Backbone: An adaptable energy systems modelling framework

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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.
Original languageEnglish
Article number3388
Number of pages34
JournalEnergies
Volume12
Issue number17
DOIs
Publication statusPublished - 2 Sept 2019
MoE publication typeA1 Journal article-refereed

Funding

N. Helistö, J. Kiviluoma, J. Ikäheimo, T. Rasku, and E. Rinne acknowledge funding from the Academy of Finland project “Improving the value of variable and uncertain power generation in energy systems (VaGe)” (grant number 284973), which is part of the New Energy programme; as well as from the Strategic Research Council at the Academy of Finland, project “Transition to a resource efficient and climate neutral electricity system (EL-TRAN)” (grant number 314319). N. Helistö also acknowledges funding from the Jenny and Antti Wihuri Foundation. Ciara O’Dwyer has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 773505. Ran Li and J. Kiviluoma are supported by Science Foundation Ireland under the Strategic Partnership Programme Grant SFI/15/SPP/E3125.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • energy systems
  • investment planning
  • modelling tools
  • modelling framework
  • open source
  • power systems
  • stochastic programming
  • unit commitment
  • variable renewable energy

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