Stochastic Finite Volume Method for Uncertainty Management in Gas Pipeline Network Flows

TL;DR

This paper introduces a stochastic finite volume method for efficiently managing uncertainty in gas pipeline network flows, enabling probabilistic optimization and analysis of physical and economic variables under demand variability.

Contribution

It develops a novel stochastic finite volume approach for hyperbolic PDEs on graphs, integrating uncertainty quantification with optimization and economic interpretation.

Findings

Method successfully models uncertainty in steady-state gas flows.

Dual variables interpreted as price distributions reveal financial volatility.

Validated on single-pipe and small network examples.

Abstract

Natural gas consumption by users of pipeline networks is subject to increasing uncertainty that originates from the intermittent nature of electric power loads serviced by gas-fired generators. To enable computationally efficient optimization of gas network flows subject to uncertainty, we develop a finite volume representation of stochastic solutions of hyperbolic partial differential equation (PDE) systems on graph-connected domains with nodal coupling and boundary conditions. The representation is used to express the physical constraints in stochastic optimization problems for gas flow allocation subject to uncertain parameters. The method is based on the stochastic finite volume approach that was recently developed for uncertainty quantification in transient flows represented by hyperbolic PDEs on graphs. In this study, we develop optimization formulations for steady-state gas flow over actuated transport networks subject to probabilistic constraints. In addition to the distributions for the physical solutions, we examine the dual variables that are produced by way of the optimization, and interpret them as price distributions that quantify the financial volatility that arises through demand uncertainty modeled in an optimization-driven gas market mechanism. We demonstrate the computation and distributional analysis using a single-pipe example and a small test network.