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Examples

In order to familiarize with the code or investigate the input and outputs of the rules, a notebook folder, as part of the documentation repository has been provided. In order to work with these notebooks, you need to set up the recommended folder structure for pypsa-earth:

Start by creating a folder named pypsa-earth-project by executing the following line in the terminal:

mkdir pypsa-earth-project

Change the current directory to that folder:

cd pypsa-earth-project

Clone pypsa-earth repository from GitHub into the folder:

git clone https://github.com/pypsa-meets-earth/pypsa-earth.git

Finally Clone documentation repository into the folder:

git clone https://github.com/pypsa-meets-earth/documentation.git

This setup helps you to start working with the following notebooks:

  • sample_network_analysis: introduces a sample analysis for the networks built with pypsa-earth tutorial, gives you a taste of what pypsa-earth can do
  • network_comparison: compares the network models developed along the data workflow; useful and interactive plots are generated
  • osm_build_network_plot: provides specific plots and outputs for the download_osm_data rule
  • osm_data_access: explains how OSM data are being loaded by using download_osm_data
  • osm_powermap: contains nice plots and description of the output of the data downloaded and cleaned by using download_osm_data and clean_osm_data
  • solve_network_results: provides useful plots and textual outputs to investigate the results of the last optimization performed using solve_network
  • build_bus_regions: it explores the inputs and outputs of the build_bus_regions rule, namely the bus regions shapes, and the elements of the network (lines, substations, etc.)
  • build_shapes: describes the shapes created using the rule build_shapes for the on-shore and off-shore areas
  • demand_gegis: it enables exploring th GeGIS dataset used to perform the analysis. These data are obtained using the GlobalEnergyGIS package for Africa.
  • shape_comparison: this notebook enables comparing the shapes used along the data workflow
  • add_electricity: it analyzes the outputs of the add_electricity rule, including the PyPSA model and the RES/demand inputs
  • base_network: it eases the visualization and analysis of the output PyPSA network model that the rule base_network builds
  • build_cutout: the notebook analyzes the outputs of the rule `build_cutout`` rule, which are the solar, wind and hydro time series generated with Atlite
  • build_renewable_profiles: it enables investigating the specific time series generated by the rule build_renewable_profiles; in particular, it shows the potential of selected resources (e.g. solar) and the corresponding time series of renewable energy production available for selected buses
  • landuse-availability: this notebook aims at showing how Atlite accounts for land constraints in the analysis