URBS

Revision as of 11:49, 13 October 2015

urbs

Authors:
Thomas Richter, Thomas Hamacher, Matthias Huber, Johannes Dorfner

Contact:
Johannes Dorfner

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.

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Open Source
GNU General Public License version 3.0 (GPL-3.0)

Directly downloadable

Some input data shipped

Model Scope

Model type and solution approach

Model class	Energy Modelling Framework
Sectors	User-dependent, Electricity
Technologies	Renewables, Conventional Generation, CHP
Decisions	dispatch, investment
Regions	User-dependent
Geographic Resolution	User-dependent
Time resolution	Hour
Network coverage	transmission, net transfer capacities

Model type	Optimization
	Linear optimization model of a user-defined reference energy system.
Variables	100000
Computation time	20 minutes (Highly dependent on model size (esp. storage) and solver (CPLEX, GLPK, Gurobi))
Objective	Minimise total discounted cost of system
Uncertainty modeling	None
Suited for many scenarios / monte-carlo	Yes

References

Scientific references

https://dx.doi.org/10.5281/zenodo.32038

Reports produced using the model

[[report_references::* 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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@@ Line 32: / Line 32: @@
 |computation_time_comments=Highly dependent on model size (esp. storage) and solver (CPLEX, GLPK, Gurobi)
 |citation_doi=10.5281/zenodo.32038
-|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
+|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/doc/1233948/1233948.pdf 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
 * [http://dx.doi.org/10.1016/j.enpol.2011.12.040 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.
 }}

Author institution	TUM EI ENS +
Authors	Thomas Richter, Thomas Hamacher, Matthias Huber, Johannes Dorfner +
Citation doi	10.5281/zenodo.32038 +
Computation time comments	Highly dependent on model size (esp. storage) and solver (CPLEX, GLPK, Gurobi)
Computation time minutes	20 +
Contact email	johannes.dorfner@tum.de +
Contact persons	Johannes Dorfner +
Data availability	some +
Decisions	dispatch + and investment +
Deterministic	None +
Full Model Name	urbs +
Georegions	User-dependent +
Georesolution	User-dependent +
Is suited for many scenarios	true +
License	GNU General Public License version 3.0 (GPL-3.0) +
Math modeltype	Optimization +
Math modeltype shortdesc	Linear optimization model of a user-defined reference energy system. +
Math objective	Minimise total discounted cost of system +
Model class	Energy Modelling Framework +
Model source public	true +
Modelling software	Python (Pyomo) +
Network coverage	transmission + and net transfer capacities +
Number of variables	100,000 +
Open future	true +
Open source licensed	true +
Processing software	Python (pandas et al) +
Sectors	User-dependent + and Electricity +
Source download	https://github.com/tum-ens/urbs +
Technologies	Renewables +, Conventional Generation + and CHP +
Text description	URBS is a linear programming optimisation … 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. scales from neighbourhoods to continents.
Timeresolution	Hour +
Website	https://github.com/tum-ens/urbs +

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URBS

Revision as of 11:49, 13 October 2015

References

Scientific references

Reports produced using the model

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URBS

Revision as of 11:49, 13 October 2015

References

Scientific references

Reports produced using the model