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

Emission-Aware Optimization of Gas Networks   This repository collects the Belgium gas network dataset, details on the training procedure, and codes to replicate the results reported in the following paper:   *Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach *   accepted for presentation at the 62nd IEEE Conference on Decision and Control, Dec. 13-15, 2023, Singapore.   Materials are released with the Attribution 4.0 International (CC BY 4.0) license.   The repository contains two folders: * operation_planning folder containing data and codes for neural network training and operation planning optimization  * long_term_planning folder containing data and codes for neural network training and long-term planning optimization    The models are implemented in Julia-1.6 Language, using JuMP.jl using Flux.jl library for machine learning and JuMP.jl library for mathematical programming. Before running the code, make sure to activate the virtual environment from Project.toml files stored in each folder, e.g., by running  julia> ] (@v1.6) pkg> activate . (operation_planning) pkg> instantiate For experiment settings, refer to exp_settings dictionary in file main.jl. For network data, refer to .../data/case_BE.