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Reduction of a district heating model using network decomposition
Author(s) -
Rein Markus,
Mohring Jan,
Damm Tobias,
Klar Axel
Publication year - 2019
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201900038
Subject(s) - subnetwork , projection (relational algebra) , reduction (mathematics) , galerkin method , model order reduction , computer science , nonlinear system , mathematical optimization , dimension (graph theory) , decomposition , exploit , state space , transformation (genetics) , electric power system , algorithm , mathematics , power (physics) , physics , ecology , biochemistry , statistics , geometry , computer security , chemistry , quantum mechanics , gene , pure mathematics , biology
In this contribution we study model order reduction of nonlinear transport networks for district heating systems. In these, heating energy injected at a centralized power plant is transported to consumers. They are modeled by a hyperbolic differential algebraic system with large state space dimension. The network structure introduces sparse system dynamics, which transform to a dense reduced system leading to unacceptable computational costs [1]. To exploit the benefits of sparsity, sub‐parts of the network are reduced separately in a structure preserving way using Galerkin projection [2]. After introducing the dynamical model, we discuss a decomposition strategy which aims at minimizing the number of observables for each subnetwork. We demonstrate its numerical benefits at an existing large scale heating network.