A multi-scale energy systems (MUSES) modeling framework | www.callio.pe
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 Calliope model consists of a collection of text files (in YAML and CSV formats) that fully define a model, with details on technologies, locations, resource potentials, etc. Calliope takes these files, constructs an optimization problem, solves it, and reports back results. Results can be saved to CSV or NetCDF files for further processing, or analysed directly in Python through Python's extensive scientific data processing capabilities provided by libraries like Pandas and xarray.
Calliope comes with several built-in analysis and visualisation tools. Having some knowledge of the Python programming language helps when running Calliope and using these tools, but is not a prerequisite.
Calliope can run on Windows, macOS and Linux. Installing it is quickest with the
conda package manager by running a single command:
conda create -c conda-forge -n calliope python=3.6 calliope. See the documentation for more information on installing.
Several easy to understand example models are included with Calliope and accessible through the
The tutorials in the documentation run through these examples. A good place to start is to look at these tutorials to get a feel for how Calliope works, and then to read the "Introduction", "Building a model", "Running a model", and "Analysing a model" sections in the online documentation.
A fully-featured example model is UK-Calliope, which models the power system of Great Britain (England+Scotland+Wales), and has been used in several peer-reviewed scientific publications.
Documentation is available on Read the Docs:
See changes made in recent versions in the changelog.
If you use Calliope, please cite the following paper:
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
Copyright 2013-2018 Calliope contributors listed in AUTHORS
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.