tum-ens / pyGRETA

python Generator of REnewable Time series and mAps

python Generator of REnewable Time series and mAps: a tool that generates high-resolution potential maps and time series for user-defined regions within the globe.

Features

• 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

Applications

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 do not need to exclude unsuitable areas - use the Global Solar Atlas or Global Wind Atlas
• You only need time series for specific points - use other webtools such as Renewables.ninja
• You only need time series for administrative divisions (countries, NUTS-2, etc.), for which such data is readily available - see Renewables.ninja or EMHIRES

Outputs

Potential maps for solar PV and onshore wind in Australia, using weather data for 2015:

Contributors ✨

Thanks goes to these wonderful people (emoji key):

kais-siala 💬 🐛 💻 📖 🤔 🚧 👀 ⚠️ 📢	HoussameH 💬 💻 📖	Pierre Grimaud 🐛	thushara2020 👀	lodersky 📖 💻 👀	sonercandas 📖	patrick-buchenberg 📦
molarana 🎨

This project follows the all-contributors specification. Contributions of any kind welcome!

Please cite as:

Kais Siala, & Houssame Houmy. (2020, June 1). tum-ens/pyGRETA: python Generator of REnewable Time series and mAps (Version v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.3727416