TY - JOUR A1 - Gugat, Martin A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Sirvent, Mathias A1 - Wintergerst, David T1 - MIP-Based Instantaneous Control of Mixed-Integer PDE-Constrained Gas Transport Problems JF - Computational Optimization and Applications N2 - We study the transient optimization of gas transport networks including both discrete controls due to switching of controllable elements and nonlinear fluid dynamics described by the system of isothermal Euler equations, which are partial differential equations in time and 1-dimensional space. This combination leads to mixed-integer optimization problems subject to nonlinear hyperbolic partial differential equations on a graph. We propose an instantaneous control approach in which suitable Euler discretizations yield systems of ordinary differential equations on a graph. This networked system of ordinary differential equations is shown to be well-posed and affine-linear solutions of these systems are derived analytically. As a consequence, finite-dimensional mixed-integer linear optimization problems are obtained for every time step that can be solved to global optimality using general-purpose solvers. We illustrate our approach in practice by presenting numerical results on a realistic gas transport network. KW - Mixed-integer optimal control KW - Instantaneous control KW - Partial differential equations on graphs KW - Gas networks KW - Mixed-integer linear optimization Y1 - 2017 U6 - https://doi.org/10.1007/s10589-017-9970-1 VL - 70 IS - 1 SP - 267 EP - 294 ER - TY - JOUR A1 - Adelhütte, Dennis A1 - Aßmann, Denis A1 - Gonzàlez Grandòn, Tatiana A1 - Gugat, Martin A1 - Heitsch, Holger A1 - Liers, Frauke A1 - Henrion, René A1 - Nitsche, Sabrina A1 - Schultz, Rüdiger A1 - Stingl, Michael A1 - Wintergerst, David T1 - Joint model of probabilistic/robust (probust) constraints applied to gas network optimization N2 - Optimization tasks under uncertain conditions abound in many real-life applications. Whereas solution approaches for probabilistic constraints are often developed in case the uncertainties can be assumed to follow a certain probability distribution, robust approaches are usually used in case solutions are sought that are feasible for all realizations of uncertainties within some pre-defined uncertainty set. As many applications contain different types of uncertainties that require robust as well as probabilistic treatments, we deal with a class of joint probabilistic/robust constraints as its appears in optimization problems under uncertainty. Focusing on complex uncertain gas network optimization problems, we show the relevance of this class of problems for the task of maximizing free booked capacities in an algebraic model for a stationary gas network. We furthermore present approaches for their solution. Finally, we study the problem of controlling a transient system that is governed by the wave equation. The task consists in determining controls such that a certain robustness measure remains below some given upper bound, with high probability. KW - robust optimization KW - chance constraints KW - optimal control KW - spheric-radial decomposition Y1 - 2017 U6 - https://doi.org/10.1007/s10013-020-00434-y ER - TY - INPR A1 - Wintergerst, David T1 - Application of Chance Constrained Optimization to Gas Networks N2 - We consider optimization problems with a joint probabilistic constraint under normally distributed uncertain parameters. The parametric constraints are replaced by one constraint stating that the probability of being feasible shall exceed or be equal to a prescribed threshold. In order to apply the concept to gas network optimization under uncertain boundary flows, which corresponds to the demand of customers, we derive an analytic gradient formula. The integral corresponding to the probability can be parameterized by spherical radial decomposition. For this parameterization gradient formulas are known under convexity assumptions of the parametric constraints in the parameter. For the application in gas networks that we have in mind, the convexity assumption of the parametric constraints is not satisfied. Therefore, we weaken it to convexity of the region of feasible parameters for a fixed optimization variable. We proceed to show that the assumptions needed for the gradient formula are met in the gas network optimization problem on a tree. For the numerical implementation we propose a multilevel sampling algorithm that uses a coarse approximation of the chance constraint to generate a warm start for the expensive approximation with fine sampling. The numerical results illustrate that this approach significantly reduces the computation time. KW - chance constraint KW - spherical radial decomposition KW - isothermal Euler equations KW - gas networks KW - multilevel Y1 - 2017 ER - TY - JOUR A1 - Gugat, Martin A1 - Wintergerst, David A1 - Schultz, Rüdiger ED - Iske, Armin T1 - Networks of pipelines for gas with nonconstant compressibility factor: stationary states JF - Computational and Applied Mathematics N2 - For the management of gas transportation networks, it is essential to know how the stationary states of the system are determined by the boundary data. The isothermal Euler equations are an accurate pde-model for the gas flow through each pipe. A compressibility factor is used to model the nonlinear relationship between density and pressure that occurs in real gas in contrast to ideal gas. The gas flow through the nodes is governed by algebraic node conditions that require the conservation of mass and the continuity of the pressure. We examine networks that are described by arbitrary finite graphs and show that for suitably chosen boundary data, subsonic stationary states exist and are uniquely determined by the boundary data. Our construction of the stationary states is based upon explicit representations of the stationary states on each single pipe that can easily be evaluated numerically. We also use the monotonicity properties of these states as functions of the boundary data. Y1 - 2016 U6 - https://doi.org/10.1007/s40314-016-0383-z ER - TY - INPR A1 - Wintergerst, David A1 - Gugat, Martin T1 - Finite Time Blow-up of Traveling Wave Solutions for the Flow of Real Gas through Pipeline Networks N2 - In the context of gas transportation, analytical solutions are essential for the understanding of the underlying dynamics described by a system of partial differential equations. We derive traveling wave solutions for the 1-d isothermal Euler equations. A non-constant compressibility factor is used to describe the correlation between density and pressure. The blow-up of the traveling wave solution in � finite time is proven. We then extend our analysis to networks under appropriate coupling conditions and derive compatibility conditions to fulfill these coupling conditions. KW - isothermal Euler equations KW - real gas KW - finite time blow-up KW - traveling waves KW - networks Y1 - 2016 ER - TY - JOUR A1 - Gugat, Martin A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Sirvent, Mathias A1 - Wintergerst, David T1 - Towards Simulation Based Mixed-Integer Optimization with Differential Equations JF - Networks N2 - We propose a decomposition based method for solving mixed-integer nonlinear optimization problems with “black-box” nonlinearities, where the latter, e.g., may arise due to differential equations or expensive simulation runs. The method alternatingly solves a mixed-integer linear master problem and a separation problem for iteratively refining the mixed-integer linear relaxation of the nonlinear equalities. The latter yield nonconvex feasible sets for the optimization model but we have to restrict ourselves to convex and monotone constraint functions. Under these assumptions, we prove that our algorithm finitely terminates with an approximate feasible global optimal solution of the mixed integer nonlinear problem. Additionally, we show the applicability of our approach for three applications from optimal control with integer variables, from the field of pressurized flows in pipes with elastic walls, and from steady-state gas transport. For the latter we also present promising numerical results of our method applied to real-world instances that particularly show the effectiveness of our method for problems defined on networks. KW - Mixed-Integer Optimization KW - Simulation Based Optimization KW - Optimization with Differential Equations KW - Decomposition Method KW - Gas Transport Networks Y1 - 2018 U6 - https://doi.org/10.1002/net.21812 ER -