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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=https://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-Stylised.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. |
| | | | |
| | Feedback and contributions are very welcome! | | Feedback and contributions are very welcome! |
| | + | |User documentation=https://calliope.readthedocs.io/en/stable/ |
| | |open_source_licensed=Yes | | |open_source_licensed=Yes |
| | |license=Apache License 2.0 (Apache-2.0) | | |license=Apache License 2.0 (Apache-2.0) |
| | |model_source_public=Yes | | |model_source_public=Yes |
| | + | |Link to source=https://github.com/calliope-project/calliope |
| | |data_availability=some | | |data_availability=some |
| | |open_future=Yes | | |open_future=Yes |
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| | |sectors=User-dependent | | |sectors=User-dependent |
| | |technologies=Renewables, Conventional Generation, CHP | | |technologies=Renewables, Conventional Generation, CHP |
| − | |Storage (Gas)=No | + | |Demand sectors=Households, Industry, Transport, Commercial sector, Other |
| − | |Storage (Heat)=No | + | |Energy carrier (Gas)=Natural gas, Biogas, Hydrogen |
| | + | |Energy carrier (Liquid)=Diesel, Ethanol, Petrol |
| | + | |Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium |
| | + | |Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind |
| | + | |Transfer (Electricity)=Distribution, Transmission |
| | + | |Transfer (Gas)=Distribution, Transmission |
| | + | |Transfer (Heat)=Distribution, Transmission |
| | + | |Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS |
| | + | |Storage (Gas)=Yes |
| | + | |Storage (Heat)=Yes |
| | |decisions=dispatch, investment | | |decisions=dispatch, investment |
| | + | |Changes in efficiency=fixed or time varying |
| | |georegions=User-dependent | | |georegions=User-dependent |
| | |georesolution=User-dependent | | |georesolution=User-dependent |
| | |timeresolution=Hour | | |timeresolution=Hour |
| − | |network_coverage=net transfer capacities | + | |network_coverage=transmission, distribution, net transfer capacities |
| | + | |Observation period=Less than one year, More than one year |
| | + | |Additional dimensions (Ecological)=CO2, land use, and more (user-defined) |
| | + | |Additional dimensions (Economical)=LCOE |
| | |math_modeltype=Optimization | | |math_modeltype=Optimization |
| − | |math_objective=Costs or user-dependent | + | |math_objective=User-dependent, including financial cost, CO2, and water consumption |
| | + | |deterministic=Deterministic; stochastic programming add-on |
| | |is_suited_for_many_scenarios=Yes | | |is_suited_for_many_scenarios=Yes |
| − | |montecarlo=No | + | |montecarlo=Yes |
| | |computation_time_comments=user-dependent | | |computation_time_comments=user-dependent |
| − | |citation_references=Pfenninger and Pickering (2018). Calliope: a multi-scale energy systems modelling framework. Journal of Open Source Software, 3(29), 825 | + | |citation_references=Pfenninger and Pickering, (2018). Calliope: a multi-scale energy systems modelling framework. Journal of Open Source Software, 3(29), 825 |
| | |citation_doi=10.21105/joss.00825 | | |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 |
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| | + | 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 |
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| | + | 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 |
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| | + | 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 |
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| | + | 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 |
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| | + | 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 |
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| | + | 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 |
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| | + | 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 |
| | |Model input file format=No | | |Model input file format=No |
| | |Model file format=No | | |Model file format=No |
| | |Model output file format=No | | |Model output file format=No |
| | }} | | }} |
