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Failure probability estimation of the gas supply using a data-driven model in an integrated energy system
Applied Energy ( IF 11.2 ) Pub Date : 2018-09-18 , DOI: 10.1016/j.apenergy.2018.09.097
Xueqian Fu , Gengyin Li , Xiurong Zhang , Zheng Qiao

Probabilistic security evaluation is one of the academic frontiers in the research on energy system reliability. It is very important to evaluate the impact of gas systems on the power/heat system for practical engineering in gas turbine engine-based integrated energy systems. This paper proposes a data-driven model instead of a physical model to estimate the probabilities of the incident of insufficient gas supply suffered from weather uncertainty, which affects the reliability of gas turbine engine-based integrated energy systems. According to actual energy projections, it can be assumed that the uncertainty of intermittent wind power, load fluctuations, and variations in gas deliverability derives from fluctuating weather conditions such as the temperature and wind. The wind power, load, and gas consumption data in the integrated energy system and the gas supply data of the station are sufficient to accurately build a data-driven model. Traditional methods based on physical models include the Iman and Stein methods, the first-order reliability method, and the mixed Monte Carlo algorithm to judge the effectiveness of the proposed method. The results from three cases are a testimony to the accuracy and engineering feasibility of the proposed method. The calculation of a data-driven model is easier than that of a physical model, and its simplification is conducive to failure probability estimation in a real application.



中文翻译:

在集成能源系统中使用数据驱动模型估算供气故障概率

概率安全评估是能源系统可靠性研究的学术前沿之一。对于基于燃气轮机的集成能源系统中的实际工程,评估燃气系统对电力/热力系统的影响非常重要。本文提出了一种数据驱动的模型,而不是物理模型,以估计由于天气不确定性而导致供气不足的事件的概率,这将影响基于燃气轮机的集成能源系统的可靠性。根据实际的能源预测,可以假定间歇性风力发电,负荷波动以及气体输送能力变化的不确定性是由温度和风等波动的天气条件引起的。风力,负荷,综合能源系统中的耗气量数据和站点的供气数据足以准确地建立数据驱动的模型。基于物理模型的传统方法包括Iman和Stein方法,一阶可靠性方法以及混合Monte Carlo算法,以判断该方法的有效性。三种情况的结果证明了该方法的准确性和工程可行性。数据驱动模型的计算比物理模型更容易,其简化有助于实际应用中的故障概率估计。以及混合蒙特卡罗算法来判断所提方法的有效性。三种情况的结果证明了该方法的准确性和工程可行性。数据驱动模型的计算比物理模型更容易,其简化有助于实际应用中的故障概率估计。以及混合蒙特卡罗算法来判断所提方法的有效性。三种情况的结果证明了该方法的准确性和工程可行性。数据驱动模型的计算比物理模型更容易,其简化有助于实际应用中的故障概率估计。

更新日期:2018-09-18
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