wiki.openmod-initiative.org - User contributions [en]

IRENA FlexTool

2023-09-18T07:52:58Z

Juha Kiviluoma:

{{Model
|Full_Model_Name=IRENA FlexTool
|Acronym=FlexTool
|author_institution=VTT Technical Research Centre of Finland
|authors=Juha Kiviluoma, Arttu Tupala, Antti Soininen
|contact_persons=Juha Kiviluoma
|contact_email=juha.kiviluoma@vtt.fi
|website=https://irena-flextool.github.io/flextool/
|source_download=https://github.com/irena-flextool/flextool
|logo=Flextool logo.png
|text_description=IRENA FlexTool is an energy and power systems model for understanding the role of variable power generation in future energy systems. It performs capacity expansion planning as well as operational planning.

VTT develops the model for IRENA (and receives a lot of feedback from IRENA to improve the model)
|Primary outputs=Investments, retirements, generation, demand, storage, transfer, prices, reserves, penalties/violations
|Support=https://github.com/irena-flextool/flextool/issues
|Framework=Uses Spine Toolbox
|User documentation=https://irena-flextool.github.io/flextool/
|Source of funding=IRENA, LeapRE
|Number of developers=3
|open_source_licensed=Yes
|license=GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/irena-flextool/flextool
|data_availability=some
|open_future=No
|modelling_software=GNU MathProg
|processing_software=Python, SQL
|External optimizer=HiGHS default (supports others)
|Additional software=Spine Toolbox
|GUI=Yes
|model_class=Multi-purpose
|sectors=All sectors (user can add more)
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Transport, Commercial sector, Other
|Energy carrier (Gas)=Natural gas, Biogas, Hydrogen
|Energy carrier (Liquid)=Diesel, Ethanol, Petrol
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Transmission
|Transfer (Gas)=Transmission
|Transfer (Heat)=Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=Yes
|Storage (Heat)=Yes
|User behaviour=User can define additional constraints to simulate some user behaviours
|Market models=Commodity and energy markets can be added (including CO2)
|decisions=dispatch, investment
|Changes in efficiency=Two-part (min. load and full load efficiency with a curve between))
|georegions=User dependent
|georesolution=User dependent
|timeresolution=Hour
|network_coverage=transmission, net transfer capacities
|Observation period=More than one year
|Additional dimensions (Ecological)=User dependent
|Additional dimensions (Economical)=Price time series
|Additional dimensions (Social)=-
|Additional dimensions (Other)=-
|math_modeltype=Optimization
|math_modeltype_shortdesc=Typically linear cost minimization, but unit online decisions can be mixed-integer linear (and effectively investment decisions too).
|math_objective=cost minimization
|deterministic=perfect foresight, but can use limited horizon
|is_suited_for_many_scenarios=Yes
|number_of_variables=Case dependent
|montecarlo=No
|computation_time_minutes=Case dependent
|computation_time_hardware=Case dependent
|computation_time_comments=Should be quite fast for linear problems.
|Model validation=Has been compared against PLEXOS
|Comment on model validation=Not published
|Interfaces=Spine Toolbox
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

