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| | |contact_persons=Tomi J. Lindroos | | |contact_persons=Tomi J. Lindroos |
| | |contact_email=Tomi.J.Lindroos@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/-/tree/release-3.x |
| | |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). |
| | |Primary outputs=Costs, emissions, generation, consumption, transfers | | |Primary outputs=Costs, emissions, generation, consumption, transfers |
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| | |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/-/tree/release-3.x |
| | |data_availability=some | | |data_availability=some |
| | |open_future=No | | |open_future=No |
Revision as of 08:35, 14 November 2023
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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
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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
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 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.
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| Variables
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1000000
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| Computation time
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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 14.11.2023):
Model documentation (please cite this if looking for a generic Backbone reference)
Helistö, N., Kiviluoma, J., Ikäheimo, J., Rasku, T., Rinne, E., O’Dwyer, C., Li, R., & Flynn, D. (2019). Backbone - An adaptable energy systems modelling framework. Energies, 12(17), 3388. https://doi.org/10.3390/en12173388
Model verification for power systems (using an IEEE test system)
C. Barrows et al. (2020). The IEEE Reliability Test System: A Proposed 2019 Update. IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 119-127, Jan. 2020. https://doi.org/10.1109/TPWRS.2019.2925557
Papers with a methodological focus
Finke, J. and Bertsch, V. (2023). Implementing a highly adaptable method for the multi-objective optimisation of energy systems. Applied Energy, Volume 332, 2023, 120521, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2022.120521.
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, T., Miettinen, J., Rinne, E., & Kiviluoma, J. (2020). Impact of 15-day energy forecasts on the hydro-thermal scheduling of a future Nordic power system. Energy, 192, 116668. https://doi.org/10.1016/j.energy.2019.116668
Case studies
Putkonen, N., Lindroos, T.J., Neniškis, E., Žalostība, D., Norvaiša, E., Galinis, A., Teremranova, J. & Kiviluoma, J. (2022) Modeling the Baltic countries’ Green Transition and Desynchronization from the Russian Electricity Grid. https://dx.doi.org/10.54337/ijsepm.7059
Pursiheimo, E., Lindroos, T. J., Sundell, D., Rämä, M., Tulkki, V. (2022) Optimal Investment Analysis for Heat Pumps and Nuclear Heat in Decarbonised Helsinki Metropolitan District Heating System. Energy Storages and Saving, https://doi.org/10.1016/j.enss.2022.03.001
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. (2019). A comparison of widespread flexible residential electric heating and energy efficiency in a future Nordic power system. Energies, 12(1), 5. https://doi.org/10.3390/en12010005]]
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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