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Development of functionalities for improved storage modelling in OSeMOSYS

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  • Palombelli, Andrea
  • Gardumi, Francesco
  • Rocco, MatteoVincenzo
  • Howells, Mark
  • Colombo, Emanuela

Abstract

Integrated systems modelling has provided insights on pathways for the sustainable use of energy, land and water resources. Among the existing modelling tools, some have been widely used to engage policy makers, due to their open source and basic structure. Yet, in the attempt of simplifying the representation of integrated systems, essential features significantly influencing the dynamics between systems may have been left aside. This study proposes an improved formulation of the Open Source Energy Modelling System (OSeMOSYS), for a better representation of energy and resource storage processes. In particular, the focus of this work is the storage losses introduction for both dams for hydropower generation and batteries for electricity storage. The modifications were applied to a case study representing key features of both a developed and developing country. The results highlight that, with low additional computational effort, a much more accurate representation of the storage technologies can be achieved. Despite the introduction of losses, renewable energy technologies tend to have a high penetration in the future energy mix thanks to storage applications that remedy their unpredictability and seasonality.

Suggested Citation

  • Palombelli, Andrea & Gardumi, Francesco & Rocco, MatteoVincenzo & Howells, Mark & Colombo, Emanuela, 2020. "Development of functionalities for improved storage modelling in OSeMOSYS," Energy, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301328
    DOI: 10.1016/j.energy.2020.117025
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    Cited by:

    1. Henke, Hauke T.J. & Gardumi, Francesco & Howells, Mark, 2022. "The open source electricity Model Base for Europe - An engagement framework for open and transparent European energy modelling," Energy, Elsevier, vol. 239(PA).
    2. Zhang, Jiaqi & Tian, Guang & Chen, Xiangyu & Liu, Pei & Li, Zheng, 2023. "A chance-constrained programming approach to optimal planning of low-carbon transition of a regional energy system," Energy, Elsevier, vol. 278(PA).
    3. 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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