atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series

Abstract

Renewable energy sources are likely to build the backbone of the future global energy system. One important key to a successful energy transition is to analyse the weather-dependent energy outputs of existing and eligible renewable resources. atlite is an open Python software package for retrieving global historical weather data and converting it to power generation potentials and time series for renewable energy technologies like wind turbines or solar photovoltaic panels based on detailed mathematical models. It further provides weather-dependent output on the demand side like building heating demand and heat pump performance. The software is optimized to aggregate data over multiple large regions with user-defined weightings based on land use or energy yield. Statement of needDeriving weather-based time series and maximum capacity potentials for renewables over large regions is a common problem in energy system modelling. Websites with exposed open APIs such as renewables.ninja ) exist for such purpose but are difficult to use for local execution, e.g. in cluster environments, and restricted to non-commercial use. Further, by design, they neither expose the underlying datasets nor methods for deriving time series, here referred to as conversion functions/methods. This makes them unsuited for utilizing different weather datasets or exploring alternative conversion functions. The pvlib (Holmgren et al., 2018) is suited for local execution and allows interchangeable input data but is specialized to PV systems only and intended for single location modelling. Other packages like the Danish REatlas (Andresen et al., 2015) face obstacles with accessibility, are based on proprietary code, miss documentation and are restricted in flexibility regarding their inputs.

Hourly historical and near-future weather and climate variables for energy system modelling

“…A number of automated tools have now also been developed to convert gridded meteorological data into time se-ries of renewable generation (e.g. atlite Hofmann et al, 2021, andpvlib Holmgren et al, 2018).…”

Beyond cost reduction: improving the value of energy storage in electricity systems

“…Such constraints help to implement social, environmental or physical based boundary conditions. Atlite is one of the tools that are implemented in PyPSA to quantify for instance the land availability for solar and wind power plants by incorporating protected areas and land coverage classification data to reduce the renewable installation potential [73].…”

Overcoming the disconnect between energy system and climate modeling

“…With respect to synchronicity, many historical datasets include wind and solar resource data, which can be paired with historical electricity demand and hydropower time series. To process historical datasets, energy system modellers leverage widely-used, open-source code and/or tools (e.g., ECEM [73], Atlas [74], atlite [75], and SAM [76]) that convert meteorological data, e.g. wind speeds, into energy system model inputs, e.g.…”