IRENA FlexTool

2023-09-18T07:50:49Z

Juha Kiviluoma: First submission

{{Model
|Full_Model_Name=IRENA FlexTool
|Acronym=FlexTool
|author_institution=VTT Technical Research Centre of Finlan
|authors=Juha Kiviluoma, Arttu Tupala, Antti Soininen
|contact_persons=Juha Kiviluoma
|contact_email=juha.kiviluoma@vtt.fi
|website=https://irena-flextool.github.io/flextool/
|source_download=https://github.com/irena-flextool/flextool
|logo=Flextool logo.png
|text_description=IRENA FlexTool is an energy and power systems model for understanding the role of variable power generation in future energy systems. It performs capacity expansion planning as well as operational planning.
|Primary purpose=Expansion planning and scheduling
|Primary outputs=Investments, retirements, generation, demand, storage, transfer, prices, reserves, penalties/violations
|Support=https://github.com/irena-flextool/flextool/issues
|Framework=Uses Spine Toolbox
|User documentation=https://irena-flextool.github.io/flextool/
|Source of funding=IRENA, LeapRE
|Number of developers=3
|open_source_licensed=Yes
|license=GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/irena-flextool/flextool
|data_availability=some
|open_future=No
|modelling_software=GNU MathProg
|processing_software=Python, SQL
|External optimizer=HiGHS default (supports others)
|Primary purpose=Expansion planning and scheduling
|Additional software=Spine Toolbox
|GUI=Yes
|model_class=Multi-purpose
|sectors=All sectors (user can add more)
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Transport, Commercial sector, Other
|Energy carrier (Gas)=Natural gas, Biogas, Hydrogen
|Energy carrier (Liquid)=Diesel, Ethanol, Petrol
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Transmission
|Transfer (Gas)=Transmission
|Transfer (Heat)=Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=Yes
|Storage (Heat)=Yes
|User behaviour=User can define additional constraints to simulate some user behaviours
|Market models=Commodity and energy markets can be added (including CO2)
|decisions=dispatch, investment
|Changes in efficiency=Two-part (min. load and full load efficiency with a curve between))
|georegions=User dependent
|georesolution=User dependent
|timeresolution=Hour
|network_coverage=transmission, net transfer capacities
|Observation period=More than one year
|Additional dimensions (Ecological)=User dependent
|Additional dimensions (Economical)=Price time series
|Additional dimensions (Social)=-
|Additional dimensions (Other)=-
|math_modeltype=Optimization
|math_modeltype_shortdesc=Typically linear cost minimization, but unit online decisions can be mixed-integer linear (and effectively investment decisions too).
|math_objective=cost minimization
|deterministic=perfect foresight, but can use limited horizon
|is_suited_for_many_scenarios=Yes
|number_of_variables=Case dependent
|montecarlo=No
|computation_time_minutes=Case dependent
|computation_time_hardware=Case dependent
|computation_time_comments=Should be quite fast for linear problems.
|Model validation=Has been compared against PLEXOS
|Comment on model validation=Not published
|Interfaces=Spine Toolbox
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

File:Flextool logo.png

2023-09-18T07:33:40Z

Juha Kiviluoma: IRENA FlexTool logo

IRENA FlexTool logo

IRENA FlexTool

2023-09-18T07:30:40Z

Juha Kiviluoma: Created page with "{{Model |Full_Model_Name=IRENA FlexTool |Acronym=FlexTool |author_institution=VTT Technical Research Centre of Finlan |authors=Juha Kiviluoma, Arttu Tupala, Antti Soininen |op..."

{{Model
|Full_Model_Name=IRENA FlexTool
|Acronym=FlexTool
|author_institution=VTT Technical Research Centre of Finlan
|authors=Juha Kiviluoma, Arttu Tupala, Antti Soininen
|open_source_licensed=No
|model_source_public=No
|open_future=No
|GUI=No
|Storage (Gas)=No
|Storage (Heat)=No
|is_suited_for_many_scenarios=No
|montecarlo=No
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

Backbone

2022-03-14T11:19:30Z

Juha Kiviluoma:

{{Model
|Full_Model_Name=Backbone - energy systems model
|Acronym=Backbone
|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
|contact_persons=Juha Kiviluoma, Erkka Rinne
|contact_email=juha.kiviluoma@vtt.fi, erkka.rinne@vtt.fi
|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).
|Primary outputs=Costs, emissions, generation, consumption, transfers
|Support=Voluntary
|Framework=Backbone is a framework.
|User documentation=https://gitlab.vtt.fi/backbone/backbone/wikis/home
|Code documentation=Formulas: https://doi.org/10.3390/en12173388; Code documentation: within code
|Source of funding=Academy of Finland; ESIPP project (Ireland)
|Number of developers=8
|open_source_licensed=Yes
|license=GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0)
|model_source_public=Yes
|Link to source=https://gitlab.vtt.fi/backbone/backbone
|open_future=No
|modelling_software=GAMS
|processing_software=Spine Toolbox forthcoming. Currently Excel / SQL.
|GUI=No
|model_class=Framework
|sectors=All
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Transport, Commercial sector, Other
|Energy carrier (Gas)=Natural gas, Biogas, Hydrogen
|Energy carrier (Liquid)=Diesel, Ethanol, Petrol
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Transmission
|Transfer (Gas)=Transmission
|Transfer (Heat)=Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=Yes
|Storage (Heat)=Yes
|User behaviour=Markets only
|Market models=Any product can have a market; also reserve markets
|decisions=dispatch, investment
|Changes in efficiency=Piecewise linear (SOS2 and incremental), Time-dependent efficiency, environment dependent efficiency
|georegions=Depends on user
|georesolution=Depends on user
|timeresolution=Hour
|network_coverage=transmission, DC load flow, net transfer capacities
|Observation period=More than one year
|Additional dimensions (Ecological)=Depends on data
|Additional dimensions (Economical)=-
|Additional dimensions (Social)=-
|Additional dimensions (Other)=-
|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_objective=Cost minimization
|deterministic=Short-term and long-term stochastics are available
|is_suited_for_many_scenarios=Yes
|number_of_variables=1000000
|montecarlo=No
|computation_time_minutes=1000
|computation_time_comments=The implementation leads to reasonable computation time, but we plan to improve calculation time in future.
|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
|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).
|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.
|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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

