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| | {{Model | | {{Model |
| | |Full_Model_Name=Calliope | | |Full_Model_Name=Calliope |
| − | |author_institution=ETH Zürich, University of Cambridge | + | |author_institution=ETH Zürich |
| | |authors=Stefan Pfenninger, Bryn Pickering | | |authors=Stefan Pfenninger, Bryn Pickering |
| − | |contact_persons=Stefan Pfenninger | + | |contact_persons=contact@callio.pe |
| − | |contact_email=stefan.pfenninger@usys.ethz.ch | + | |contact_email=contact@callio.pe |
| | |website=http://www.callio.pe/ | | |website=http://www.callio.pe/ |
| | |source_download=https://github.com/calliope-project/calliope | | |source_download=https://github.com/calliope-project/calliope |
| − | |logo=calliope logo.png | + | |logo=Calliope-Logo-Simplified.png |
| | |text_description=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. | | |text_description=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. |
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| | |network_coverage=transmission, distribution, net transfer capacities | | |network_coverage=transmission, distribution, net transfer capacities |
| | |Observation period=Less than one year, More than one year | | |Observation period=Less than one year, More than one year |
| − | |Additional dimensions (Ecological)=CO2, land use, and more (user-defined) | + | |Additional dimensions (Ecological)=CO2, land use, and more (user-defined) |
| | |Additional dimensions (Economical)=LCOE | | |Additional dimensions (Economical)=LCOE |
| | |math_modeltype=Optimization | | |math_modeltype=Optimization |
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| | |montecarlo=Yes | | |montecarlo=Yes |
| | |computation_time_comments=user-dependent | | |computation_time_comments=user-dependent |
| − | |citation_references=Stefan Pfenninger and James Keirstead. 2015. Renewables, nuclear, or fossil fuels? Scenarios for Great Britain's power system considering costs, emissions and energy security. Applied Energy, 152, pp. 83–93. | + | |citation_references=Pfenninger and Pickering, (2018). Calliope: a multi-scale energy systems modelling framework. Journal of Open Source Software, 3(29), 825 |
| − | |citation_doi=10.1016/j.apenergy.2015.04.102
| + | |citation_doi=10.21105/joss.00825 |
| | + | |report_references=Simon Morgenthaler, Wilhelm Kuckshinrichs and Dirk Witthaut (2020). Optimal system layout and locations for fully renewable high temperature co-electrolysis. Applied Energy, doi: 10.1016/j.apenergy.2019.114218 |
| | + | |
| | + | C. Del Pero, F. Leonforte, F. Lombardi, N. Stevanato, J. Barbieri, N. Aste, H. Huerto, E. Colombo (2019). Modelling Of An Integrated Multi-Energy System For A Nearly Zero Energy Smart District. 2019 International Conference on Clean Electrical Power (ICCEP) (pp. 246–252). doi: 10.1109/ICCEP.2019.8890129 |
| | + | |
| | + | Adriaan Hilbers, David Brayshaw and Axel Gandy (2019). Importance subsampling: improving power system planning under climate-based uncertainty. Applied Energy, doi: 10.1016/j.apenergy.2019.04.110 |
| | + | |
| | + | Francesco Lombardi, Matteo Vincenzo Rocco and Emanuela Colombo (2019). A multi-layer energy modelling methodology to assess the impact of heat-electricity integration strategies: the case of the residential cooking sector in Italy. Energy, doi: 10.1016/j.energy.2019.01.004 |
| | + | |
| | + | Bryn Pickering and Ruchi Choudhary (2019). District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles. Applied Energy 236, 1138–1157. doi: 10.1016/j.apenergy.2018.12.037 |
| | + | |
| | + | Bryn Pickering and Ruchi Choudhary (2018). Mitigating risk in district-level energy investment decisions by scenario optimisation, in: Proceedings of BSO 2018. Presented at the 4th Building Simulation and Optimization Conference, Cambridge, UK, pp. 38–45. PDF in Conference proceedings |
| | + | |
| | + | 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. PDF link |
| | + | |
| | + | 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, 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. doi: 10.3390/en10070861 |
| | + | |
| | + | 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 |
| | + | |
| | + | Mercè Labordena and Johan Lilliestam (2015). Cost and Transmission Requirements for Reliable Solar Electricity from Deserts in China and the United States. Energy Procedia. doi: 10.1016/j.egypro.2015.07.850 |
| | + | |
| | + | 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 |
| | + | |
| | + | 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 |
| | |Interfaces=netCDF input/output | | |Interfaces=netCDF input/output |
| | |Model input file format=No | | |Model input file format=No |
Latest revision as of 11:55, 16 April 2020
|
Calliope
by ETH Zürich
Authors:
Stefan Pfenninger, Bryn Pickering
Contact:
contact@callio.pe
|
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.
