| Name | Calliope Europe |
| Acronym | euro-calliope |
| Methodical Focus | linear optimisation |
| Institution(s) | ETH Zürich; Switzerland, University of Cambridge; UK |
| Author(s) (institution, working field, active time period) | Dr. Stefan Pfenninger; ETH Zürich; National climate/renewable energy policy; 2013 - current, Bryn Pickering; University of Cambridge; Multi-energy district energy systems; 2016 - current |
| Current contact person | Stefan Pfenninger |
| Contact (e-mail) | stefan.pfenninger@usys.ethz.ch |
| Website | https://callio.pe |
| Logo |
|
|---|---|
| Primary Purpose | Euro-Calliope is a suite of pre-built models and routines to build these models from raw data. You can configure, adapt, and extend both models and routines in many ways to build your own model that is fit for your purpose. Calliope is an energy systems linear optimisation framework, 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). Energy system planning/operation problems can be easily built, optimised for minimum cost (or other metrics, such as environmental impact) and analysed. It is highly usable for non-technical researchers, whilst maintaining a clear code-base for extendability by interested software developers. |
| Primary Outputs | The model outputs are the technology investment portfolio for a given system, be it a network describing an entire country, or just a district of a few buildings, and the operation schedule for those technologies to meet demand. The operation schedule can be as high resolution as the data available to the user, with hourly timesteps being standard for our research. |
| Support / Community / Forum | |
| Framework | |
| Link to User Documentation | https://euro-calliope.readthedocs.io/en/latest/#where-to-start |
| Link to Developer/Code Documentation | https://euro-calliope.readthedocs.io/en/latest/ |
| Documentation quality | excellent |
| Source of funding | No direct funding for Calliope development |
| Number of developers | less than 10 |
| Number of users | less than 100 |
| Open Source | |
| License | MIT |
| Source code available | |
| GitHub | |
| Access to source code | https://github.com/calliope-project/euro-calliope |
| Data provided | example data |
| Collaborative programming |
| GitHub Organisation |
| Modelling software | Python |
| Internal data processing software | |
| External optimizer | |
| Additional software | |
| GUI |
| Modeled energy sectors (final energy) |
|
||||||
| Modeled demand sectors |
|
||||||
| Modeled technologies: components for power generation or conversion |
|
||||||
| Modeled technologies: components for transfer, infrastructure or grid |
|
||||||
| Properties electrical grid |
|
||||||
| Modeled technologies: components for storage |
|
||||||
| User behaviour and demand side management | |||||||
| Changes in efficiency | |||||||
| Market models | - | ||||||
| Geographical coverage | |||||||
| Geographic (spatial) resolution |
|
||||||
| Time resolution | - | ||||||
| Comment on geographic (spatial) resolution | |||||||
| Observation period | - | ||||||
| Additional dimensions (sector) |
|
| Model class (optimisation) | - |
| Model class (simulation) | - |
| Other | |
| Short description of mathematical model class | |
| Mathematical objective | - |
| Approach to uncertainty | - |
| Suited for many scenarios / monte-carlo | |
| typical computation time | less than an hour |
| Typical computation hardware | Typical laptop in 2018, for large models. Example models run in less than a second. |
| Technical data anchored in the model | None |
| Interfaces | |
| Model file format | PyPi wheel (install via conda-forge is preferrable) |
| Input data file format | YAML human-readable text & CSV (for timeseries) |
| Output data file format | CSV files or NetCDF |
| Integration with other models | |
| Integration of other models |
| Citation reference | 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 |
| Citation DOI | 10.1016/j.apenergy.2017.03.051 |
| Reference Studies/Models | Bryn Pickering and Ruchi Choudhary (2017). Applying Piecewise Linear Characteristic Curves in District Energy Optimisation. Proceedings of the 30th ECOS Conference, San Diego, CA, 2-6 July 2017; 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; Paula Díaz Redondo, Oscar Van Vliet and Anthony Patt (2017). Do We Need Gas as a Bridging Fuel? A Case Study of the Electricity System of Switzerland. Energies, 10 (7), p. 861; Paula Díaz Redondo and Oscar Van Vliet (2016). Modelling the Energy Future of Switzerland after the Phase Out of Nuclear Power Plants. Energy Procedia; 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; Stefan Pfenninger and James Keirstead (2015). Comparing concentrating solar and nuclear power as baseload providers using the example of South Africa. Energy |
| Example research questions | - |
| Model usage | - |
| Model validation |
|
| Example research questions | - |
| further properties | |
| Model specific properties | - |