Abstract

The importance of probabilistic assessment in distribution systems is very high due to the increasing penetration of renewable energy sources (RESs) with their fluctuating behavior. Also, there are some other uncertain variables in distribution systems such as loads fluctuations. More awareness of the system state by considering many uncertainties provides more certainty in decision making and causes better risk management. Moreover, the distribution static compensator (DSTATCOM) has been recently implemented due to its efficient abilities in the distribution systems operation especially when RESs are integrated into them. This article investigates the probabilistic assessment of DSTATCOM operation in a distribution system using the k-means-based data clustering method (DCM). The uncertainty of load demands, wind speed, and solar radiation are considered in this study. The performance of DCM is compared to the Monte Carlo simulation (MCS) and Latin Hypercube sampling (LHS) methods in terms of accuracy and computational burden. The efficiency of k-means-based DCM is investigated in the IEEE 69-node test system.

摘要

由于可再生能源 (RES) 的渗透率越来越高，而且其波动行为，概率评估在配电系统中的重要性非常高。此外，配电系统中还有一些其他不确定变量，例如负载波动。通过考虑许多不确定性，更多地了解系统状态可以为决策提供更多确定性，并导致更好的风险管理。此外，配电静态补偿器 (DSTATCOM) 由于其在配电系统运行中的高效能力而最近已实施，特别是当 RES 集成到其中时。本文研究了使用k的配电系统中 DSTATCOM 操作的概率评估-基于均值的数据聚类方法（DCM）。本研究考虑了负载需求、风速和太阳辐射的不确定性。DCM 的性能在准确性和计算负担方面与蒙特卡罗模拟 (MCS) 和拉丁超立方体采样 (LHS) 方法进行了比较。在 IEEE 69 节点测试系统中研究了基于k均值的 DCM的效率。