Fast and Reliable Transient Simulation and Continuous Optimization of Large-Scale Gas Networks

TL;DR

This paper introduces a fast, reliable simulation and optimization framework for large-scale gas networks, enabling efficient intra-day gas flow management and near-term forecasting of system changes.

Contribution

It presents a novel combination of hierarchical physical models, semiconvex approximations, and a discrete adjoint approach with SQP for improved gas network simulation and optimization.

Findings

Accelerates computation of gas flow forecasts.

Enables economic intra-day gas schedule control.

Demonstrates effectiveness on real pipeline case studies.

Abstract

We are concerned with the simulation and optimization of large-scale gas pipeline systems in an error-controlled environment. The gas flow dynamics is locally approximated by sufficiently accurate physical models taken from a hierarchy of decreasing complexity and varying over time. Feasible work regions of compressor stations consisting of several turbo compressors are included by semiconvex approximations of aggregated characteristic fields. A discrete adjoint approach within a first-discretize-then-optimize strategy is proposed and a sequential quadratic programming with an active set strategy is applied to solve the nonlinear constrained optimization problems resulting from a validation of nominations. The method proposed here accelerates the computation of near-term forecasts of sudden changes in the gas management and allows for an economic control of intra-day gas flow schedules in large networks. Case studies for real gas pipeline systems show the remarkable performance of the new method.