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DOE OSTI · 3377279

SimH 2 : an integrated techno-economic modeling framework for hydrogen pipeline infrastructure and network optimization

Abstract

Large-scale hydrogen (H 2 ) pipeline transport design and network optimization have seldom been reported due to the lack of a cost model accounting for the relationship between transport cost and hydrogen mass flow rate. Here, this work introduced a system-level cost model for hydrogen pipeline transport at supercritical state and integrated it with an existing CO 2 pipeline network tool, SimCCS, for hydrogen-specific pipeline design and optimization. The Intermountain West (I-West) region of the U.S., historically dependent on fossil fuel-based economies, is chosen to demonstrate the capabilities of our H 2 pipeline cost model and transport network optimization platform called SimH 2 . Two scenarios are examined: one where the pipeline is not allowed to pass through disadvantaged communities and the other where it is permitted. The results highlight that incorporating disadvantaged-community constraints lead to longer pipeline routes and increased transport costs, reflecting the trade-offs involved in equitable infrastructure development. It is demonstrated that the newly developed SimH 2 tool not only enables the efficient design of H 2 transportation pipelines but also optimizes the network by accounting for local terrain and the presence of disadvantaged areas.

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Wang, Min [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000244542480), Ma, Zhiwei [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Singapore Institute of Technology (Singapore)] (ORCID:0000000331388207), Mehana, Mohamed Zakaria Saad [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000324722879), Chen, Bailian [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000336558340). 2026-10-15. SimH 2 : an integrated techno-economic modeling framework for hydrogen pipeline infrastructure and network optimization. https://doi.org/10.1016/j.enconman.2026.121855

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