Backbone

2022-03-14T11:18:56Z

Juha Kiviluoma:

{{Model
|Full_Model_Name=Backbone - energy systems model
|Acronym=Backbone
|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
|contact_persons=Juha Kiviluoma, Erkka Rinne
|contact_email=juha.kiviluoma@vtt.fi, erkka.rinne@vtt.fi
|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).
|Primary outputs=Costs, emissions, generation, consumption, transfers
|Support=Voluntary
|Framework=Backbone is a framework.
|User documentation=https://gitlab.vtt.fi/backbone/backbone/wikis/home
|Code documentation=Formulas: https://doi.org/10.3390/en12173388; Code documentation: within code
|Source of funding=Academy of Finland; ESIPP project (Ireland)
|Number of developers=8
|open_source_licensed=Yes
|license=GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0)
|model_source_public=Yes
|Link to source=https://gitlab.vtt.fi/backbone/backbone
|open_future=No
|modelling_software=GAMS
|processing_software=Spine Toolbox forthcoming. Currently Excel / SQL.
|GUI=No
|model_class=Framework
|sectors=All
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Transport, Commercial sector, Other
|Energy carrier (Gas)=Natural gas, Biogas, Hydrogen
|Energy carrier (Liquid)=Diesel, Ethanol, Petrol
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Transmission
|Transfer (Gas)=Transmission
|Transfer (Heat)=Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=Yes
|Storage (Heat)=Yes
|User behaviour=Markets only
|Market models=Any product can have a market; also reserve markets
|decisions=dispatch, investment
|Changes in efficiency=Piecewise linear (SOS2 and incremental), Time-dependent efficiency, environment dependent efficiency
|georegions=Depends on user
|georesolution=Depends on user
|timeresolution=Hour
|network_coverage=transmission, DC load flow, net transfer capacities
|Observation period=More than one year
|Additional dimensions (Ecological)=Depends on data
|Additional dimensions (Economical)=-
|Additional dimensions (Social)=-
|Additional dimensions (Other)=-
|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_objective=Cost minimization
|deterministic=Short-term and long-term stochastics are available
|is_suited_for_many_scenarios=Yes
|number_of_variables=1000000
|montecarlo=No
|computation_time_minutes=1000
|computation_time_comments=The implementation leads to reasonable computation time, but we plan to improve calculation time in future.
|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
|report_references=Please see for 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).
|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.
|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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

Backbone

2022-03-14T11:14:08Z

Juha Kiviluoma:

