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Evaluation method of active distribution network resilience considering the power supply recovery and interaction of subareas

机译:考虑蛛网电源恢复和相互作用的主动分配网络抵御能力评估方法

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Extreme weather events lead to large-scale blackouts, resulting in huge economic losses, and a reasonable resilience evaluation system can direct effective methods to reduce these losses. In this paper, an evaluation index system of active distribution network (ADN) resilience is constructed based on analytic hierarchy process considering the supporting role of distributed resources in extreme conditions. Firstly, ADN is divided into several critical and uncritical subareas according to the load characteristics. Based on the fault recovery characteristics of subareas, the resilience indexes such as fault recovery time, outrage time and energy loss percentage are proposed. Secondly, the piecewise linear function is used to normalize the resilience indexes, weights are given to calculate the resilience score of each subarea due to the different load characteristics. Considering the interaction between subareas, the complex correlation coefficient method is used to determine the weight of them and calculate the resilience score of distribution network. Finally, Monte Carlo method is used to simulate the occurrence of extreme weather. The method based on IEEE33 bus example can quantitatively analyze the power supply recovery ability of each subarea and ADN under extreme conditions.
机译:极端天气事件导致大规模的停电,导致经济损失巨大,合理的弹性评估系统可以指导有效的方法来减少这些损失。在本文中,考虑到在极端条件下分布式资源的支持作用,构建了主动分配网络(ADN)弹性的评估指标系统。首先,根据负载特性,ADN被分成几个临界和非临界蛛网。基于Subareas的故障恢复特性,提出了诸如故障恢复时间,愤怒时间和能量损失百分比的弹性指标。其次,分段线性函数用于归一化弹性指标,给出重量,以计算由于不同的负载特性而导致每个子区域的弹性得分。考虑到蛛网中的相互作用,复杂的相关系数方法用于确定它们的权重,并计算分配网络的恢复性评分。最后,Monte Carlo方法用于模拟极端天气的发生。基于IEE33总线示例的方法可以在极端条件下定量分析每个子区域和ADN的电源恢复能力。

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