efficiency at 68.04 %, with improvements of 15.71 % and 15.07 %. The IIC were reduced by 18.86 %, and EIP decreased by 11.79 %. Power matching under variable loads was performed leveraging high fuel tolerance. This revealed the intrinsic mechanism by which fuel components, through their influence on reforming reactions, electrochemical processes, and thermal balance, drive the redistribution of power across components, leading to an optimized power curve. The LCA revealed that operating with natural gas until 2045 yielded a NPV of 0.0489$/kW, LCOE of 1050($'000), and IIR of 0.2366, showing strong economic benefits. Biogas and biomass gas, based on the “carbon neutrality” principle, had carbon emissions of only 26 and 36 g/kWh, with EIL values as low as 238 and 314, highlighting their low-carbon advantages.
Suggested Citation
Chen, Yiyu & Lu, Junming & Huo, Yongchen & Xu, Jianghai & Liu, Yuanli & Zhan, Zizhang & Huang, Taiming & Wan, Zhongmin, 2026.
"A high-fuel tolerance SOFC-GT-SE hybrid power generation system considering the impact of carbon deposition: techno-economic analysis and multi-objective optimization,"
Energy, Elsevier, vol. 345(C).
Handle:
RePEc:eee:energy:v:345:y:2026:i:c:s0360544226002860
DOI: 10.1016/j.energy.2026.140184
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