Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach
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Abstract (arXiv)
Gas network planning optimization under emission constraints prioritizes gas supply with the least CO2 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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References · 15
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- 2020Decreasing market value of variable renewables can be avoided by policy actioncited by 1
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