Optimal Control of Transient Flows in Pipeline Networks with Heterogeneous Mixtures of Hydrogen and Natural Gas

TL;DR

This paper develops a control framework for managing transient flows of hydrogen-natural gas mixtures in pipeline networks, enabling efficient, safe, and flexible blending to reduce carbon emissions while utilizing existing infrastructure.

Contribution

It introduces a novel modeling approach for mixed gas flows with constituent mixing over time, and formulates a model predictive control method for optimal compressor operation.

Findings

The reduced model accurately approximates the original system.

Optimal control effectively minimizes energy use while maintaining pressure constraints.

Validation confirms the approach's feasibility on a test network.

Abstract

We formulate a control system model for the distributed flow of mixtures of highly heterogeneous gases through large-scale pipeline networks with time-varying injections of constituents, withdrawals, and control actions of compressors. This study is motivated by the proposed blending of clean hydrogen into natural gas pipelines as an interim means to reducing end use carbon emissions while utilizing existing infrastructure for its planned lifetime. We reformulate the partial differential equations for gas dynamics on pipelines and balance conditions at junctions using lumped elements to a sparse nonlinear differential algebraic equation system. Our key advance is modeling the mixing of constituents in time throughout the network, which requires doubling the state space needed for a single gas and increases numerical ill-conditioning. The reduced model is shown to be a consistent approximation of the original system, which we use as the dynamic constraints in a model-predictive optimal control problem for minimizing the energy expended by applying time-varying compressor operating profiles to guarantee time-varying delivery profiles subject to system pressure limits. The optimal control problem is implemented after time discretization using a nonlinear program, with validation of the results done using a transient simulation. We demonstrate the methodology for a small test network, and discuss scalability and potential applications.