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| | |computation_time_minutes=20 | | |computation_time_minutes=20 |
| | |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_doi=10.5281/zenodo.32038 | + | |citation_doi=10.5281/zenodo.46118 |
| | |report_references=]] | | |report_references=]] |
| | * [https://mediatum.ub.tum.de/node?id=1171502 Electricity system optimization in the EUMENA region]; Matthias Huber, Johannes Dorfner, Thomas Hamacher; technical report, Munich, 2012 | | * [https://mediatum.ub.tum.de/node?id=1171502 Electricity system optimization in the EUMENA region]; Matthias Huber, Johannes Dorfner, Thomas Hamacher; technical report, Munich, 2012 |
Revision as of 17:23, 16 February 2016
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urbs
by TUM EI ENS
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://dx.doi.org/10.5281/zenodo.46118
Reports produced using the model
- Electricity system optimization in the EUMENA region; Matthias Huber, Johannes Dorfner, Thomas Hamacher; technical report, Munich, 2012
- Modelling a Low-Carbon Power System for Indonesia, Malaysia and Singapore; Juergen Stich, Melanie Mannhart, Thomas Zipperle, Tobias Massier, Matthias Huber, Thomas Hamacher; 33rd IEW International Energy Workshop, Peking, 2014
- Transmission grid extensions for the integration of variable renewable energies in Europe: Who benefits where?; Katrin Schaber, Florian Steinke, Thomas Hamacher; Energy Policy, Volume 43, April 2012, 123–135.]]
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