The curious case of the conflicting roles of hydrogen in global energy scenarios
Quarton, C. J.; Tlili, O.; Welder, L.; Mansilla, C.; Blanco, H.; Heinrichs, H.; Leaver, Jonathan; Samsatli, NJ.; Lucchese, P.; Robinius, M.; Samsatli, S.
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Citation:Quarton, CJ., Tlili, O., Welder, L., Mansilla, C., Blanco, H., Heinrichs, H., Leaver, J., Samsatli, NJ., Lucchese, P., Robinius, M., & Samsatli, S. (2019). The curious case of the conflicting roles of hydrogen in global energy scenarios. Sustainable Energy and Fuels, 9 Oct, 1-17. doi:10.1039/C9SE00833K
Permanent link to Research Bank record:https://hdl.handle.net/10652/4797
As energy systems transition from fossil-based to low-carbon, they face many challenges, particularly concerning energy security and flexibility. Hydrogen may help to overcome these challenges, with potential as a transport fuel, for heating, energy storage, conversion to electricity, and in industry. Despite these opportunities, hydrogen has historically had a limited role in influential global energy scenarios. Whilst more recent studies are beginning to include hydrogen, the role it plays in different scenarios is extremely inconsistent. In this perspective paper, reasons for this inconsistency are explored, considering the modelling approach behind the scenario, scenario design, and data assumptions. We argue that energy systems are becoming increasingly complex, and it is within these complexities that new technologies such as hydrogen emerge. Developing a global energy scenario that represents these complexities is challenging, and in this paper we provide recommendations to help ensure that emerging technologies such as hydrogen are appropriately represented. These recommendations include: using the right modelling tools, whilst knowing the limits of the model; including the right sectors and technologies; having an appropriate level of ambition; and making realistic data assumptions. Above all, transparency is essential, and global scenarios must do more to make available the modelling methods and data assumptions used