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Interval Energy Flow Analysis in Integrated Electrical and Natural-Gas Systems Considering Uncertainties

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  • Shouxiang Wang

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Shuangchen Yuan

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

As integrated electrical and natural-gas systems (IENGS) are popularized, the uncertainties brought by variation of electrical load, power generation, and gas load should not be ignored. The aim of this paper is to analyze the impact of those uncertain variables on the steady-state operation of the whole systems. In this paper, an interval energy flow model considering uncertainties was built based on the steady-state energy flow. Then, the Krawczyk–Moore interval iterative method was used to solve the proposed model. To obtain precise results of the interval model, interval addition and subtraction operations were performed by affine mathematics. The case study demonstrated the effectiveness of the proposed approach compared with Monte Carlo simulation. Impacts of uncertainties brought by the variation of electrical load, power generation, and gas load were analyzed, and the convergence of energy flow under different uncertainty levels of electrical load was studied. The results led to the conclusion that each kind of uncertainties would have an impact on the whole system. The proposed method could provide good insights into the operating of IENGS with those uncertainties.

Suggested Citation

  • Shouxiang Wang & Shuangchen Yuan, 2019. "Interval Energy Flow Analysis in Integrated Electrical and Natural-Gas Systems Considering Uncertainties," Energies, MDPI, vol. 12(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2043-:d:234983
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    References listed on IDEAS

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    Cited by:

    1. 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).

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