Machine Learning for Improved Gas Network Models in Coordinated Energy Systems

TL;DR

This paper introduces a neural-network-constrained optimization approach to model non-convex natural gas flow dynamics within integrated power and gas systems, enhancing accuracy and computational efficiency.

Contribution

It proposes a novel neural network-based regression of the Weymouth equation integrated into a mixed-integer linear program for better gas flow modeling.

Findings

Improved accuracy in gas flow modeling

Enhanced computational efficiency with reformulated activation functions

Effective application to real-life Belgian energy systems

Abstract

The current energy transition promotes the convergence of operation between the power and natural gas systems. In that direction, it becomes paramount to improve the modeling of non-convex natural gas flow dynamics within the coordinated power and gas dispatch. In this work, we propose a neural-network-constrained optimization method which includes a regression model of the Weymouth equation, based on supervised machine learning. The Weymouth equation links gas flow to inlet and outlet pressures for each pipeline via a quadratic equality, which is captured by a neural network. The latter is encoded via a tractable mixed-integer linear program into the set of constraints. In addition, our proposed framework is capable of considering bidirectionality without having recourse to complex and potentially inaccurate convexification approaches. We further enhance our model by introducing a reformulation of the activation function, which improves the computational efficiency. An extensive numerical study based on the real-life Belgian power and gas systems shows that the proposed methodology yields promising results in terms of accuracy and tractability.