Complementarity-based nonlinear programming techniques for optimal mixing in gas networks
Hante F, Schmidt M (2019)
Publication Type: Journal article
Publication year: 2019
Journal
DOI: 10.1007/s13675-019-00112-w
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.
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Germany (DE)How to cite
APA:
Hante, F., & Schmidt, M. (2019). Complementarity-based nonlinear programming techniques for optimal mixing in gas networks. EURO Journal on Computational Optimization. https://doi.org/10.1007/s13675-019-00112-w
MLA:
Hante, Falk, and Martin Schmidt. "Complementarity-based nonlinear programming techniques for optimal mixing in gas networks." EURO Journal on Computational Optimization (2019).
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