pyGRETA, pyCLARA, pyPRIMA: A pre-processing suite to generate flexible model regions for energy system models

Abstract

This paper presents a combination of three pre-processing tools that allow energy system modelers to define the number and shape of their model regions flexibly. Firstly, weather reanalysis data and other geographic maps are combined in pyGRETA to downscale wind and solar data and obtain renewable energy potential maps in high spatial resolution, while pyPRIMA can provide the spatial distribution of the energy demand and a pre-processed network of transmission lines. Secondly, the raster maps and the transmission grid are fed into pyCLARA to obtain a shapefile of regions with homogeneous characteristics. Thirdly, the obtained shapefile is used in pyGRETA to generate representative time series of renewable power generation, and in pyPRIMA to pre-process the rest of the data (power plants, demand, grid, etc.) to prepare input files for model frameworks. The three tools have a similar software architecture and are available in GitHub with an open source license and a detailed description. A minimal working example shows how they can operate together to ensure a high degree of modeling flexibility.

Keywords

• Energy system modeling
• Spatial complexity
• Clustering
• Pre-processing