Renewable energy sources (RESs) such as wind turbines (WTs) and photovoltaic (PV) cells have become more widespread in recent years due to their superior technical, economical, and environmental benefits. These types of energy sources are innately unpredictable, owing to the uncertain nature of their primary resources and bring about more challenges in the distribution networks. Moreover, there may be a high correlations between uncertain input variables which intensify the mentioned challenges. On the other hand, power electronic‐based devices have had an effective role in improving the performance of distribution networks. Distribution network flexible AC transmission system (D‐FACTS) devices such as distribution static compensator (D‐STATCOM) can be efficiently used for making the modern distribution networks with high penetration of RESs more flexible. In this article, probabilistic assessment of the D‐STATCOM operation is considered in the distribution networks, including RESs such as WTs and PV cells. For this purpose, the probabilistic extended forward‐backward load flow with D‐STATCOM modeling has been introduced. The uncertainties are considered in power demands, wind speed, and solar radiation. Also, the correlations between uncertain variables are taken into account. Probabilistic assessment for this problem is conducted by k‐means based data clustering method for the first time. In addition to the comparison of the results with the Latin hypercube sampling, the results are validated with the Monte Carlo simulation method as a reference technique. The IEEE 33‐node and 69‐node test networks are used as case studies and show the effectiveness of the proposed method.

中文翻译：

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考虑相关不确定变量的D-STATCOM操作的概率评估

近年来，诸如风力涡轮机（WTs）和光伏（PV）电池之类的可再生能源（RESs）由于其优越的技术，经济和环境效益而变得越来越广泛。这些类型的能源由于其主要资源的不确定性而天生就无法预测，并给配电网络带来了更多挑战。此外，不确定的输入变量之间可能存在高度相关性，从而加剧了上述挑战。另一方面，基于电力电子的设备在改善配电网络性能方面发挥了有效作用。配电网络灵活的交流输电系统（D‐FACTS）设备（例如配电静态补偿器（D‐STATCOM））可以有效地用于使具有较高RES渗透率的现代配电网络更加灵活。在本文中，配电网中考虑了D-STATCOM操作的概率评估，包括RES（例如WT和PV电池）。为此，引入了带有D-STATCOM建模的概率扩展正反潮流。在功率需求，风速和太阳辐射中考虑了不确定性。同样，不确定变量之间的相关性也要考虑在内。首次使用基于k均值的数据聚类方法对该问题进行了概率评估。除了将结果与拉丁超立方体采样进行比较之外，还使用蒙特卡罗模拟方法作为参考技术对结果进行了验证。IEEE 33节点和69节点测试网络被用作案例研究，并证明了该方法的有效性。