Edit Model: Backbone

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Basic Information

Full model name:	The name of the model in full, not the abbreviation.
Acronym:	The short name of the model, this should be the same as the page title.
Author institution:	The organization that hosts the project. Select the lead institution if more that one organization is involved.
Authors:	A comma-separated list of "Firstname Lastname" pairs. For example: John Doe, Jill Doe, Max Mustermann
Current contact persons:	A comma-separated list of "Firstname Lastname" pairs. For example: John Doe, Jill Doe, Max Mustermann
Contact email address:	The email address of one contact person.
Website:	The website URL including the "http://" part. Ideally a link to the landing page for the project and not the homepage of the institution hosting the project.
Source download link:	The download URL including the "http://" part.
Logo:	Upload file The logo used by the project, if any.
Short 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). Describe your project in a few short sentences.
Primary purpose:	Record the primary purpose of your project.
Primary outputs:	List the primary outputs you expect from your project.
Support:	TOFIX
Framework:	TOFIX
Link to user documentation:	Add a URL to the user documentation you offer.
Link to code documentation:	Add a URL to the developer documentation you offer.
Source of funding:	List your funding organizations, if appropriate.
Number of developers:	Add the number of developers working on the project.
Number of users:	Add the number of modelers using the project. Modelers can be developers too.

Openness

Open Source licensed?	Is the model published under an open source license? We regard licenses from OSI (opensource.org) and The Open Definition (opendefinition.org) as open source. Please contact us if you are using another license. If you leave this dialog unchecked, this implies you are planning to release your model under an open license in due course.
If so, what license?
Source code directly downloadable?	Yes means that the source code can be downloaded anonymously and that registration or manual inquiry is not required.
Link to source code:
Data provided?	None all some Indicate if some or all of the datasets necessary to run the model are shipped alongside the model.
Planned to open up in the future?	Check this dialog if the model and data are not fully open at present, but you plan to open these up in the future. This field is ignored if the open source license field is set to yes.

Software

Modelling software:	A comma-separated list of the languages and perhaps also the key libraries and/or development environments used for modelling. Do not include solvers, they are the subject of a separate question.
Data processing software:	A comma-separated list of the languages and software used for processing and visualizing data.
External optimizer:	A comma-separated list of the solvers used by the project. If non-proprietary solvers can be deployed, then these should be listed too.
Primary purpose:	TOFIX
Additional software required:	A comma-separated list of additional software and related dependencies that may be required to develop and/or run the model.
GUI:	Yes if a graphical user interface is offered.

