A Computational Intelligent Algorithm For Solving Unit Commitment Problem

Unit Commitment (UC) is the task in finding the most optimum generating schedule of each generating unit. In determining UC, Distributed Generation (DG) would be one of the factors that would influence the outcome of power generation as it would deploy small – scaled technologies nearer to consum...

Full description

Saved in:
Bibliographic Details
Main Author: Aiza Nazifa Binti Mohd Zahari
Format:
Language:English
Published: 2023
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-20616
record_format dspace
spelling my.uniten.dspace-206162023-05-05T05:09:11Z A Computational Intelligent Algorithm For Solving Unit Commitment Problem Aiza Nazifa Binti Mohd Zahari Full Thesis Format Unit Commitment (UC) is the task in finding the most optimum generating schedule of each generating unit. In determining UC, Distributed Generation (DG) would be one of the factors that would influence the outcome of power generation as it would deploy small – scaled technologies nearer to consumers. It would be able to compensate on shortcomings of the power network i.e. power shortage or line interruption. A traditional DG layout would require the use of only Non – Renewable Energy source (NREs) such as coal that would have a significant impact on the environment. This paper will be focusing on the integration of Renewable Energy Source (RES) which is Solar and NREs which would be Micro Gas Turbine (MGT) into the DG layout with the use of Mixed Integer Linear Programming (MILP) to solve the mathematical problem. The integration of both energy source would reduce the operational cost. Results obtained would also support that the power output from this planning would be able to meet with the ever changing load demands. 2023-05-03T15:08:45Z 2023-05-03T15:08:45Z 2019-10 https://irepository.uniten.edu.my/handle/123456789/20616 en application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
topic Full Thesis Format
spellingShingle Full Thesis Format
Aiza Nazifa Binti Mohd Zahari
A Computational Intelligent Algorithm For Solving Unit Commitment Problem
description Unit Commitment (UC) is the task in finding the most optimum generating schedule of each generating unit. In determining UC, Distributed Generation (DG) would be one of the factors that would influence the outcome of power generation as it would deploy small – scaled technologies nearer to consumers. It would be able to compensate on shortcomings of the power network i.e. power shortage or line interruption. A traditional DG layout would require the use of only Non – Renewable Energy source (NREs) such as coal that would have a significant impact on the environment. This paper will be focusing on the integration of Renewable Energy Source (RES) which is Solar and NREs which would be Micro Gas Turbine (MGT) into the DG layout with the use of Mixed Integer Linear Programming (MILP) to solve the mathematical problem. The integration of both energy source would reduce the operational cost. Results obtained would also support that the power output from this planning would be able to meet with the ever changing load demands.
format
author Aiza Nazifa Binti Mohd Zahari
author_facet Aiza Nazifa Binti Mohd Zahari
author_sort Aiza Nazifa Binti Mohd Zahari
title A Computational Intelligent Algorithm For Solving Unit Commitment Problem
title_short A Computational Intelligent Algorithm For Solving Unit Commitment Problem
title_full A Computational Intelligent Algorithm For Solving Unit Commitment Problem
title_fullStr A Computational Intelligent Algorithm For Solving Unit Commitment Problem
title_full_unstemmed A Computational Intelligent Algorithm For Solving Unit Commitment Problem
title_sort computational intelligent algorithm for solving unit commitment problem
publishDate 2023
_version_ 1806427598233796608
score 13.214268