Title :
Optimal Distributed Generation placement in distribution network
Author :
Vukobratovic, Marko ; Hederic, Zeljko ; Hadziselimovic, Miralem
Author_Institution :
Dept. of Power Syst., J.J. Strossmayer Univ. of Osijek, Osijek, Croatia
Abstract :
This paper presents a method for optimal Distributed Generation placement with goal of reducing active power system losses and voltage level regulation. Active power losses in radial distribution network are determined using an Artificial Neural Network (ANN) by simultaneous formulation for the determination process based on voltage level control and injected power. Adequate installed power of distributed generation and the appropriate terminal for distributed generation utilization are selected by means of a genetic algorithm (GA), performed in a distinct manner that fits the type of decision-making assignment. The training data for ANN is obtained by means of load flow simulation performed in DIgSILENT PowerFactory software on a part of the Croatian distribution network. The active power losses and voltage conditions are simulated for various operation scenarios in which the back propagation ANN model has been tested to predict the power losses and voltage levels for each system terminal, and GA is used to determine the optimal terminal for distributed generation placement.
Keywords :
backpropagation; decision making; distributed power generation; genetic algorithms; neural nets; power distribution control; power engineering computing; power generation control; voltage control; ANN training data; Croatian distribution network; DIgSILENT PowerFactory software; GA; active power system loss reduction; artificial neural network; back propagation ANN model; decision-making assignment; determination process; genetic algorithm; injected power; load flow simulation; optimal distributed generation placement; radial distribution network; system terminal; voltage conditions; voltage level control; voltage level regulation; Artificial neural networks; Distributed power generation; Optimization; Power systems; Sociology; Statistics; Training; Artificial Neural Networks; Distributed generation; Genetic Algorithm; Power losses reduction; Voltage control;
Conference_Titel :
Energy Conference (ENERGYCON), 2014 IEEE International
Conference_Location :
Cavtat
DOI :
10.1109/ENERGYCON.2014.6850572