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Backbone—An Adaptable Energy Systems Modelling Framework

Author

Listed:
  • Niina Helistö

    (Smart Energy and Transport Solutions, VTT Technical Research Centre of Finland Ltd, FI-02044 VTT Espoo, Finland)

  • Juha Kiviluoma

    (Smart Energy and Transport Solutions, VTT Technical Research Centre of Finland Ltd, FI-02044 VTT Espoo, Finland
    School of Electrical and Electronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland)

  • Jussi Ikäheimo

    (Smart Energy and Transport Solutions, VTT Technical Research Centre of Finland Ltd, FI-02044 VTT Espoo, Finland)

  • Topi Rasku

    (Smart Energy and Transport Solutions, VTT Technical Research Centre of Finland Ltd, FI-02044 VTT Espoo, Finland)

  • Erkka Rinne

    (Smart Energy and Transport Solutions, VTT Technical Research Centre of Finland Ltd, FI-02044 VTT Espoo, Finland)

  • Ciara O’Dwyer

    (School of Electrical and Electronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland)

  • Ran Li

    (School of Electrical and Electronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland)

  • Damian Flynn

    (School of Electrical and Electronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland)

Abstract

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). An application of the framework is demonstrated using a power system example, and Backbone is shown to produce results comparable to a commercial tool. However, the adaptability of Backbone further enables the creation and solution of energy systems models relatively easily for many different purposes and thus it improves on the available methodologies.

Suggested Citation

  • Niina Helistö & Juha Kiviluoma & Jussi Ikäheimo & Topi Rasku & Erkka Rinne & Ciara O’Dwyer & Ran Li & Damian Flynn, 2019. "Backbone—An Adaptable Energy Systems Modelling Framework," Energies, MDPI, vol. 12(17), pages 1-34, September.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3388-:d:263443
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    References listed on IDEAS

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    2. Luigi Bottecchia & Pietro Lubello & Pietro Zambelli & Carlo Carcasci & Lukas Kranzl, 2021. "The Potential of Simulating Energy Systems: The Multi Energy Systems Simulator Model," Energies, MDPI, vol. 14(18), pages 1-27, September.
    3. Lisa Göransson, 2023. "Balancing Electricity Supply and Demand in a Carbon-Neutral Northern Europe," Energies, MDPI, vol. 16(8), pages 1-27, April.
    4. Ikäheimo, Jussi & Weiss, Robert & Kiviluoma, Juha & Pursiheimo, Esa & Lindroos, Tomi J., 2022. "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system," Applied Energy, Elsevier, vol. 305(C).
    5. Finke, Jonas & Bertsch, Valentin & Di Cosmo, Valeria, 2023. "Exploring the feasibility of Europe’s renewable expansion plans based on their profitability in the market," Energy Policy, Elsevier, vol. 177(C).
    6. Luis Montero & Antonio Bello & Javier Reneses, 2020. "A New Methodology to Obtain a Feasible Thermal Operation in Power Systems in a Medium-Term Horizon," Energies, MDPI, vol. 13(12), pages 1-17, June.
    7. Helistö, Niina & Kiviluoma, Juha & Morales-España, Germán & O’Dwyer, Ciara, 2021. "Impact of operational details and temporal representations on investment planning in energy systems dominated by wind and solar," Applied Energy, Elsevier, vol. 290(C).
    8. Finke, Jonas & Bertsch, Valentin, 2023. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," Applied Energy, Elsevier, vol. 332(C).
    9. Liu, Qipeng & Li, Ran & Dereli, Recep Kaan & Flynn, Damian & Casey, Eoin, 2022. "Water resource recovery facilities as potential energy generation units and their dynamic economic dispatch," Applied Energy, Elsevier, vol. 318(C).
    10. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    11. Huckebrink, David & Bertsch, Valentin, 2022. "Decarbonising the residential heating sector: A techno-economic assessment of selected technologies," Energy, Elsevier, vol. 257(C).
    12. Gao, Xian & Knueven, Bernard & Siirola, John D. & Miller, David C. & Dowling, Alexander W., 2022. "Multiscale simulation of integrated energy system and electricity market interactions," Applied Energy, Elsevier, vol. 316(C).
    13. Helistö, Niina & Kiviluoma, Juha & Reittu, Hannu, 2020. "Selection of representative slices for generation expansion planning using regular decomposition," Energy, Elsevier, vol. 211(C).
    14. David Huckebrink & Valentin Bertsch, 2021. "Integrating Behavioural Aspects in Energy System Modelling—A Review," Energies, MDPI, vol. 14(15), pages 1-26, July.
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    16. Kumar, Shravan & Thakur, Jagruti & Gardumi, Francesco, 2022. "Techno-economic modelling and optimisation of excess heat and cold recovery for industries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

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