CSO Algorithm Applications in Optimal Load Management and Economic Dispatch

博士 === 國立高雄應用科技大學 === 電機工程系 === 99 === The aim of this research is to study the load management (LM) and economic dispatch decision for the Taiwanese industries through Cat Swarm Optimization (CSO) algorithm. CSO algorithm can be proposed to select optimal demand contract and drop the basic electric...

Full description

Bibliographic Details
Main Authors: Jung-Chin Chen, 陳榮進
Other Authors: Jong-Ching Hwang
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/28142409785772085646
id ndltd-TW-099KUAS8442007
record_format oai_dc
spelling ndltd-TW-099KUAS84420072015-10-16T04:02:47Z http://ndltd.ncl.edu.tw/handle/28142409785772085646 CSO Algorithm Applications in Optimal Load Management and Economic Dispatch CSO演算法應用在負載管理及經濟調度最佳化 Jung-Chin Chen 陳榮進 博士 國立高雄應用科技大學 電機工程系 99 The aim of this research is to study the load management (LM) and economic dispatch decision for the Taiwanese industries through Cat Swarm Optimization (CSO) algorithm. CSO algorithm can be proposed to select optimal demand contract and drop the basic electricity cost. Results indicated that the CSO is superior to Particle Swarm Optimization (PSO) in the fast convergence and better performance to find the global best solution, considering the same iteration time. Also the CSO algorithm is highly helpful to Taiwanese industries on the optimal LM decision. From the view point of demand side management, this research is to apply SCADA system and a feasible LM options for the Taiwanese industries to reduce the power cost. Also this study refers to provide decision-makers with useful LM strategies as reference. Finally, it is suggested that future research might explore in nonlinear optimization problem through CSO algorithm, as well as in engineering and power system. Jong-Ching Hwang 黃鐘慶 2011 學位論文 ; thesis 91 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 博士 === 國立高雄應用科技大學 === 電機工程系 === 99 === The aim of this research is to study the load management (LM) and economic dispatch decision for the Taiwanese industries through Cat Swarm Optimization (CSO) algorithm. CSO algorithm can be proposed to select optimal demand contract and drop the basic electricity cost. Results indicated that the CSO is superior to Particle Swarm Optimization (PSO) in the fast convergence and better performance to find the global best solution, considering the same iteration time. Also the CSO algorithm is highly helpful to Taiwanese industries on the optimal LM decision. From the view point of demand side management, this research is to apply SCADA system and a feasible LM options for the Taiwanese industries to reduce the power cost. Also this study refers to provide decision-makers with useful LM strategies as reference. Finally, it is suggested that future research might explore in nonlinear optimization problem through CSO algorithm, as well as in engineering and power system.
author2 Jong-Ching Hwang
author_facet Jong-Ching Hwang
Jung-Chin Chen
陳榮進
author Jung-Chin Chen
陳榮進
spellingShingle Jung-Chin Chen
陳榮進
CSO Algorithm Applications in Optimal Load Management and Economic Dispatch
author_sort Jung-Chin Chen
title CSO Algorithm Applications in Optimal Load Management and Economic Dispatch
title_short CSO Algorithm Applications in Optimal Load Management and Economic Dispatch
title_full CSO Algorithm Applications in Optimal Load Management and Economic Dispatch
title_fullStr CSO Algorithm Applications in Optimal Load Management and Economic Dispatch
title_full_unstemmed CSO Algorithm Applications in Optimal Load Management and Economic Dispatch
title_sort cso algorithm applications in optimal load management and economic dispatch
publishDate 2011
url http://ndltd.ncl.edu.tw/handle/28142409785772085646
work_keys_str_mv AT jungchinchen csoalgorithmapplicationsinoptimalloadmanagementandeconomicdispatch
AT chénróngjìn csoalgorithmapplicationsinoptimalloadmanagementandeconomicdispatch
AT jungchinchen csoyǎnsuànfǎyīngyòngzàifùzàiguǎnlǐjíjīngjìdiàodùzuìjiāhuà
AT chénróngjìn csoyǎnsuànfǎyīngyòngzàifùzàiguǎnlǐjíjīngjìdiàodùzuìjiāhuà
_version_ 1718090643725615104