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| | |Acronym=Backbone | | |Acronym=Backbone |
| | |author_institution=VTT Technical Research Centre of Finland; University College Dublin | | |author_institution=VTT Technical Research Centre of Finland; University College Dublin |
| − | |authors=Juha Kiviluoma, Erkka Rinne, Topi Rasku, Niina Helistö, Jussi Ikäheimo, Dana Kirchem, Ran Li, Ciara O'Dwyer | + | |authors=Juha Kiviluoma, Erkka Rinne, Topi Rasku, Niina Helistö, Jussi Ikäheimo, Dana Kirchem, Ran Li, Ciara O'Dwyer, Jussi Ikäheimo, Tomi J. Lindroos, Eric Harrison |
| − | |contact_persons=Juha Kiviluoma, Erkka Rinne | + | |contact_persons=Tomi J. Lindroos |
| − | |contact_email=juha.kiviluoma@vtt.fi, erkka.rinne@vtt.fi | + | |contact_email=Tomi.J.Lindroos@vtt.fi |
| | |source_download=https://gitlab.vtt.fi/backbone/backbone | | |source_download=https://gitlab.vtt.fi/backbone/backbone |
| | |text_description=Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS). | | |text_description=Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS). |
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| | |Code documentation=Formulas: https://doi.org/10.3390/en12173388; Code documentation: within code | | |Code documentation=Formulas: https://doi.org/10.3390/en12173388; Code documentation: within code |
| | |Source of funding=Academy of Finland; ESIPP project (Ireland) | | |Source of funding=Academy of Finland; ESIPP project (Ireland) |
| − | |Number of developers=8 | + | |Number of developers=11 |
| | |open_source_licensed=Yes | | |open_source_licensed=Yes |
| | |license=GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) | | |license=GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
| | |model_source_public=Yes | | |model_source_public=Yes |
| | |Link to source=https://gitlab.vtt.fi/backbone/backbone | | |Link to source=https://gitlab.vtt.fi/backbone/backbone |
| | + | |data_availability=some |
| | |open_future=No | | |open_future=No |
| | |modelling_software=GAMS | | |modelling_software=GAMS |
| − | |processing_software=Spine Toolbox forthcoming. Currently Excel / SQL. | + | |processing_software=Spine Toolbox or Excel |
| | |GUI=No | | |GUI=No |
| | |model_class=Framework | | |model_class=Framework |
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| | |Observation period=More than one year | | |Observation period=More than one year |
| | |Additional dimensions (Ecological)=Depends on data | | |Additional dimensions (Ecological)=Depends on data |
| − | |Additional dimensions (Economical)=- | + | |Additional dimensions (Economical)=Depends on data |
| | |Additional dimensions (Social)=- | | |Additional dimensions (Social)=- |
| | |Additional dimensions (Other)=- | | |Additional dimensions (Other)=- |
| | |math_modeltype=Optimization | | |math_modeltype=Optimization |
| − | |math_modeltype_shortdesc=The model minimizes the objective function and includes constraints related to energy balance, unit operation, transfers, system operation, portfolio design, etc. | + | |math_modeltype_shortdesc=The model minimizes the objective function and includes constraints related to energy balance, emissions, unit operation, transfers, system operation, portfolio design, etc. |
| − | |math_objective=Cost minimization | + | |math_objective=Cost minimization; emission minimization; |
| | |deterministic=Short-term and long-term stochastics are available | | |deterministic=Short-term and long-term stochastics are available |
| | |is_suited_for_many_scenarios=Yes | | |is_suited_for_many_scenarios=Yes |
| | |number_of_variables=1000000 | | |number_of_variables=1000000 |
| | |montecarlo=No | | |montecarlo=No |
| − | |computation_time_minutes=1000 | + | |computation_time_minutes=10 |
| − | |computation_time_comments=The implementation leads to reasonable computation time, but we plan to improve calculation time in future. | + | |computation_time_hardware=Normal laptop |
| | + | |computation_time_comments=Simple models can solve in seconds, complicated Europe-level models typically solve full year in ~hour |
| | |citation_references=Helistö, N.; Kiviluoma, J.; Ikäheimo, J.; Rasku, T.; Rinne, E.; O’Dwyer, C.; Li, R.; Flynn, D. Backbone—An Adaptable Energy Systems Modelling Framework. Energies 2019, 12, 3388. | | |citation_references=Helistö, N.; Kiviluoma, J.; Ikäheimo, J.; Rasku, T.; Rinne, E.; O’Dwyer, C.; Li, R.; Flynn, D. Backbone—An Adaptable Energy Systems Modelling Framework. Energies 2019, 12, 3388. |
| | |citation_doi=https://doi.org/10.3390/en12173388 | | |citation_doi=https://doi.org/10.3390/en12173388 |
| − | |report_references= Please see a longer list at: | + | |report_references=Please see a longer list at: |
| | https://gitlab.vtt.fi/backbone/backbone/-/wikis/More-information/List-of-publications | | https://gitlab.vtt.fi/backbone/backbone/-/wikis/More-information/List-of-publications |
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| | |Comment on model validation=Produces similar unit commitment results as a commercial tool in wide-spread use. | | |Comment on model validation=Produces similar unit commitment results as a commercial tool in wide-spread use. |
| | |Specific properties=Flexible temporal and technological detail. An energy systems model with capability for detailed unit commitment of the power system. Can include operational detail in generation/transmission planning. | | |Specific properties=Flexible temporal and technological detail. An energy systems model with capability for detailed unit commitment of the power system. Can include operational detail in generation/transmission planning. |
| − | |Interfaces=Spine Toolbox forthcoming. | + | |Interfaces=Excel or Spine Toolbox |
| | |Model input file format=No | | |Model input file format=No |
| | |Model file format=No | | |Model file format=No |
| | |Model output file format=No | | |Model output file format=No |
| | }} | | }} |
Revision as of 08:29, 14 November 2023
|
Backbone - energy systems model
by VTT Technical Research Centre of Finland; University College Dublin
Authors:
Juha Kiviluoma, Erkka Rinne, Topi Rasku, Niina Helistö, Jussi Ikäheimo, Dana Kirchem, Ran Li, Ciara O'Dwyer, Jussi Ikäheimo, Tomi J. Lindroos, Eric Harrison
Contact:
Tomi J. Lindroos
|
Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS).
