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| | |modelling_software=Python, Pyomo | | |modelling_software=Python, Pyomo |
| | |processing_software=Pandas | | |processing_software=Pandas |
| − | |model_class=Electricity System Model, | + | |model_class=Electricity System Model, |
| − | |sectors=Electricity, | + | |sectors=Electricity, |
| | |technologies=Renewables, Conventional Generation | | |technologies=Renewables, Conventional Generation |
| | |decisions=dispatch, investment | | |decisions=dispatch, investment |
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| | |math_objective=Cost minimization | | |math_objective=Cost minimization |
| | |deterministic=Not covered (yet) | | |deterministic=Not covered (yet) |
| − | |is_suited_for_many_scenarios=No | + | |is_suited_for_many_scenarios=Yes |
| | + | |example_research_questions=Power flow analysis, market analysis, total system investment optimisation, contingency analysis |
| | }} | | }} |
Revision as of 20:19, 21 March 2016
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Python for Power System Analysis
by FIAS
Authors:
Tom Brown, Jonas Hörsch, David Schlachtberger
Contact:
Tom Brown
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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.
Based on Python, Pyomo. Using Pandas for data processing.
Website / Documentation
Download
|
 Open Source
GNU General Public License version 3.0 (GPL-3.0)
Directly downloadable
Input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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Electricity System Model
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| Sectors
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Electricity
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| Technologies
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Renewables, Conventional Generation
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| Decisions
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dispatch, investment
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| Regions
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Germany (later Europe)
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| Geographic Resolution
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User dependent
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| Time resolution
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Hour
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| Network coverage
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transmission, distribution, AC load flow, DC load flow, net transfer capacities
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| Model type
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Optimization, Simulation
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|
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Non-linear power flow; linear optimal power flow / investment optimisation
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| Variables
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| Computation time
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minutes
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| Objective
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Cost minimization
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| Uncertainty modeling
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Not covered (yet)
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| Suited for many scenarios / monte-carlo
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Yes
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|
References
Scientific references
Example research questions
Power flow analysis, market analysis, total system investment optimisation, contingency analysis
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