A novel methodology for forecasting gas supply reliability of natural gas pipeline systems

Feng CHEN, Changchun WU

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PDF(790 KB)
Front. Energy ›› 2020, Vol. 14 ›› Issue (2) : 213-223. DOI: 10.1007/s11708-020-0672-5
RESEARCH ARTICLE

A novel methodology for forecasting gas supply reliability of natural gas pipeline systems

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Abstract

In this paper, a novel systematic and integrated methodology to assess gas supply reliability is proposed based on the Monte Carlo method, statistical analysis, mathematical-probabilistic analysis, and hydraulic simulation. The method proposed has two stages. In the first stage, typical scenarios are determined. In the second stage, hydraulic simulation is conducted to calculate the flow rate in each typical scenario. The result of the gas pipeline system calculated is the average gas supply reliability in each typical scenario. To verify the feasibility, the method proposed is applied for a real natural gas pipelines network system. The comparison of the results calculated and the actual gas supply reliability based on the filed data in the evaluation period suggests the assessment results of the method proposed agree well with the filed data. Besides, the effect of different components on gas supply reliability is investigated, and the most critical component is identified. For example, the 48th unit is the most critical component for the SH terminal station, while the 119th typical scenario results in the most severe consequence which causes the loss of 175.61×104 m3 gas when the 119th scenario happens. This paper provides a set of scientific and reasonable gas supply reliability indexes which can evaluate the gas supply reliability from two dimensions of quantity and time.

Keywords

natural gas pipeline system / gas supply reliability / evaluation index / Monte Carlo method / hydraulic simulation

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Feng CHEN, Changchun WU. A novel methodology for forecasting gas supply reliability of natural gas pipeline systems. Front. Energy, 2020, 14(2): 213‒223 https://doi.org/10.1007/s11708-020-0672-5

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