/ARO-GasInfrastructure

Input data and source code for the paper "European Gas Infrastructure Expansion Planning: An Adaptive Robust Optimization Approach"

Primary LanguageGAMS

Input data and source code for the paper "European Gas Infrastructure Expansion Planning: An Adaptive Robust Optimization Approach"

I. Riepin, M. Schmidt, L. Baringo, and F. Müsgens (2021). in review

The European natural gas market is experiencing structural and fundamental market changes, increasing uncertainty on the supply and demand side. These uncertainties pose questions about the value of strategic infrastructure investments, e.g., the Projects of Common Interest, to ensure supply security. This paper evaluates which of these infrastructure projects are valuable to maintaining system resiliency considering cold-winter demand spikes, supply shortages and availability of investment budget. Additionally, we test whether any other infrastructure expansion projects would be valuable from a system perspective. Methodologically, this paper demonstrates an application of adaptive robust optimization approach to the European gas infrastructure planning problem. The methodology proves its suitability to capture the long-term uncertainty and to identify the critical elements in the gas system.

Keywords:

Capacity planning, gas infrastructure, robust optimization, uncertainty

Links:

Working paper link soon.

The code reproduces the benchmarks from the paper

Note that model output files are uploaded into 'results' folder. The folder contains the results used in the paper in the GAMS Data eXchange (GDX) format

Citing us

The ARO model used in our paper is free: you can access, modify and share it under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. This model is shared in the hope that it will be useful for further research on topics of robust optimization and gas transmission network expantion, but without any warranty of merchantability or fitness for a particular purpose.

If you use the model or its components for your research, we would appreciate it if you would cite us as follows:

@article{
working paper
}