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Examples

Examples#

In order to familiarize with the code or investigate the input and outputs of the rules, a notebooks 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