Maximizing the storage capacity of gas networks: a global MINLP approach

作     者：Burlacu, Robert Egger, Herbert Gross, Martin Martin, Alexander Pfetsch, Marc E. Schewe, Lars Sirvent, Mathias Skutella, Martin 

作者机构：Friedrich Alexander Univ Erlangen Nurnberg FAU Discrete Optimizat Cauerstr 11 D-91058 Erlangen Germany Tech Univ Darmstadt Res Grp Numer & Sci Comp Dolivostr 15 D-64293 Darmstadt Germany Rhein Westfal TH Aachen Chair Management Sci Kackertstr 7 D-52072 Aachen Germany Tech Univ Darmstadt Res Grp Optimizat Dolivostr 15 D-64293 Darmstadt Germany Tech Univ Berlin Fac Math & Nat Sci 2 Str 17 Juni 136 D-10623 Berlin Germany 

出 版 物：《OPTIMIZATION AND ENGINEERING》 (最优化与工程学)

年 卷 期：2019年第20卷第2期

页      面：543-573页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0701[理学-数学] 

基　　金：Erlangen Regional Computing Center (RRZE) 

主　　题：Mixed-integer nonlinear programming Transient gas transport optimization Storage capacity maximization Power-to-gas First-discretize-then-optimize 

摘      要：In this paper, we study the transient optimization of gas networks, focusing in particular on maximizing the storage capacity of the network. We include nonlinear gas physics and active elements such as valves and compressors, which due to their switching lead to discrete decisions. The former is described by a model derived from the Euler equations that is given by a coupled system of nonlinear parabolic partial differential equations (PDEs). We tackle the resulting mathematical optimization problem by a first-discretize-then-optimize approach. To this end, we introduce a new discretization of the underlying system of parabolic PDEs and prove well-posedness for the resulting nonlinear discretized system. Endowed with this discretization, we model the problem of maximizing the storage capacity as a non-convex mixed-integer nonlinear problem (MINLP). For the numerical solution of the MINLP, we algorithmically extend a well-known relaxation approach that has already been used very successfully in the field of stationary gas network optimization. This method allows us to solve the problem to global optimality by iteratively solving a series of mixed-integer problems. Finally, we present two case studies that illustrate the applicability of our approach.