Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm

A new topic of Zero Energy Building is getting famous in research area because of its goal of reaching zero carbon emission and low building cost. Renewable energy system is one of the ideas to achieve the objective of Zero Energy Building. Recently, Genetic Algorithm is widely used in many research...

Full description

Saved in:

Bibliographic Details
Main Author:	Bong, Julies Shu Ai
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/85875/1/JuliesBongShuAiMFS2017.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.85875
record_format	uketd_dc
spelling	my-utm-ep.858752020-07-30T07:38:16Z Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm 2017 Bong, Julies Shu Ai QA Mathematics A new topic of Zero Energy Building is getting famous in research area because of its goal of reaching zero carbon emission and low building cost. Renewable energy system is one of the ideas to achieve the objective of Zero Energy Building. Recently, Genetic Algorithm is widely used in many research area due to its capability to escape from a local minimal to obtain a better solution. In our study, Genetic Algorithm is chosen in sizing optimization of the number of photovoltaic, wind turbine and battery of a hybrid photovoltaic-wind-battery system. Besides, these numbers are used to minimize the total annual cost of the hybrid energy system towards the concept of Zero Energy Building. There are a few Genetic Algorithm parameters that need to be considered in the optimization process which is generation number, population size, crossover operator and mutation operator. Therefore, two Genetic Algorithm parameters will be analysed and optimized which is generation number and population size. All of the simulations are done by using Microsoft Visual Studio 2010. From the results of simulations, the best generation number and population size is 100 000 and 3 000 respectively. In summary, Genetic Algorithm is efficient in minimizing cost function of a hybrid photovoltaic-wind-battery system with its robustness property. 2017 Thesis http://eprints.utm.my/id/eprint/85875/ http://eprints.utm.my/id/eprint/85875/1/JuliesBongShuAiMFS2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:132603 masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Bong, Julies Shu Ai Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm
description	A new topic of Zero Energy Building is getting famous in research area because of its goal of reaching zero carbon emission and low building cost. Renewable energy system is one of the ideas to achieve the objective of Zero Energy Building. Recently, Genetic Algorithm is widely used in many research area due to its capability to escape from a local minimal to obtain a better solution. In our study, Genetic Algorithm is chosen in sizing optimization of the number of photovoltaic, wind turbine and battery of a hybrid photovoltaic-wind-battery system. Besides, these numbers are used to minimize the total annual cost of the hybrid energy system towards the concept of Zero Energy Building. There are a few Genetic Algorithm parameters that need to be considered in the optimization process which is generation number, population size, crossover operator and mutation operator. Therefore, two Genetic Algorithm parameters will be analysed and optimized which is generation number and population size. All of the simulations are done by using Microsoft Visual Studio 2010. From the results of simulations, the best generation number and population size is 100 000 and 3 000 respectively. In summary, Genetic Algorithm is efficient in minimizing cost function of a hybrid photovoltaic-wind-battery system with its robustness property.
format	Thesis
qualification_level	Master's degree
author	Bong, Julies Shu Ai
author_facet	Bong, Julies Shu Ai
author_sort	Bong, Julies Shu Ai
title	Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm
title_short	Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm
title_full	Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm
title_fullStr	Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm
title_full_unstemmed	Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm
title_sort	sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2017
url	http://eprints.utm.my/id/eprint/85875/1/JuliesBongShuAiMFS2017.pdf
_version_	1747818468935204864

Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm

Similar Items