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| | |math_modeltype_shortdesc=Linear optimisation (with an option of mixed-integer programming) | | |math_modeltype_shortdesc=Linear optimisation (with an option of mixed-integer programming) |
| | |math_objective=Minimise total discounted cost of system | | |math_objective=Minimise total discounted cost of system |
| − | |is_suited_for_many_scenarios=No | + | |is_suited_for_many_scenarios=Yes |
| | |montecarlo=No | | |montecarlo=No |
| | |computation_time_comments=Depends on solver used (glpsol/CPLEX/Gurobi etc.) | | |computation_time_comments=Depends on solver used (glpsol/CPLEX/Gurobi etc.) |
Latest revision as of 09:49, 23 March 2023
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open-source energy modelling system
by KTH Royal Institute of Technology
Authors:
Mark Howells, Holger Rogner, Neil Strachan, Charles Heaps, Hillard Huntington, Socrates Kypreos, Alison Hughes, Semida Silveira, Joe DeCarolis, Morgan Bazillian, Alexander Roehrl
Contact:
Mark Howells, Will Usher, Abhishek Shivakumar, Manuel Welsch, Vignesh Sridharan
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OSeMOSYS has been created by a community of leading institutions and is capable of powerful energy systems analysis and prototyping new energy model formulations. It is typically used for the analysis of energy systems looking over the medium (10-15yrs) and long (50-100yrs) term. It is used by experienced modellers as an exploratory tool, by developing country modellers where data limitations are an issue, and as a teaching tool.
Based on GNU MathProg. Using Python for data processing.
Website / Documentation
Download
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 Open Source
Apache License 2.0 (Apache-2.0)
Directly downloadable
Input data shipped
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| Model Scope |
Model type and solution approach |
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| Model type
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Optimization
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Linear optimisation (with an option of mixed-integer programming)
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| Variables
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| Computation time
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minutes (Depends on solver used (glpsol/CPLEX/Gurobi etc.))
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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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| Suited for many scenarios / monte-carlo
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Yes
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References
Scientific references
https://dx.doi.org/doi:10.1016/j.enpol.2011.06.033
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