With the rising penetration levels of random resources such as renewable energy (RE) and electric vehicles (EVs), the uncertainty of active distribution network (ADN) is inherently increasing. To deal with disturbances and enhance stable operation, many power electronic devices such as Soft Normally Open Point (SNOP) have been integrated to ADN. This paper proposes a novel node flexibility evaluation method of ADN for SNOP integration. Firstly, operation scenarios of ADN are generated based on the k-medoids and k-means clustering method. A hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) is used in the scenarios. The above is the demand assessment for the flexibility evaluation. Then, RE consumption, load satisfaction, voltage deviation and netload fluctuation of each node are obtained from scenarios and weighted by the information entropy weight method (EWM) to form the comprehensive flexibility index. Nodes are classified according to the evaluation results, which provide the integrated location of SNOP. Finally, a case study showed the differences between the proposed method and others.

中文翻译：

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SNOP集成的主动配电网节点柔性评估新方法

随着诸如可再生能源（RE）和电动汽车（EV）之类的随机资源的普及程度不断提高，有源配电网（ADN）的不确定性在本质上不断增加。为了应对干扰并增强稳定运行，许多电力电子设备（例如，软常开点（SNOP））已集成到ADN中。提出了一种新的用于SNOP集成的ADN节点灵活性评估方法。首先，基于k-medoids和k-means聚类方法生成ADN的运行场景。在方案中使用了基于粒子群优化（PSO）和模拟退火（SA）的混合算法。以上是对灵活性评估的需求评估。然后，可再生能源消耗，负载满意度，从场景中获得每个节点的电压偏差和净负载波动，并通过信息熵权法（EWM）进行加权，以形成综合的灵活性指标。根据评估结果对节点进行分类，从而提供SNOP的集成位置。最后，案例研究表明了所提出的方法与其他方法之间的差异。