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

  • 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.

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Author:Falk Hante, Martin Schmidt
Document Type:Preprint
Date of Publication (online):2017/09/12
Date of first Publication:2017/09/12
Release Date:2017/09/12
Tag:Complementarity constraints; Gas transport networks; Inverse parametric quadratic programming; MPCC; Mixing
Institutes:Friedrich-Alexander-Universität Erlangen-Nürnberg