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| | |Full_Model_Name=Backbone - energy systems model | | |Full_Model_Name=Backbone - energy systems model |
| | |Acronym=Backbone | | |Acronym=Backbone |
| − | |author_institution=VTT Technical Research Centre of Finland; University College Dublin | + | |author_institution=VTT Technical Research Centre of Finland |
| − | |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/-/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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| | |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/-/tree/release-3.x |
| | + | |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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| | |georegions=Depends on user | | |georegions=Depends on user |
| | |georesolution=Depends on user | | |georesolution=Depends on user |
| − | |timeresolution=Hour | + | |timeresolution=15 Minute |
| | |network_coverage=transmission, DC load flow, net transfer capacities | | |network_coverage=transmission, DC load flow, net transfer capacities |
| | |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 full year in seconds, complicated European models with 20+ regions and many sectors 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=Journal publications (updated 14.11.2023): |
| − | https://gitlab.vtt.fi/backbone/backbone/-/wikis/More-information/List-of-publications
| + | |
| | | | |
| − | Journal publications (updated 14.3.2022):
| + | 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., 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 |
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| | 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 | | 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 | + | 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 |
| − | Volume 192, 1 February 2020, 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 |
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| | 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 | | 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 |
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| | 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 | | 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 | + | 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 |
| | | | |
| − | Pursiheimo, E., & Kiviluoma, J. (2021). Analyzing electrification scenarios for the northern European energy system. In Electrification (pp. 271-288). Academic Press.
| + | |
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| | + | |
| | + | Shared model data |
| | + | |
| | + | Ikäheimo, J., Purhonen, A., Lindroos, T.J., Rämä, M. and Harrison, E. Northern European Model. https://github.com/vttresearch/north_european_model |
| | + | |
| | + | Lindroos, T.J., and Pursiheimo, E. Helsinki Region DHC model. https://gitlab.vtt.fi/backbone/models/helsinki-dhc-model |
| | + | |
| | + | |
| | + | |
| | + | Please see a longer list at: |
| | + | https://gitlab.vtt.fi/backbone/backbone/-/wikis/More-information/List-of-publications |
| | |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). | | |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). |
| | |Model validation=Comparison against two other models: https://doi.org/10.3390/en12173388 | | |Model validation=Comparison against two other models: https://doi.org/10.3390/en12173388 |
| | |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 |
| | }} | | }} |
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
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
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
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
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
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).
