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| | {{Model | | {{Model |
| | |Full_Model_Name=Python for Power System Analysis | | |Full_Model_Name=Python for Power System Analysis |
| | + | |Acronym=PyPSA |
| | |author_institution=FIAS | | |author_institution=FIAS |
| | |authors=Tom Brown, Jonas Hörsch, David Schlachtberger | | |authors=Tom Brown, Jonas Hörsch, David Schlachtberger |
| | |contact_persons=Tom Brown | | |contact_persons=Tom Brown |
| | |contact_email=brown@fias.uni-frankfurt.de | | |contact_email=brown@fias.uni-frankfurt.de |
| − | |website=http://www.pypsa.org/ | + | |website=https://www.pypsa.org/ |
| − | |source_download=https://github.com/FRESNA/PyPSA | + | |source_download=https://github.com/PyPSA/PyPSA |
| − | |text_description=PyPSA is a free software toolbox for simulating and optimising modern electric power systems that include features such as variable wind and solar generation, storage units and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series. | + | |text_description=PyPSA is a free software toolbox for simulating and optimising modern energy systems that include features such as variable wind and solar generation, storage units, sector coupling and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series. |
| | + | |Support=https://groups.google.com/forum/#!forum/pypsa |
| | + | |User documentation=https://pypsa.org/doc/ |
| | + | |Source of funding=BMBF |
| | |open_source_licensed=Yes | | |open_source_licensed=Yes |
| | |license=GNU General Public License version 3.0 (GPL-3.0) | | |license=GNU General Public License version 3.0 (GPL-3.0) |
| | |model_source_public=Yes | | |model_source_public=Yes |
| | + | |Link to source=https://github.com/PyPSA/PyPSA |
| | |data_availability=all | | |data_availability=all |
| | |open_future=No | | |open_future=No |
| | |modelling_software=Python, Pyomo | | |modelling_software=Python, Pyomo |
| | |processing_software=Pandas | | |processing_software=Pandas |
| − | |model_class=Electricity System Model, | + | |External optimizer=All those supported by Pyomo |
| − | |sectors=Electricity, | + | |GUI=No |
| − | |technologies=Renewables, Conventional Generation | + | |model_class=Energy System Model, |
| | + | |sectors=Electricity, Heat, Transport, User-defined |
| | + | |technologies=Renewables, Conventional Generation, CHP |
| | + | |Storage (Gas)=No |
| | + | |Storage (Heat)=No |
| | |decisions=dispatch, investment | | |decisions=dispatch, investment |
| − | |georegions=Germany (later Europe) | + | |georegions=Europe, China, South Africa |
| | |georesolution=User dependent | | |georesolution=User dependent |
| | |timeresolution=Hour | | |timeresolution=Hour |
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| | |math_modeltype_shortdesc=Non-linear power flow; linear optimal power flow / investment optimisation | | |math_modeltype_shortdesc=Non-linear power flow; linear optimal power flow / investment optimisation |
| | |math_objective=Cost minimization | | |math_objective=Cost minimization |
| − | |deterministic=Not covered (yet) | + | |deterministic=Not explicitly covered, but stochastic optimisation possible |
| | |is_suited_for_many_scenarios=Yes | | |is_suited_for_many_scenarios=Yes |
| − | |example_research_questions=Power flow analysis, market analysis, total system investment optimisation, contingency analysis | + | |montecarlo=No |
| | + | |citation_references=Journal of Open Research Software, 2018, 6 (1) |
| | + | |citation_doi=https://doi.org/10.5334/jors.188 |
| | + | |report_references=https://pypsa.org/publications/ |
| | + | |example_research_questions=Power flow analysis, market analysis, total system investment optimisation, contingency analysis, sector coupling |
| | + | |Model input file format=No |
| | + | |Model file format=No |
| | + | |Model output file format=No |
| | }} | | }} |
Power flow analysis, market analysis, total system investment optimisation, contingency analysis, sector coupling
