Details
| Original language | English |
|---|---|
| Pages (from-to) | 131-164 |
| Number of pages | 34 |
| Journal | Optimization and engineering |
| Volume | 16 |
| Issue number | 1 |
| Publication status | Published - 18 Mar 2014 |
Abstract
Economic reasons and the regulation of gas markets create a growing need for mathematical optimization of natural gas networks. Real life planning tasks often lead to highly complex and extremely challenging optimization problems whose numerical treatment requires a breakdown into several simplified problems to be solved by carefully chosen hierarchies of models and algorithms. This paper presents stationary NLP type models of gas networks that are primarily designed to include detailed nonlinear physics in the final optimization steps for mid term planning problems after fixing discrete decisions with coarsely approximated physics.
Keywords
- Gas networks, High-detail modeling, Nonsmooth nonlinear discrete-continuous optimization, Smoothing techniques, Stationary flow
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Electrical and Electronic Engineering
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In: Optimization and engineering, Vol. 16, No. 1, 18.03.2014, p. 131-164.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - High detail stationary optimization models for gas networks
AU - Schmidt, Martin
AU - Steinbach, Marc C.
AU - Willert, Bernhard M.
N1 - Funding Information: This work has been supported by the German Federal Ministry of Economics and Technology owing to a decision of the German Bundestag. The responsibility for the content of this publication lies with the authors. We would also like to thank our industry partner Open Grid Europe GmbH and the project partners in the ForNe consortium; Friedrich-Alexander-Universität Erlangen-Nürnberg, Konrad Zuse Zentrum für Informationstechnik Berlin (ZIB), Universität Duisburg-Essen, Weierstraß Institut für Angewandte Analysis und Stochastik (WIAS), Humboldt Universität zu Berlin, and Technische Universität Darmstadt. At last, we are also very grateful to four anonymous referees, whose comments greatly helped to improve the quality of the paper.
PY - 2014/3/18
Y1 - 2014/3/18
N2 - Economic reasons and the regulation of gas markets create a growing need for mathematical optimization of natural gas networks. Real life planning tasks often lead to highly complex and extremely challenging optimization problems whose numerical treatment requires a breakdown into several simplified problems to be solved by carefully chosen hierarchies of models and algorithms. This paper presents stationary NLP type models of gas networks that are primarily designed to include detailed nonlinear physics in the final optimization steps for mid term planning problems after fixing discrete decisions with coarsely approximated physics.
AB - Economic reasons and the regulation of gas markets create a growing need for mathematical optimization of natural gas networks. Real life planning tasks often lead to highly complex and extremely challenging optimization problems whose numerical treatment requires a breakdown into several simplified problems to be solved by carefully chosen hierarchies of models and algorithms. This paper presents stationary NLP type models of gas networks that are primarily designed to include detailed nonlinear physics in the final optimization steps for mid term planning problems after fixing discrete decisions with coarsely approximated physics.
KW - Gas networks
KW - High-detail modeling
KW - Nonsmooth nonlinear discrete-continuous optimization
KW - Smoothing techniques
KW - Stationary flow
UR - http://www.scopus.com/inward/record.url?scp=84926278979&partnerID=8YFLogxK
U2 - 10.1007/s11081-014-9246-x
DO - 10.1007/s11081-014-9246-x
M3 - Article
AN - SCOPUS:84926278979
VL - 16
SP - 131
EP - 164
JO - Optimization and engineering
JF - Optimization and engineering
SN - 1389-4420
IS - 1
ER -