Based on GAMS. Using Spine Toolbox or Excel for data processing.
Download
|
 Open Source
GNU Library or "Lesser" General Public License version 3.0 (LGPL-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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Framework
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| Sectors
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All
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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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Depends on user
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| Geographic Resolution
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Depends on user
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| Time resolution
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Hour
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| Network coverage
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transmission, DC load flow, net transfer capacities
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| Model type
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Optimization
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|
|
The model minimizes the objective function and includes constraints related to energy balance, emissions, unit operation, transfers, system operation, portfolio design, etc.
|
| Variables
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1000000
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| Computation time
|
10 minutes (Simple models can solve in seconds, complicated Europe-level models typically solve full year in ~hour)
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| Objective
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Cost minimization; emission minimization;
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| Uncertainty modeling
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Short-term and long-term stochastics are available
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| Suited for many scenarios / monte-carlo
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Yes
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References
Scientific references
Helistö, N.; Kiviluoma, J.; Ikäheimo, J.; Rasku, T.; Rinne, E.; O’Dwyer, C.; Li, R.; Flynn, D. Backbone—An Adaptable Energy Systems Modelling Framework. Energies 2019, 12, 3388.
https://dx.doi.org/https://doi.org/10.3390/en12173388
Reports produced using the model
[[report_references::Please see a longer list at:
https://gitlab.vtt.fi/backbone/backbone/-/wikis/More-information/List-of-publications
Journal publications (updated 14.3.2022):
Helistö, N., Kiviluoma, J., Morales-España, G. & O’Dwyer, C. (2021). Impact of operational details and temporal representations on investment planning in energy systems dominated by wind and solar. Applied Energy, 290, 116712. https://doi.org/10.1016/j.apenergy.2021.116712
Helistö, N., Kiviluoma, J., & Reittu, H. (2020). Selection of representative slices for generation expansion planning using regular decomposition. Energy, 211, 118585. https://doi.org/10.1016/j.energy.2020.118585
Rasku et al. Impact of 15-day energy forecasts on the hydro-thermal scheduling of a future Nordic power system. Energy
Volume 192, 1 February 2020, 116668. https://doi.org/10.1016/j.energy.2019.116668
Kiviluoma, J., O'Dwyer, C., Ikäheimo, J., Lahon, R., Li, Ran, Kirchem D., Helistö, N., Rinne, E., Flynn, D. (2022) Multi-sectoral flexibility measures to facilitate wind and solar power integration. IET Renew. Power Gener., https://doi.org/10.1049/rpg2.12399
Ikäheimo, J., Weiss, R., Kiviluoma, J., Pursiheimo, E., & Lindroos, T. J. (2022). Impact of power-to-gas on the cost and design of the future low-carbon urban energy system. Applied Energy, 305, [117713]. https://doi.org/10.1016/j.apenergy.2021.117713
Lindroos, T. J., Mäki, E., Koponen, K., Hannula, I., Kiviluoma, J., & Raitila, J. (2021). Replacing fossil fuels with bioenergy in district heating – Comparison of technology options. Energy, 231, [120799]. https://doi.org/10.1016/j.energy.2021.120799
Rasku, T. & Kiviluoma, J. A Comparison of Widespread Flexible Residential Electric Heating and Energy Efficiency in a Future Nordic Power System. Energies 2019, 12(1), 5; https://doi.org/10.3390/en12010005
Pursiheimo, E., & Kiviluoma, J. (2021). Analyzing electrification scenarios for the northern European energy system. In Electrification (pp. 271-288). Academic Press.]]
Example research questions
Cost efficient future energy systems with high shares of variable power generation. Exploring the impact of operational details on energy system planning. Optimizing the use of storages and energy intensive processes that have days-long time delays (model temporal structure can change during the horizon).
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