The Solution of Potential-Driven, Steady-State Nonlinear Network Flow Equations via Graph Partitioning

TL;DR

This paper introduces a graph partitioning-based algorithm for efficiently solving large-scale potential-driven steady-state nonlinear network flow equations, enabling decentralized computation and data sharing at interconnects.

Contribution

The paper presents a novel partitioning approach that allows solving nonlinear network flow equations locally within partitions, facilitating decentralized computation and data sharing.

Findings

Method connected to Schur complement

Viability demonstrated on challenging test cases

Enables decentralized network flow solutions

Abstract

The solution of potential-driven steady-state flow in large networks is required in various engineering applications, such as transport of natural gas or water through pipeline networks. The resultant system of nonlinear equations depends on the network topology, and its solution grows more challenging as the network size increases. We present an algorithm that utilizes a given partition of a network into tractable sizes to compute a global solution for the full nonlinear system through local solution of smaller subsystems induced by the partitions. When the partitions are induced by interconnects or transfer points corresponding to networks owned by different operators, the method ensures data is shared solely at the interconnects, leaving network operators free to solve the network flow system corresponding to their own domain in any manner of their choosing. The proposed method is shown to be connected to the Schur complement and the method's viability demonstrated on some challenging test cases.