Probabilistic Load Flow Evaluation For Electric Power System Operation And Control

Probabilistic load flow analysis is a tools that has capability to handle the uncertainties in power system network.However,traditional tools only find the network parameters for a specific operating condition that is not enough.In this dissertation,probabilistic load flow aanlysis has been put forward in order to solve probabilistic problem related to power system.In order to solve related problems,different methods has been implemented and results has been compare with traditional methods,but significant results has been shown with improve Latin hypercube sampling and Copula theory.This method addresses the linear and non-linear dependence between random variables more efficiently and accurately than other methods.The proposed method is nearly unconstrained to the marginal probability distribution types of the input random variables.The dependency between the input random variables is established using Copula theory in this work.An improved Latin hypercube sampling is adopted due to the real discrete data.Uncertainty and dependence factors are considered to access the load flow of the power system accurately and comprehensively.The validity of the probability distribution between the correlated random variables is evaluated by adopting the power output of wind farms located in New Jersey.The effectiveness and accuracy of the proposed model were investigated using the comparative test in modified IEEE 14-bus and IEEE 118-bus test systems.