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Day-ahead and intra-day scheduling of integrated electricity-hydrogen-gas energy system considering spectral risk theory

Author

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  • Duan, Jiandong
  • Tian, Qinxing
  • Gao, Qi
  • Zhou, Zhipeng

Abstract

With the development of the new power systems, the issue of wind and solar power curtailment is increasingly prominent. The production and utilization of hydrogen can support the energy system with high renewable energy penetration. Therefore, this paper incorporates hydrogen coupling into the traditional integrated electricity-gas energy system and formulates a day-ahead and intra-day optimal scheduling model. Firstly, instead of using a constant coefficient, a piecewise function is developed to simulate the dynamic hydrogen production characteristics of the electrolyzer. Secondly, a multi-energy complementary integration model is established. In the integrated energy system, hydrogen is injected into natural gas pipelines via the hydrogen mixing node to power the gas turbine, and a novel hydrogen energy utilization method bypassing the methanation process is proposed. Finally, the spectral risk theory is introduced to assess the risk losses caused by uncertainties in the integrated energy system, comprehensively balance the operating cost and risk exposure. Six comparative case studies are designed to validate the effectiveness of the proposed scheduling model considering the dynamic hydrogen doping strategy and spectrum risk theory. Simulation results demonstrate that the diversified hydrogen utilization framework and the spectral risk assessment method significantly enhance system economics and renewable energy consumption.

Suggested Citation

  • Duan, Jiandong & Tian, Qinxing & Gao, Qi & Zhou, Zhipeng, 2025. "Day-ahead and intra-day scheduling of integrated electricity-hydrogen-gas energy system considering spectral risk theory," Energy, Elsevier, vol. 323(C).
    • Handle: RePEc:eee:energy:v:323:y:2025:i:c:s0360544225014136
    DOI: 10.1016/j.energy.2025.135771
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    4. Cheng, Hangyu & Chen, Jiahui & Jung, Seunghun & Kim, Young-Bae, 2025. "Hierarchical rolling optimization strategy for hybrid electric-hydrogen system based on deep reinforcement learning," Energy, Elsevier, vol. 338(C).

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