resite

This file provides the information required to set up the resite tool. Also, it provides a minimal example for siting RES assets.

Setup

This module runs in any 3.x version of Python. The Python packages required to run this module are listed in the requirements.yml file included in the repository. Their installation can be easily achieved via the the following command that builds a separate environment for this module from the yml file:

conda env create -f environment.yml

In addition, the tool also requires the installation of the pypsa module (details here) for model development purposes which are detailed in the models.py script. Since one of the latest versions of this module is required, its installation has to be carried out manually via

pip install git+https://github.com/PyPSA/PyPSA.git

Lastly, the tool requires to installation of an MILP solver. At the time of writing, gurobi is the only solver this model supports (see installation details here.

At the time of writing, the model readily supports (given the availability of already downloaded data here and the set-up within tools.py) siting assessments in Europe, North Africa, Middle East, Greenland and the USA.

Example run

The module structure includes five folders. The input data folder contains all data required at different stages within the model. The output_data folder contains the result sub-folders. The src_acquisition folder contains the script used for reanalysis data download from the ECMWF ERA5 database. The src folder contains the scripts running the resite model. Finally, the src_postprocessing folder includes scripts used for analysing results.

I. Resource data retrieval

It should be noted that usable data (incl. resource data, land characteristics, etc.) are already available here. We recommend using this data to get a good understanding of the model architecture and cababilities. In case additional data is needed, the acquisition procedure detailed below should suffice.

1. Set up access to the CDS API by following instructions here.
2. Open the data_retrieval.py file in src_acquisition using any file editor or IDE available and adjust the download settings as required (e.g., area, time coverage, spatial resolution, variables).
3. Run data_retrieval.py. This will commence a connection to the CDS servers and data download will start. According to the amount of data requested, this process takes time. Downloaded files will be saved in the input_data/resource_data folder in a subfolder named after the spatial resolution chosen.

II. Optimal deployment of generation sites

1. Open the parameters.yml file using any available text editor or IDE and adjust the model parameters according to the needs. Further comments on how to set these are provided in the file. Save the file.
2. Run main.py. Upon completion, the output is saved in a dedicated sub-folder (named based on its creation time stamp) in output_data.

III. Results assessment

Run output.py in src_postprocessing. In console, define an instance of the Output class associated to a run, e.g. 20190101_123000, as such:

instance = Output('20190101_123000')

The instance has the following attributes:

• return_numerics(), which returns a series of scalar indicators associated with i) the optimal deployment scheme and ii) a couple other siting strategies. It should be called as:

results = instance.return_numerics(**kwargs)

• plot_numerics(), which provides a visual representation of the results obtained above. It is called as:

instance.plot_numerics(results, **kwargs)

• optimal_location_plot(), which plots the optimal sites on a map. It is called as:

instance.optimal_locations_plot()

• optimal_locations_plot_heatmaps(), which plots RES capacities per node (if capacities are considered)

instance.optimal_locations_plot_heatmaps()

Citing

Please cite the following paper if you use this software in your research.

• D. Radu, M. Berger, R. Fonteneau, A. Dubois, H. Pandžić, Y. Dvorkin, Q. Louveaux, D. Ernst, Resource Complementarity as Criterion for Siting Renewable Energy Generation Assets, pre-print, 2019.

License

This software is released as free software under the GPLv3.