Catalog Home Page

Optimal allocation of distributed generation in distributed network with genetic algorithm

Ma, Yunyi (2015) Optimal allocation of distributed generation in distributed network with genetic algorithm. Honours thesis, Murdoch University.

[img]
Preview
PDF - Whole Thesis
Download (1MB)

Abstract

With the higher and higher demand by people for the quality and reliability of the power supply, the centralized power grid cannot meet the requirements. Distributed generation access to the distributed network is an inevitable trend of the development of power industry. It is also one of the important aspects of the development of a smart grid. Distributed Generation is a small scale, low investment and clean power resource. Installing distributed generation in the network can also have positive effects of the networks, such as lower losses, stable voltage and so on; these effects are related to the location and sizing of distributed generation. So this thesis is about optimal location and sizing of distributed generation in distributed network.

At first, I introduce the definition of distributed generation and some common types of DG, such as wind power and PV power. Using Power factory simulate IEEE14 system was simulated to find the how the DG affects the power losses and voltage quality of the network.

According to the analysis above, a model was built with the lowest investment and power losses and the highest voltage quality based on IEEE 14 buses system. The model was simulated in Power Factory; calculated the power flow using Power factory and used adaptive genetic algorithm to solve the optimal problem. The results of the case study will prove that installing distributed generation can decrease the power losses and improve the voltage quality.

Finally, from the practical applications aspect, some directions for future research in this problem have been indicated.

Publication Type: Thesis (Honours)
Murdoch Affiliation: School of Engineering and Information Technology
Supervisor: Crebbin, Gregory
URI: http://researchrepository.murdoch.edu.au/id/eprint/29870
Item Control Page Item Control Page

Downloads

Downloads per month over past year