Complementarity-Based Nonlinear Programming Techniques for Optimal Mixing in Gas Networks

Other publication type


Publication Details

Author(s): Hante F, Schmidt M
Publication year: 2017
Language: English


Abstract

We consider nonlinear and nonsmooth mixing aspects in gas transport
optimization problems. As mixed-integer reformulations of pooling-type
mixing models already render small-size instances computationally
intractable, we investigate the applicability of smooth nonlinear
programming techniques for equivalent complementarity-based
reformulations. Based on recent results for remodeling piecewise affine
constraints using an inverse parametric quadratic programming approach,
we show that classical stationarity concepts are meaningful for the
resulting complementarity-based reformulation of the mixing equations.
Further, we investigate in a numerical study the performance of this
reformulation compared to a more compact complementarity-based one that
does not feature such beneficial regularity properties. All computations
are performed on publicly available data of real-world size problem
instances from steady-state gas transport.


FAU Authors / FAU Editors

Hante, Falk PD Dr.
Lehrstuhl für Angewandte Mathematik
Schmidt, Martin Prof. Dr.
Juniorprofessur für Optimierung von Energiesystemen


How to cite

APA:
Hante, F., & Schmidt, M. (2017). Complementarity-Based Nonlinear Programming Techniques for Optimal Mixing in Gas Networks.

MLA:
Hante, Falk, and Martin Schmidt. Complementarity-Based Nonlinear Programming Techniques for Optimal Mixing in Gas Networks. 2017.

BibTeX: 

Last updated on 2018-08-08 at 18:54