Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach

• URL: http://arxiv.org/abs/2209.08645v1
• Date: Sun, 18 Sep 2022 20:08:24 GMT
• Title: Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach
• Authors: Vladimir Dvorkin, Samuel Chevalier, Spyros Chatzivasileiadis
• Abstract summary: Gas network planning optimization under emission constraints prioritizes gas supply with the least CO$$ intensity. ICNN-aided optimization provides a feasible solution to network planning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gas network planning optimization under emission constraints prioritizes gas supply with the least CO$_2$ intensity. As this problem includes complex physical laws of gas flow, standard optimization solvers cannot guarantee convergence to a feasible solution. To address this issue, we develop an input-convex neural network (ICNN) aided optimization routine which incorporates a set of trained ICNNs approximating the gas flow equations with high precision. Numerical tests on the Belgium gas network demonstrate that the ICNN-aided optimization dominates non-convex and relaxation-based solvers, with larger optimality gains pertaining to stricter emission targets. Moreover, whenever the non-convex solver fails, the ICNN-aided optimization provides a feasible solution to network planning.

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