Coverage

Model class:	A short description indicating the class and/or scope of the model. For instance: Australian national electricity market, European transmission grid, distributed energy systems, smart grid simulation, web-based interactive, national planning, and so forth. Look at the data provided by existing projects for more hints.
Modeled sectors:	A comma-separated list of the economic sectors that are supported. For example: electricity, heat, gas, oil, mobility, household, carbon sequestration.
Modeled generation technologies:	Renewables Conventional Generation CHP Supported generation technologies. CHP and cogeneration are synomyms.
Demand sectors:	Households Industry Transport Commercial sector Other Supported demand sectors.
Modeled energy carriers (Gas):	Natural gas Biogas Hydrogen Supported energy carriers related to gaseous fuels.
Modeled energy carriers (Liquid):	Diesel Ethanol Petrol Supported energy carriers related to liquid fuels.
Modeled energy carriers (Solid):	Biomass Coal Lignite Uranium Supported energy carriers related to solid fuels.
Modeled energy carriers (Renewable):	Geothermal heat Hydro Sun Wind Supported renewable sources. Sun includes photovoltaic and solar thermal technologies.
Transport (Electricity):	Distribution Transmission Support for low-voltage distribution and high-voltage transmission.
Transport (Gas):	Distribution Transmission Support for low-pressure reticulation and high-pressure transmission.
Transport (Heat):	Distribution Transmission Support for district heat and similar.
Storage (Electricity):	Battery CAES Chemical Kinetic PHS Support for local and grid-based electricity storage. CAES is compressed air energy storage and PHS is pumped hydroelectric storage.
Storage (Gas):	Support for gas storage, either as a gas or liquid.
Storage (Heat):	Support for heat storage.
User behaviour:	A brief description of the representation of consumer behavior, perhaps using techniques from bounded rationality.
Market models:	A brief description of the representation of energy and carbon markets and their type.
Modeled decisions:	dispatch investment Dispatch decisions relate to system operations and investment decisions relate to plant commissioning and decommissioning.
Changes in efficiency:	Are plant efficiencies fixed or can they depend on the state of the environment and/or the plant in question?
Geographic regions covered:	A comma-separated list of regions covered by the model. Leave this blank if the model and data are not limited to a specific region. Examples include: USA, Canada, Germany, western Europe.
Geographic resolution:	The geographic resolution of the model. Examples include: country, federal states, NUTS 3 (a Eurostat designation), TSO regions, individual power stations.
Time resolution:	The timestep normally used by the model.
Network coverage:	transmission distribution AC load flow DC load flow net transfer capacities How are engineering networks represented for in your model? Multiple answers are possible. AC load flow refers to AC transmission and DC load flow refers to DC transmission.
Observation period:	Less than one month Less than one year More than one year The time horizon normally used by a model.
Additional dimensions (Ecological):	Can the model calculate and report greenhouse gas emissions, air pollution, water demand, and direct and indirect land use?
Additional dimensions (Economic):	Can the model calculate and report economic statistics, including: aggregate financial cost, net-present value, spot price time series?
Additional dimensions (Social):	Can the model estimate and report social statistics, such as energy sector employment?
Additional dimensions (Other):	Any other dimensions of significance.

Mathematical properties

Model type:	Optimization Simulation Other Agent-based Multiple answers are possible.
Short description of mathematical model type:	The model minimizes the objective function and includes constraints related to energy balance, emissions, unit operation, transfers, system operation, portfolio design, etc. Provide a brief description of the mathematics behind the model. This may range from simple accounting through to intertemporal mathematical optimization.
Math objective:	A comma-separated list of supported system objectives. For example: financial cost, economic welfare, greenhouse gas emissions.
Approach to uncertainty:	How is uncertainty dealt with by your model? Answers might include: deterministic, stochastic, stochastic hydro inflows but otherwise perfect foresight, monte carlo methods, sensitivity analysis.
Suited for many scenarios / monte-carlo:	Is the model able to be used for large-scale scenario analysis with hundreds or thousands of different parameter sets being investigated?
Number of variables:	How many variables might a typical model have? Please enter a number without special letters or characters.
Supports Monte Carlo simulation:	Does the model support Monte Carlo methods?
Computation time minutes:	How long might a typical model take to solve in minutes? Please enter a number without special letters or characters.
Hardware used for computation time:	A brief description of the hardware used to establish runtime benchmarks.
Comments regarding computation time:	You can add a comment regarding the computation time. For example, you could explain under what circumstances the given run time time would be typical.

References

Citation reference:	A single publication which can be used to cite the model. For example: Doe, John (2014). "An electricity market model for Europe", Journal of Modeling Sciences, Vol 10, Issue 80. The DOI is given next.
Citation doi:	The DOI identifier for the given reference, if available.
References to reports produced using the model:	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 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 References to reports produced using the model.
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). A selection of research questions that the model is capable of answering.
Larger scale usage:	TOFIX
Model validation:	List any procedures used to help ensure that the model is valid.
Comment on model validation:	Comment on procedures used to help ensure the model is valid.
Model specific properties:	List any properties that are special or unique to your model.

Model integration

Integrated models:	TOFIX
Interfaces:	List the interfaces that your model can offer to other models.
Integrating models:	TOFIX
Model input file format:	Briefly specify the data input format used by your model. For example: structured text, XML, CSV, JSON, pandas data structures, and Excel.
Model file format:	TOFIX
Model output file format:	Briefly specify the data output format used by your model.

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Edit Model: Backbone

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Openness

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Mathematical properties

References

Model integration

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