Open AccessDistributed generation placement in distributive substations analysis using Markov Chain Monte Carlo model considering the reliability of power supply
Author(s) -
Natasha Dimishkovska,
Atanas Iliev
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/878/1/012013
Subject(s) - reliability engineering , reliability (semiconductor) , monte carlo method , markov chain , transformer , matlab , distributed generation , computer science , distributed power , power (physics) , distributive property , markov chain monte carlo , generator (circuit theory) , engineering , electrical engineering , voltage , renewable energy , mathematics , statistics , physics , quantum mechanics , machine learning , pure mathematics , operating system
Proper operation of the power substations is of great importance for power network reliability, stability and uninterrupted power supply. Distributed generation provides higher reliability in power supply, but still, there are contingencies in the electric power production and supply process, which lead to outages in the power supply. In this paper, a method for substations’ reliability estimation with distributed generation is presented based on Markov Chain Monte Carlo method. The method considers the possible substation operation states and using random number generator in MATLAB, it simulates faults and calculates the substations’ reliability. The method is demonstrated on two cases of 110/35 kV substations, each consisting of two transformers and distributed generator, analysing the best placement for the distributed generation.