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Interval energy flow calculation method for electricity-heat-hydrogen integrated energy system considering the correlation between variables

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  • Chen, Yuxin
  • Jiang, Yuewen

Abstract

The uncertain operating state of integrated energy systems (IESs) increases significantly with the ever-increasing renewable energy penetration. In order to evaluate the operation state of IES more reasonably, this paper proposes an interval energy flow (IEF) method and applies it to an IES that utilizes hydrogen as an energy conversion carrier. Firstly, a steady-state energy flow model for an electricity-heat-hydrogen system is presented. Then, the parallelogram model is employed to describe the correlation between interval variables to acquire more accurate results. Furtherly, combined with the above two models, the IEF is transformed into minimum and maximum optimization models. Due to the complexity of nonlinear optimization calculation, the interval solution is procured by the alternating iterative method based on linear optimization and deterministic energy flow for the sake of accuracy and simplicity. Finally, a case study demonstrates the effectiveness of the proposed method, and a detailed discussion about the impact of correlation on interval solutions is given. The calculation speed of the proposed method is 6.5 times faster than Monte Carlo simulation. The interval ranges of state variables are narrowed significantly after considering correlation, with a maximum narrowing range of up to 63.08%.

Suggested Citation

  • Chen, Yuxin & Jiang, Yuewen, 2023. "Interval energy flow calculation method for electricity-heat-hydrogen integrated energy system considering the correlation between variables," Energy, Elsevier, vol. 263(PB).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pb:s0360544222025646
    DOI: 10.1016/j.energy.2022.125678
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    1. Zhang, Hui & Wang, Jiye & Zhao, Xiongwen & Yang, Jingqi & Bu sinnah, Zainab Ali, 2023. "Modeling a hydrogen-based sustainable multi-carrier energy system using a multi-objective optimization considering embedded joint chance constraints," Energy, Elsevier, vol. 278(C).

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