A multi-scale energy systems (MUSES) modeling framework
Calliope is a framework to develop energy system models using a modern and open source Python-based toolchain. It is under active development and freely available under the Apache 2.0 license.
Calliope is a framework to develop energy system models, with a focus on flexibility, high spatial and temporal resolution, the ability to execute many runs based on the same base model, and a clear separation of framework (code) and model (data).
A model based on Calliope consists of a collection of text files (in YAML and CSV formats) that define the technologies, locations and resource potentials. Calliope takes these files, constructs an optimization problem, solves it, and reports results in the form of Pandas data structures for easy analysis with Calliope’s built-in tools or the standard Python data analysis stack.
Calliope’s main features include:
- Generic technology definition allows modeling any mix of production, storage and consumption
- Resolved in space: define locations with individual resource potentials
- Resolved in time: read time series with arbitrary resolution
- Model specification in an easy-to-read and machine-processable YAML format
- Able to run on computing clusters
- Easily extensible in a modular way: custom constraint generator functions and custom time mask functions
- Uses a state-of-the-art Python toolchain based on Pyomo and Pandas
- Freely available under the Apache 2.0 license
Comments, bug reports, pull requests, and any other contributions are welcome. See below (Contact) for contact information.
Please cite the following paper if you use Calliope for academic research:
Stefan Pfenninger (2017). Dealing with multiple decades of hourly wind and PV time series in energy models: a comparison of methods to reduce time resolution and the planning implications of inter-annual variability. Applied Energy. doi: 10.1016/j.apenergy.2017.03.051
The following publications make use of Calliope-based models:
- Stefan Pfenninger (2017). Dealing with multiple decades of hourly wind and PV time series in energy models: a comparison of methods to reduce time resolution and the planning implications of inter-annual variability. Applied Energy. doi: 10.1016/j.apenergy.2017.03.051
- Paula Díaz Redondo and Oscar Van Vliet (2016). Modelling the Energy Future of Switzerland after the Phase Out of Nuclear Power Plants. Energy Procedia. doi: 10.1016/j.egypro.2015.07.843
- Stefan Pfenninger and James Keirstead (2015). Comparing concentrating solar and nuclear power as baseload providers using the example of South Africa. Energy. doi: 10.1016/j.energy.2015.04.077
- Stefan Pfenninger and James Keirstead (2015). Renewables, nuclear, or fossil fuels? Comparing scenarios for the Great Britain electricity system. Applied Energy, 152, pp. 83-93. doi: 10.1016/j.apenergy.2015.04.102
Contact Stefan Pfenninger if you have a publication that should be listed here.