连续随机干扰下动态电力系统的快速Monte Carlo仿真
Fast Monte Carlo Simulation of Dynamic Power Systems Under Continuous Random Disturbances
摘要：
可再生能源发电产生的连续时间随机干扰对电力系统的动态行为有着重要的影响。在评估这种影响时，必须将扰动视为连续时间随机过程，而不是不随时间变化的随机变量，以确保准确性。蒙特卡罗仿真（MC）是一种非侵入性的方法来评估这种影响，可以在商业电力系统仿真软件上执行，并且易于电力公司使用，但是计算繁琐。拉丁超立方抽样（LHS）等快速抽样方法已被引入以加速随机变量的抽样，但还不能应用于连续扰动的抽样。为了克服这一局限性，本文提出了一种快速MC方法，使LHS能够加快连续扰动的采样速度，该方法基于扰动的Itô过程模型，并利用独立正态随机变量的函数逼近Itô过程。IEEE 39节点系统的算例表明，在估计系统动态响应的期望和方差时，该方法的收敛速度分别是传统MC的47.6倍和6.7倍。
英文摘要：
Continuous-time random disturbances from the renewable generation pose a significant impact on power system dynamic behavior. In evaluating this impact, the disturbances must be considered as continuous-time random processes instead of random variables that do not vary with time to ensure accuracy. Monte Carlo simulation (MCs) is a nonintrusive method to evaluate such impact that can be performed on commercial power system simulation software and is easy for power utilities to use, but is computationally cumbersome. Fast samplings methods such as Latin hypercube sampling (LHS) have been introduced to speed up sampling random variables, but yet cannot be applied to sample continuous disturbances. To overcome this limitation, this paper proposes a fast MCs method that enables the LHS to speed up sampling continuous disturbances, which is based on the Itô process model of the disturbances and the approximation of the Itô process by functions of independent normal random variables. A case study of the IEEE 39-Bus System shows that the proposed method is 47.6 and 6.7 times faster to converge compared to the traditional MCs in evaluating the expectation and variance of the system dynamic response.
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