Machine Learning for Improved Gas Network Models in Coordinated Energy Systems
This work addresses the need for improved gas network models in coordinated energy systems, representing an incremental advancement by enhancing computational efficiency and accuracy over existing methods.
The authors tackled the challenge of modeling non-convex natural gas flow dynamics in coordinated power and gas dispatch by proposing a neural-network-constrained optimization method that encodes the Weymouth equation via a tractable mixed-integer linear program, achieving promising results in accuracy and tractability based on a real-life Belgian case study.
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.