Feedback and contributions are very welcome!
Based on Python (Pyomo). Using Python (pandas et al) for data processing.
Website / Documentation
Download
|
 Open Source
Apache License 2.0 (Apache-2.0)
Directly downloadable
Some input data shipped
|
| Model Scope |
Model type and solution approach |
| Model class
|
Framework
|
| Sectors
|
User-dependent
|
| Technologies
|
Renewables, Conventional Generation, CHP
|
| Decisions
|
dispatch, investment
|
| Regions
|
User-dependent
|
| Geographic Resolution
|
User-dependent
|
| Time resolution
|
Hour
|
| Network coverage
|
transmission, distribution, net transfer capacities
|
|
| Model type
|
Optimization
|
|
|
|
| Variables
|
|
| Computation time
|
minutes (user-dependent)
|
| Objective
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User-dependent, including financial cost, CO2, and water consumption
|
| Uncertainty modeling
|
Deterministic; stochastic programming add-on
|
| Suited for many scenarios / monte-carlo
|
Yes
|
|
References
Scientific references
Pfenninger and Pickering, (2018). Calliope: a multi-scale energy systems modelling framework. Journal of Open Source Software, 3(29), 825
https://dx.doi.org/10.21105/joss.00825
Reports produced using the model
Simon Morgenthaler, Wilhelm Kuckshinrichs and Dirk Witthaut (2020). Optimal system layout and locations for fully renewable high temperature co-electrolysis. Applied Energy, doi: 10.1016/j.apenergy.2019.114218
C. Del Pero, F. Leonforte, F. Lombardi, N. Stevanato, J. Barbieri, N. Aste, H. Huerto, E. Colombo (2019). Modelling Of An Integrated Multi-Energy System For A Nearly Zero Energy Smart District. 2019 International Conference on Clean Electrical Power (ICCEP) (pp. 246–252). doi: 10.1109/ICCEP.2019.8890129
Adriaan Hilbers, David Brayshaw and Axel Gandy (2019). Importance subsampling: improving power system planning under climate-based uncertainty. Applied Energy, doi: 10.1016/j.apenergy.2019.04.110
Francesco Lombardi, Matteo Vincenzo Rocco and Emanuela Colombo (2019). A multi-layer energy modelling methodology to assess the impact of heat-electricity integration strategies: the case of the residential cooking sector in Italy. Energy, doi: 10.1016/j.energy.2019.01.004
Bryn Pickering and Ruchi Choudhary (2019). District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles. Applied Energy 236, 1138–1157. doi: 10.1016/j.apenergy.2018.12.037
Bryn Pickering and Ruchi Choudhary (2018). Mitigating risk in district-level energy investment decisions by scenario optimisation, in: Proceedings of BSO 2018. Presented at the 4th Building Simulation and Optimization Conference, Cambridge, UK, pp. 38–45. PDF in Conference proceedings
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. PDF link
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, 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. doi: 10.3390/en10070861
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
Mercè Labordena and Johan Lilliestam (2015). Cost and Transmission Requirements for Reliable Solar Electricity from Deserts in China and the United States. Energy Procedia. doi: 10.1016/j.egypro.2015.07.850
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
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
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