AI & ML
Quantifying Linepack Storage Potential of Hydrogen Networks
arXiv eess.SY0 views
arXiv:2610.40822v1 Announce Type: new Abstract: Future hydrogen systems depend on large-scale storage to balance supply and demand. Hydrogen networks can store energy in the pipelines themselves, but the potential of this linepack storage has not been quantified comparably to dedicated storage facilities. We transfer metrics established for conventional storage, such as volume capacity and full-cycle equivalents, to hydrogen networks. To evaluate them, we formulate an optimization problem maximizing linepack storage utilization, subject to non-convex PDE-based constraints describing transient gas flow. We solve the optimization problem over a full-year horizon to capture seasonal variations and to assess the interaction with cavern storage. For computational tractability, we propose a simulation-informed sequential convex programming approach with adaptive time stepping that reduces problem size by up to 98% at accuracy comparable to fixed-resolution methods. For the GasLib-40 topology with German scenario data, the network provides a volume capacity of 24 million normal cubic meters at 28 full-cycle equivalents per year.
Source:https://arxiv.org/abs/2610.40822PreviousCo-optimization for Adaptive Conformal PredictionNextDynamic Persuasion: Managing Reputation under Censored Feedback
Related posts

Raindrop simulates your agent’s world to catch what your changes break
0The New Stack
Rechargeable battery prototype runs on vitamin B12
0Phys.org Tech
Quoting Paul Ford
0Simon Willison

Etsy is finally cracking down on AI in product listings
0TechRadar

How to watch Traitors: Igra Na Predateli season 2 online – stream the Bulgarian spin-off from anywhere
0TechRadar

The MacBook Pro PROLED XDR 14-inch is my favorite MacBook, ever — here's why
0TechRadar