atlite 0.0.4

Library for fetching and converting weather data to power systems data

Atlite is a free software, xarray-based Python library for converting weather data (such as wind speeds, solar radiation, temperature and runoff) into power systems data (such as wind power, solar power, hydro power and heating demand time series). It is designed to work with big datasets, such as hourly global weather data over several years at spatial resolutions down to e.g. 0.1 x 0.1 degree resolution.

Atlite was originally conceived as a light-weight version of the Aarhus University RE Atlas, which produces wind and solar generation time series from historical reanalysis data. It has since been extended to use weather datasets simulated with projected climate change and to compute other time series, such as hydro power, solar thermal collectors and heating demand.

Atlite is designed to be modular, so that it can work with any weather datasets. It currently has modules for the following datasets:

• NCEP Climate Forecast System hourly historical reanalysis weather data available on a 0.2 x 0.2 degree global grid

• EURO-CORDEX Climate Change Projection three-hourly up until 2100, available on a 0.11 x 0.11 degree grid for Europe

• ECMWF ERA5 hourly historical reanalysis weather data on an approximately 0.25 x 0.25 deg global grid

• CMSAF SARAH-2 half-hourly historical surface radiation on a 0.05 x 0.05 deg grid available for Europe and Africa (automatically interpolated to a 0.2 deg grid and combined with ERA5 temperature).

It can process the following weather data fields:

• Temperature

• Downward short-wave radiation

• Upward short-wave radiation

• Wind

• Runoff

• Surface roughness

• Height maps

• Soil temperature

The following power-system relevant time series can be produced for all possible spatial distributions of assets:

• Wind power generation for a given turbine type

• Solar PV power generation for a given panel type

• Solar thermal collector heat output

• Hydroelectric inflow (simplified)

• Heating demand (based on the degree-day approximation)

Citation for Aarhus University RE Atlas: G. B. Andresen, A. A. Søndergaard, M. Greiner, “Validation of danish wind time series from a new global renewable energy atlas for energy system analysis,” Energy 93, Part 1 (2015) 1074 – 1088. doi:http://dx.doi.org/10.1016/j.energy.2015.09.071.

Atlite was initially developed by the Renewable Energy Group at FIAS to carry out simulations for the CoNDyNet project, financed by the German Federal Ministry for Education and Research (BMBF) as part of the Stromnetze Research Initiative.

Getting started

• Install atlite from conda-forge or pypi.

• Download one of the weather datasets listed above (ERA5 is downloaded automatically on-demand after the ECMWF cdsapi<https://cds.climate.copernicus.eu/api-how-to> client is properly installed)

• Adjust the atlite/config.py directory paths to point to the directory where you downloaded the dataset

• Create a cutout, i.e. a geographical rectangle and a selection of times, e.g. all hours in 2011 and 2012, to narrow down the scope - see examples/create_cutout.py

• Select a sparse matrix of the geographical points inside the cutout you want to aggregate for your time series, and pass it to the appropriate converter function - see examples/

Licence

Copyright 2016-2017 Gorm Andresen (Aarhus University), Jonas Hörsch (FIAS), Tom Brown (FIAS), Markus Schlott (FIAS), David Schlachtberger (FIAS)

This program (atlite) is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.