% Encoding: UTF-8
@COMMENT{BibTeX export based on data in FAU CRIS: https://cris.fau.de/}
@COMMENT{For any questions please write to cris-support@fau.de}
@inproceedings{faucris.289827009,
abstract = {In the transition to renewable energy sources, hydrogen will potentially play an important role for energy storage. The efficient transport of this gas is possible via pipelines. An understanding of the possibilities to control the gas flow in pipelines is one of the main building blocks towards the optimal use of gas. For the operation of gas transport networks it is important to take into account the randomness of the consumers' demand, where often information on the probability distribution is available. Hence in an efficient optimal control model the corresponding probability should be included and the optimal control should be such that the state that is generated by the optimal control satisfies given state constraints with large probability. We comment on the modelling of gas pipeline flow and the problems of optimal nodal control with random demand, where the aim of the optimization is to determine controls that generate states that satisfy given pressure bounds with large probability. We include the H^2 norm of the control as control cost, since this avoids large pressure fluctuations which are harmful in the transport of hydrogen since they can cause embrittlement of the pipeline meta},
author = {Schuster, Michael and Gugat, Martin},
booktitle = {Extended Abstracts presented at the 25th International Symposium on Mathematical Theory of Networks and Systems MTNS 2022},
date = {2022-09-12/2022-09-16},
doi = {10.15495/EPub{\_}UBT{\_}00006809},
editor = {Baumann, Michael Heinrich; Grüne, Lars; Jacob, Birgit; Worthmann, Karl},
faupublication = {yes},
keywords = {gas pipeline flow, nodal control, boundary control, optimal control, hyperbolic differential equation, random demand, state constraints, pressure bound, classical solutions},
peerreviewed = {unknown},
title = {{Max}-p {Optimal} {Boundary} {Control} of {Gas} {Flow}},
venue = {Bayreuth},
year = {2022}
}