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https://github.com/fneum/import-benefits


https://github.com/fneum/import-benefits

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README 

        
# Energy Imports and Infrastructure in a Carbon-Neutral European Energy System



Code for preprint article [2404.03927](https://arxiv.org/abs/2404.03927) currently under review.



**Abstract:**



> Importing renewable energy to Europe offers many potential benefits, including reduced energy costs, lower pressure on infrastructure development, and less land-use within Europe. However, there remain many open questions: on the achievable cost reductions, how much should be imported, whether the energy vector should be electricity, hydrogen or hydrogen derivatives like ammonia or steel, and their impact on Europe's domestic energy infrastructure needs. This study integrates the TRACE global energy supply chain model with the sector-coupled energy system model for Europe PyPSA-Eur to explore scenarios with varying import volumes, costs, and vectors. We find system cost reductions of 1-14%, depending on assumed import costs, with diminishing returns for larger import volumes and a preference for methanol, steel and hydrogen imports. Keeping some domestic power-to-X production is beneficial for integrating variable renewables, utilising waste heat from fuel synthesis and leveraging local sustainable carbon sources. Our findings highlight the need for coordinating import strategies with infrastructure policy and reveal maneuvering space for incorporating non-cost decision factors.



## Installation



Clone the `git` repository from Github and enter the directory:



```sh

git clone --recurse-submodules [email protected]:fneum/import-benefits

cd import-benefits

```



Use `conda` or a similar alternative to install the necessary **Python environment**:



```sh

conda env create -f workflow/pypsa-eur/envs/environment.yaml -n pypsa-eur-imports

```



The installation process should take only a few minutes; if it takes longer, consider updating your `conda` installation before retrying to create the environment:



```sh

conda deactivate

conda update conda

```



Additionally, install a **solver** of your choice, e.g. Gurobi:



```sh

conda activate pypsa-eur-imports

conda install -c gurobi gurobi

```



For an environment specification with fixed versions, use `workflow/pypsa-eur/envs/environment.yaml`:



```sh

conda env create -f workflow/pypsa-eur/envs/environment.fixed.yaml -n pypsa-eur-imports-fixed

```



Finally, there is a data requirement that is not included in the repository and not downloaded automatically.



Download the following `zip` folder and unpack it into the `data` directory.



```sh

wget https://tubcloud.tu-berlin.de/s/Sfbd38DZTpTYpkb/download/import-benefits-data.zip

unzip import-benefits-data.zip -d workflow/pypsa-eur/data

```



The workflow can be executed on Linux, Mac or Windows. There are no futher specific non-standard hardware requirements.



## Usage



This repository uses the [`snakemake` workflow management tool](https://snakemake.readthedocs.io/en/stable/).



Apart from solving the optimisation problem of a high-resolution model configuration, the workflow can be executed on a local machine. The preprocessing can take 1-2 hours and may require 16-24 GB of RAM.



To run a test workflow resulting in a model with reduced temporal resolution with a runtime limited to around 2 hours so that it is suitable for local execution, execute the following commands from the `root` directory of the project:



```sh

conda activate pypsa-eur-imports

snakemake -call --configfile=config/config.small.yaml

```



To reproduce the model runs presented in the paper, execute the following commands from the `root` directory of the project:



```sh

conda activate pypsa-eur-imports

snakemake -call --configfile=config/config.20231025-zecm.yaml

```



These larger runs with higher temporal resolution require a high-performance computing setup with at least 128 GB RAM available and access to multiple cores is beneficial for runtime improvements but not strictly necessary. However, the runtime for a single scenario can exceed 48 hours. As there are around 200 scenarios, it is recommended to use the [`snakemake` cluster integration](https://snakemake.readthedocs.io/en/v7.19.1/executing/cluster.html) to parallelize scenarios.



The `yaml` files referenced above can be used to edit and configure the scenarios, e.g. by adding additional cases or changing some assumptions.



More detailed usage instructions for the underlying PyPSA-Eur model can be found

at https://pypsa-eur.readthedocs.io/.



## Outputs and Inspection



Running the workflow will create various intermediate resources and results files, including `.csv` files with summary data, `.pdf` files with summary plots and `.nc` files, most of which are PyPSA network files before or after solving.



These files are placed in the `workflow/pypsa-eur/resources` and `workflow/pypsa-eur/results` directories.



PyPSA networks can be read and inspected with Python in the following way:



```py

import pypsa

fn = "workflow/pypsa-eur/results/small/postnetworks/elec_s_110_lvopt__Co2L0-365H-T-H-B-I-S-A-imp_2050.nc"

n = pypsa.Network(fn)

n.statistics()

n.statistics.energy_balance()

n.buses_t.marginal_price

```



## License



[MIT](LICENSE)