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| | |modelling_software=Python (Pyomo) | | |modelling_software=Python (Pyomo) |
| | |processing_software=Python (pandas et al) | | |processing_software=Python (pandas et al) |
| − | |model_class=Energy Modelling Framework, | + | |model_class=Energy Modelling Framework, |
| − | |sectors=User-dependent, Electricity | + | |sectors=User-dependent, Electricity |
| | |technologies=Renewables, Conventional Generation, CHP | | |technologies=Renewables, Conventional Generation, CHP |
| | |decisions=dispatch, investment | | |decisions=dispatch, investment |
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| | |computation_time_comments=Highly dependent on model size (esp. storage) and solver (CPLEX, GLPK, Gurobi) | | |computation_time_comments=Highly dependent on model size (esp. storage) and solver (CPLEX, GLPK, Gurobi) |
| | |citation_references=https://github.com/tum-ens/urbs | | |citation_references=https://github.com/tum-ens/urbs |
| − | |citation_doi=Work in progress
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| | }} | | }} |
Revision as of 13:17, 28 April 2015
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urbs
by Technische Universität München
Authors:
Thomas Richter, Thomas Hamacher, Matthias Huber, Johannes Dorfner
Contact:
Johannes Dorfner
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URBS is a linear programming optimisation model for capacity expansion planning and unit commitment for distributed energy systems. Its name, latin for city, stems from its origin as a model for optimisation for urban energy systems. Since then, it has been adapted to multiple scales from neighbourhoods to continents.
Based on Python (Pyomo). Using Python (pandas et al) for data processing.
Website / Documentation
Download
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 Open Source
GNU General Public License version 3.0 (GPL-3.0)
Directly downloadable
Some input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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Energy Modelling Framework
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| Sectors
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User-dependent, Electricity
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| Technologies
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Renewables, Conventional Generation, CHP
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| Decisions
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dispatch, investment
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| Regions
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User-dependent
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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, net transfer capacities
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| Model type
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Optimization
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Linear optimization model of a user-defined reference energy system.
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| Variables
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100000
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| Computation time
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20 minutes (Highly dependent on model size (esp. storage) and solver (CPLEX, GLPK, Gurobi))
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| Objective
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Minimise total discounted cost of system
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| Uncertainty modeling
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None
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| Suited for many scenarios / monte-carlo
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Yes
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References
Scientific references
https://github.com/tum-ens/urbs
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