python Generator of REnewable Time series and mAps¶
- Code developers
Kais Siala, Houssame Houmy
- Documentation authors
Kais Siala, Houssame Houmy, Sergio Alejandro Huezo Rodriguez
Kais Siala <email@example.com>
Chair of Renewable and Sustainable Energy Systems, Technical University of Munich
Apr 20, 2022
Generation of potential maps and time series for user-defined regions within the globe
Modeled technologies: onshore wind, offshore wind, PV, CSP (user-defined technology characteristics)
Use of MERRA-2 reanalysis data, with the option to detect and correct outliers
High resolution potential taking into account the land use suitability/availability, topography, bathymetry, slope, distance to urban areas, etc.
Statistical reports with summaries (available area, maximum capacity, maximum energy output, etc.) for each user-defined region
Generation of several time series for each technology and region, based on user’s preferences
Possibility to combine the time series into one using linear regression to match given full-load hours and temporal fluctuations
This code is useful if:
You want to estimate the theoretical and/or technical potential of an area, which you can define through a shapefile
You want to obtain high resolution maps
You want to define your own technology characteristics
You want to generate time series for an area after excluding parts of it that are not suitable for renewable power plants
You want to generate multiple time series for the same area (best site, upper 10%, median, lower 25%, etc.)
You want to match historical capacity factors of countries from the IRENA database
You do not need to use the code (but you can) if:
You only need time series for specific points - use other webtools such as Renewables.ninja
Fixed the syntax of the code in the PV module tracking (lib.physical_models).
Edited the formatting of the PDF documentation.
Edited the list of references.
This is the initial version.
These documents give a general overview and help you get started from the installation to your first running model.
- User manual
- Recommended input sources
- Weather data from MERRA-2
- Raster of Mean Wind Speed
- Raster of land use
- Shapefile of the region of interest
- Shapefile of countries
- Shapefile of Exclusive Economic Zones (EEZ)
- Shapefile of Internal Waters
- Raster of topography / elevation data
- Raster of bathymetry
- Shapefile of protected areas
- Airports Coordinates
- Shapefiles from OSM data
- Raster of Settlement Footprint
- Shapefile of HydroLakes
- Shapefile of HydroRivers
- Data of Crop Production
- Data of Forestry Production
- Shapefile of Livestock density
- Recommended workflow
Continue here if you want to understand the concept of the model.
A list of the used libraries is available in the environment file:
name: ren_ts channels: - defaults - conda-forge dependencies: - pip=19.3.1 - pip: - pyproj=2.4.1 - fiona=1.8.4 - gdal=2.3.3 - geopandas=0.4.1 - h5netcdf=0.7.4 - hdf5storage=0.1.15 - numpy=1.17.3 - pandas=0.25.2 - pyomo=5.6.7 - python=3.7.5 - python-dateutil=2.8.1 - psutil=5.6.5 - rasterio=1.0.21 - scipy=1.3.1 - shapely=1.6.4 prefix: D:\Miniconda3\envs\ren_ts