Probabilistic Power Flow Analysis Using Matlab Graphical User Interface (GUI)

In today's power systems, there are renewable energy sources such as wind energy systems and solar energy systems. Renewable energy sources cause extra power fluctuation in the system. Rising of the fluctuation incresas the uncertainties of the power system. Since deterministic methods that do not contain uncertainty because of using certain fixed values instead of probabilistic values, these methods can not give reliable results under uncertainties. Therefore, statistical load flow, also known as probabilistic load flow, has taken its place as a new title in the literature in order to overcome the deficiencies of conventional load flow methods which do not contain uncertainty. In this study, a comparative analysis of Monte Carlo simulation with Latin Hypercube sampling method and Unscented transformation methods are presented. These methods are compared with the results obtained from the classical Monte Carlo simulation method. IEEE 14 and 30 bus test systems and Ondokuz Mayis University campus distribution system were chosed as a test system for the application of the proposed methods. The results show that Unscented transformation method is faster and more reliable than the other two methods.

Automated summary

This study compares the Monte Carlo simulation with Latin Hypercube sampling method and Unscented transformation methods in power systems. The results show that the Unscented transformation method is faster and more reliable than the other two methods.