Sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization

This project proposes an optimal sizing of standalone pv-battery-diesel hybrid system using particle swarm optimization (pso) algorithm. generally, standalone system generates power using diesel generator offers a continuous and reliable source of energy. however, diesel generator is only operating...

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Main Author:	Siew, Hon Chong
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99503/1/SiewHonChongMKE2021.pdf
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id	my-utm-ep.99503
record_format	uketd_dc
spelling	my-utm-ep.995032023-02-27T07:58:17Z Sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization 2021 Siew, Hon Chong TK Electrical engineering. Electronics Nuclear engineering This project proposes an optimal sizing of standalone pv-battery-diesel hybrid system using particle swarm optimization (pso) algorithm. generally, standalone system generates power using diesel generator offers a continuous and reliable source of energy. however, diesel generator is only operating efficiently for a considerable load demand but have low performance when the load is well below its rated capacity. in addition to that, the system operating & maintenance costs and co2 emission level are high. the main objective of this project is to determine an optimal configuration of the proposed standalone hybrid system to meet the targeted reliability index of the system while having the lowest cost of energy (coe). in this project, electrical load demand profile and meteorological data over a year are collected to form input parameters for the proposed hybrid system. a function of rule-based energy management strategy (ems) of the hybrid system, incorporating all the mathematical modelling of the system components is developed using matlab scripts. this function ties back to the main program of particle swarm optimization (pso), a renowned stochastic optimization algorithm that is also developed using matlab scripts. this project uses loss of power supply probability (lpsp) and cost of energy (coe) as objective functions. optimal sizing was performed using pso algorithm and the result shows the optimal configuration obtained has satisfied the targeted lpsp of 1% with the lowest coe of s$0.65 kwh. 2021 Thesis http://eprints.utm.my/id/eprint/99503/ http://eprints.utm.my/id/eprint/99503/1/SiewHonChongMKE2021.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149847 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Siew, Hon Chong Sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization
description	This project proposes an optimal sizing of standalone pv-battery-diesel hybrid system using particle swarm optimization (pso) algorithm. generally, standalone system generates power using diesel generator offers a continuous and reliable source of energy. however, diesel generator is only operating efficiently for a considerable load demand but have low performance when the load is well below its rated capacity. in addition to that, the system operating & maintenance costs and co2 emission level are high. the main objective of this project is to determine an optimal configuration of the proposed standalone hybrid system to meet the targeted reliability index of the system while having the lowest cost of energy (coe). in this project, electrical load demand profile and meteorological data over a year are collected to form input parameters for the proposed hybrid system. a function of rule-based energy management strategy (ems) of the hybrid system, incorporating all the mathematical modelling of the system components is developed using matlab scripts. this function ties back to the main program of particle swarm optimization (pso), a renowned stochastic optimization algorithm that is also developed using matlab scripts. this project uses loss of power supply probability (lpsp) and cost of energy (coe) as objective functions. optimal sizing was performed using pso algorithm and the result shows the optimal configuration obtained has satisfied the targeted lpsp of 1% with the lowest coe of s$0.65 kwh.
format	Thesis
qualification_level	Master's degree
author	Siew, Hon Chong
author_facet	Siew, Hon Chong
author_sort	Siew, Hon Chong
title	Sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization
title_short	Sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization
title_full	Sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization
title_fullStr	Sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization
title_full_unstemmed	Sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization
title_sort	sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/99503/1/SiewHonChongMKE2021.pdf
_version_	1776100606744723456

Sizing of standalone photovoltaic-battery-diesel hybrid system with a rule-based energy management strategy using particle swarm optimization

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