考虑天气影响的电-气互联系统可靠性评估

doi:10.3969/j.issn.2097-0706.2023.02.004

摘要/Abstract

摘要：

电-气互联系统（EGIES）的可靠性评估是近几年广受关注的问题。由于不同天气情况下元件的故障率不同，因此相比于采用元件平均故障率得到的系统可靠性结果，考虑天气对互联系统可靠性的影响，将会得到更客观的可靠性指标。通过模糊聚类将系统中待分析电力元件所处的天气条件与已有记录中类似的天气聚为一类，求解待求元件所处天气与同类中其余天气样本之间的关联度；根据关联度及元件历史故障率求出待求天气状况下元件的故障率；建立EGIES的最优负荷削减模型，并基于蒙特卡洛法分析互联系统的可靠性。算例结果表明，所提方法能有效地求解元件在所处天气下的故障率。天气对EGIES可靠性的影响主要体现在电力子系统可靠性的变化，考虑天气后电力子系统可靠性有所下降，天然气系统可靠性受天气影响程度小于电力子系统。

关键词: 天气, 故障率, 电-气互联系统, 可靠性, 模糊聚类

Abstract:

The reliability evaluation for electric-gas integrated energy system（EGIES） has drawn wide attention in recent years.Since the failure rates of components under different weather conditions are different，the result of the evaluation for EGIES considering weather influence is more objective than that obtained based on the average failure rate of various components. Firstly，the test weather condition of a component in the system are clustered into the existing groups of similar weather conditions by fuzzy clustering，and the correlation degree between the test weather condition and other weather samples in the same group is calculated. Then，the failure rate of the component under the test weather condition is obtained based on its correlation degree and the historical failure rate. Finally，the reliability of the EGIES is analyzed based on the optimal load-reduction model of the system by Monte Carlo method. The calculation results show that the proposed method can effectively obtain the failure rate of a component under a certain weather condition. The influence of taking weather into consideration on the reliability of the power subsystem is more prominent than that of the nature gas subsystem，and the reliability of the power subsystem will be reduced by this influence.

Key words: weather, failure rate, electric-gas integrated energy system, reliability, fuzzy clustering

中图分类号:

TK01：TM71

孔振宇, 李宏仲. 考虑天气影响的电-气互联系统可靠性评估[J]. 综合智慧能源, 2023, 45(2): 30-36.

KONG Zhenyu, LI Hongzhong. Reliability evaluation on an electric-gas integrated energy system considering weather influence[J]. Integrated Intelligent Energy, 2023, 45(2): 30-36.

图/表 7

图1

图2

表1

表2

表3

表4

系统可靠性指标对比

场景	$p L O L$	$e E N S$ /（MW·h）	$p L O G L$	e_GNS/(m³·a^-1)
1	0.072 3	1 165.21	0.106 2	3 668.84
2	0.086 1	1 349.62	0.106 9	3 696.49

表4

图3

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天气样本	最大风速	最高气温	平均相对湿度	降水量	线路故障率	变压器故障率
1	1.000 0	0.068 9	0.000 1	0.521 1	0.051 7	0.008 0
2	0.212 3	0.898 9	0.967 9	0.099 3	0.033 9	0.014 3
3	0.045 6	0.921 1	0.883 1	0.033 4	0.032 6	0.013 9
4	0.849 3	0.069 9	0.294 1	0.668 8	0.047 2	0.008 2
5	0.702 1	0.032 3	0.413 0	0.623 9	0.005 1	0.007 9
6	0.000 1	0.855 1	0.933 2	0.059 2	0.029 4	0.012 8
7	0.863 6	0.039 9	0.457 6	0.555 6	0.026 2	0.006 6
8	0.091 1	0.979 1	0.705 9	0.000 1	0.032 2	0.015 1

天气样本	输电线路故障率/%
天气样本	计算值	实际值	相对误差
7	2.73	2.62	4.13
8	3.17	3.22	1.41