Optimal Control for Scheduling and Pricing Intra-day Natural Gas Transport on Pipeline Networks

TL;DR

This paper develops an optimal control framework for natural gas pipeline networks that maximizes economic welfare while respecting physical and operational constraints, using advanced discretization and model predictive control techniques.

Contribution

It introduces a novel, efficient computational approach combining lumped element modeling and a rolling-horizon scheme for large-scale, transient gas pipeline optimization.

Findings

Effective control of compressor stations and regulators achieved.

Economic values of gas at network locations derived from dual functions.

Validated methodology on a realistic pipeline network case study.

Abstract

We formulate an economic optimal control problem for transport of natural gas over a large-scale transmission pipeline network under transient flow conditions. The objective is to maximize economic welfare for users of the pipeline system, who provide time-dependent price and quantity bids to purchase or supply gas at metered locations on a system with time-varying injections, withdrawals, and control actions of compressors and regulators. Our formulation ensures that pipeline hydraulic limitations, compressor station constraints, operational factors, and pre-existing contracts for gas transport are satisfied. A pipeline is modeled as a metric graph with gas dynamics partial differential equations on edges and coupling conditions at the nodes. These dynamic constraints are reduced using lumped elements to a sparse nonlinear differential algebraic equation system. A highly efficient temporal discretization scheme for time-periodic formulations is introduced, which we extend to develop a rolling-horizon model-predictive control scheme. We apply the computational methodology to a pipeline system test network case study. In addition to the physical flow and compressor control solution, the optimization yields dual functions that we interpret as the time-dependent economic values of gas at each location in the network.