Power grids are essential systems that should resist threats that can be generated by natural or artificial causes. To achieve a resilient system, the potential vulnerabilities have to be studied with detail to provide an impact estimation for the attacks. Also, to predict the elements of the grid that generate the most damage. In previous work, the authors have studied the effects of targeted attacks based in fault strategies under static conditions, however the integration of distributed energy resources makes it necessary evaluating the vulnerability with random variables. This paper presents an analysis of the influence of distributed generation and load variations in the vulnerability of the power system. The vulnerability framework developed in previous research was employed. A time series simulation was carried out with hourly data from irradiance and power demand. Degree, eigenvector, and Katz power traffic centralities were employed to obtain the unsatisfied load derived from the removal of the most central element. The unsatisfied load obtained presented a standard deviation between 0.1682 and 0.1867, and no significant difference was observed between the centralities employed. In addition, a factorial experiment was carried out for two factors: the generation level and the demand level. Twenty five scenarios were simulated for each IEEE test system using five levels of each variable. The VPM maximum variation was 18.8% for levels of generation and load between 50 and 150%. The results indicate that variations in load an generation can significantly affect the vulnerability predictions of the power system under the conditions of this research. The increment in distributed generation can supply the power required to face the outage of important elements in the power system, and the worst case for vulnerability evaluation happens when the generation is minimum and the load is maximum. Therefore, vulnerability studies must account for different operating conditions, and represent UL and VPM as random variables.

电网是必不可少的系统，可以抵御自然或人为原因造成的威胁。为了实现一个有弹性的系统，必须详细研究潜在的漏洞，以提供对攻击的影响估计。此外，预测网格中产生最大伤害的元素。在之前的工作中，作者研究了静态条件下基于故障策略的针对性攻击的影响，但是分布式能源的整合使得有必要使用随机变量评估漏洞。本文分析了分布式发电和负荷变化对电力系统脆弱性的影响。采用了先前研究中开发的漏洞框架。使用来自辐照度和功率需求的每小时数据进行时间序列模拟。使用度数、特征向量和 Katz 电力交通中心性来获得从去除最中心元素中得出的不满意负载。获得的不满意载荷的标准偏差在 0.1682 和 0.1867 之间，并且在所采用的中心点之间没有观察到显着差异。此外，还针对发电水平和需求水平这两个因素进行了析因试验。使用每个变量的五个级别为每个 IEEE 测试系统模拟 25 个场景。对于 50% 和 150% 之间的发电和负载水平，VPM 的最大变化为 18.8%。结果表明，在本研究条件下，发电负荷的变化会显着影响电力系统的脆弱性预测。分布式发电的增量可以提供应对电力系统重要元件停电所需的电力，脆弱性评估的最坏情况发生在发电量最小负荷最大时。因此，漏洞研究必须考虑不同的操作条件，并将 UL 和 VPM 表示为随机变量。