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| 001 | 864324 | ||
| 005 | 20240708133017.0 | ||
| 024 | 7 | _ | |a 10.1016/j.ijhydene.2019.10.080 |2 doi |
| 024 | 7 | _ | |a 2128/23833 |2 Handle |
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| 100 | 1 | _ | |a Reuss, Markus |0 P:(DE-Juel1)168335 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Modeling Hydrogen Networks for Future Energy Systems: A Comparison of Linear and Nonlinear Approaches |
| 260 | _ | _ | |a New York, NY [u.a.] |c 2019 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Common energy system models that integrate hydrogen transport in pipelines typically simplify fluid flow models and reduce the network size in order to achieve solutions quickly. This contribution analyzes two different types of pipeline network topologies (namely, star and tree networks) and two different fluid flow models (linear and nonlinear) for a given hydrogen capacity scenario of electrical reconversion in Germany to analyze the impact of these simplifications. For each network topology, robust demand and supply scenarios are generated. The results show that a simplified topology, as well as the consideration of detailed fluid flow, could heavily influence the total pipeline investment costs. For the given capacity scenario, an overall cost reduction of the pipeline costs of 37% is observed for the star network with linear cost compared to the tree network with nonlinear fluid flow. The impact of these improvements regarding the total electricity reconversion costs has led to a cost reduction of 1.4%, which is fairly small. Therefore, the integration of nonlinearities into energy system optimization models is not recommended due to their high computational burden. However, the applied method for generating robust demand and supply scenarios improved the credibility and robustness of the network topology, while the simplified fluid flow consideration can lead to infeasibilities. Thus, we suggest the utilization of the nonlinear model for post-processing to prove the feasibility of the results and strengthen their credibility, while retaining the computational performance of linear modeling. |
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| 700 | 1 | _ | |a Welder, Lara |0 P:(DE-Juel1)165160 |b 1 |u fzj |
| 700 | 1 | _ | |a Thürauf, Johannes |0 P:(DE-Juel1)171366 |b 2 |
| 700 | 1 | _ | |a Linssen, Jochen |0 P:(DE-Juel1)130470 |b 3 |u fzj |
| 700 | 1 | _ | |a Grube, Thomas |0 P:(DE-Juel1)129852 |b 4 |u fzj |
| 700 | 1 | _ | |a Schewe, Lars |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Schmidt, Martin |0 P:(DE-HGF)0 |b 6 |
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| 700 | 1 | _ | |a Robinius, Martin |0 P:(DE-Juel1)156460 |b 8 |u fzj |
| 773 | _ | _ | |a 10.1016/j.ijhydene.2019.10.080 |g Vol. 44, no. 60, p. 32136 - 32150 |0 PERI:(DE-600)1484487-4 |n 60 |p 32136 - 32150 |t International journal of hydrogen energy |v 44 |y 2019 |x 0360-3199 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/864324/files/Modeling%20Hydrogen%20Networks%20for%20Future%20Energy%20Systems%20-%20submission.pdf |y Published on 2019-11-15. Available in OpenAccess from 2021-11-15. |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/864324/files/Modeling%20Hydrogen%20Networks%20for%20Future%20Energy%20Systems%20-%20submission.pdf?subformat=pdfa |x pdfa |y Published on 2019-11-15. Available in OpenAccess from 2021-11-15. |
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