{{Model
|Full_Model_Name=Backbone - energy systems model
|Acronym=Backbone
|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
|contact_persons=Juha Kiviluoma, Erkka Rinne
|contact_email=juha.kiviluoma@vtt.fi, erkka.rinne@vtt.fi
|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).
|Primary outputs=Costs, emissions, generation, consumption, transfers
|Support=Voluntary
|Framework=Backbone is a framework.
|User documentation=https://gitlab.vtt.fi/backbone/backbone/wikis/home
|Code documentation=Formulas: https://doi.org/10.3390/en12173388; Code documentation: within code
|Source of funding=Academy of Finland; ESIPP project (Ireland)
|Number of developers=8
|open_source_licensed=Yes
|license=GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0)
|model_source_public=Yes
|Link to source=https://gitlab.vtt.fi/backbone/backbone
|open_future=No
|modelling_software=GAMS
|processing_software=Spine Toolbox forthcoming. Currently Excel / SQL.
|GUI=No
|model_class=Framework
|sectors=All
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Transport, Commercial sector, Other
|Energy carrier (Gas)=Natural gas, Biogas, Hydrogen
|Energy carrier (Liquid)=Diesel, Ethanol, Petrol
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Transmission
|Transfer (Gas)=Transmission
|Transfer (Heat)=Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=Yes
|Storage (Heat)=Yes
|User behaviour=Markets only
|Market models=Any product can have a market; also reserve markets
|decisions=dispatch, investment
|Changes in efficiency=Piecewise linear (SOS2 and incremental), Time-dependent efficiency, environment dependent efficiency
|georegions=Depends on user
|georesolution=Depends on user
|timeresolution=Hour
|network_coverage=transmission, DC load flow, net transfer capacities
|Observation period=More than one year
|Additional dimensions (Ecological)=Depends on data
|Additional dimensions (Economical)=-
|Additional dimensions (Social)=-
|Additional dimensions (Other)=-
|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_objective=Cost minimization
|deterministic=Short-term and long-term stochastics are available
|is_suited_for_many_scenarios=Yes
|number_of_variables=1000000
|montecarlo=No
|computation_time_minutes=1000
|computation_time_comments=The implementation leads to reasonable computation time, but we plan to improve calculation time in future.
|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
|report_references=
Please see for 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

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).
|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.
|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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

Backbone

2020-01-20T08:37:57Z

Juha Kiviluoma: First submission of Backbone description.

{{Model
|Full_Model_Name=Backbone - energy systems model
|Acronym=Backbone
|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
|contact_persons=Juha Kiviluoma, Erkka Rinne
|contact_email=juha.kiviluoma@vtt.fi, erkka.rinne@vtt.fi
|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).
|Primary outputs=Costs, emissions, generation, consumption, transfers
|Support=Voluntary
|Framework=Backbone is a framework.
|User documentation=https://gitlab.vtt.fi/backbone/backbone/wikis/home
|Code documentation=Formulas: https://doi.org/10.3390/en12173388; Code documentation: within code
|Source of funding=Academy of Finland; ESIPP project (Ireland)
|Number of developers=8
|open_source_licensed=Yes
|license=GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0)
|model_source_public=Yes
|Link to source=https://gitlab.vtt.fi/backbone/backbone
|open_future=No
|modelling_software=GAMS
|processing_software=Spine Toolbox forthcoming. Currently Excel / SQL.
|GUI=No
|model_class=Framework
|sectors=All
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Transport, Commercial sector, Other
|Energy carrier (Gas)=Natural gas, Biogas, Hydrogen
|Energy carrier (Liquid)=Diesel, Ethanol, Petrol
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Transmission
|Transfer (Gas)=Transmission
|Transfer (Heat)=Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=Yes
|Storage (Heat)=Yes
|User behaviour=Markets only
|Market models=Any product can have a market; also reserve markets
|decisions=dispatch, investment
|Changes in efficiency=Piecewise linear (SOS2 and incremental), Time-dependent efficiency, environment dependent efficiency
|georegions=Depends on user
|georesolution=Depends on user
|timeresolution=Hour
|network_coverage=transmission, DC load flow, net transfer capacities
|Observation period=More than one year
|Additional dimensions (Ecological)=Depends on data
|Additional dimensions (Economical)=-
|Additional dimensions (Social)=-
|Additional dimensions (Other)=-
|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_objective=Cost minimization
|deterministic=Short-term and long-term stochastics are available
|is_suited_for_many_scenarios=Yes
|number_of_variables=1000000
|montecarlo=No
|computation_time_minutes=1000
|computation_time_comments=The implementation leads to reasonable computation time, but we plan to improve calculation time in future.
|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
|report_references=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

Rasku & Kiviluoma. 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

|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
|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.
|Interfaces=Spine Toolbox forthcoming.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}