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Integrated electricity and gas system modelling with hydrogen injections and gas composition tracking

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  • Saedi, Isam
  • Mhanna, Sleiman
  • Mancarella, Pierluigi

Abstract

Despite the direct physical coupling between them, electricity and gas networks were traditionally modelled and operated independently. However, the heavy reliance on gas-fired generators to balance intermittent generation from renewable energy sources (RES), and the promising role of green hydrogen in decarbonising the natural gas system, have prompted a paradigm shift towards integrated electricity and gas system (IEGS) modelling. While many previous studies investigated the role of hydrogen in future low-carbon energy systems, a detailed assessment of hydrogen system integration into the electricity and gas transmission networks is still not addressed. Therefore, this paper presents a novel IEGS model with green hydrogen injections and gas composition tracking. The electricity system is modelled as a unit commitment model, formulated as a mixed-integer linear programming problem, and the gas system is modelled as a steady-state optimal gas flow (OGF). The developed model is demonstrated on two sets of case studies. The first case study validates the proposed OGF methodology on a small meshed gas test network, whereas the second case study demonstrates the applicability of the overall IEGS model with green hydrogen injection on the large-scale, real-world electricity and gas transmission networks of the state of Victoria (Australia). Results show that the proposed methodology can accurately capture the variations in gas flow direction while maintaining the hydrogen molar fraction within limits under hydrogen injections from multiple locations. Moreover, the amount of injected hydrogen not only depends on the level of RES curtailment, but also on local gas network constraints and local demand.

Suggested Citation

  • Saedi, Isam & Mhanna, Sleiman & Mancarella, Pierluigi, 2021. "Integrated electricity and gas system modelling with hydrogen injections and gas composition tracking," Applied Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921009727
    DOI: 10.1016/j.apenergy.2021.117598
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    References listed on IDEAS

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    Cited by:

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    7. Wang, Yuwei & Song, Minghao & Jia, Mengyao & Li, Bingkang & Fei, Haoran & Zhang, Yiyue & Wang, Xuejie, 2023. "Multi-objective distributionally robust optimization for hydrogen-involved total renewable energy CCHP planning under source-load uncertainties," Applied Energy, Elsevier, vol. 342(C).

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