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Optimising distributed generation planning using hybrid artificial intelligence approach

Distributed Generation (DG) has become more recognised in the power sector due to its capability of reducing power loss, having low investments cost and most significantly, able to exploit renewable-energy resources. DG is a small scale source, which is not centrally planned or dispatched. It is gen...

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第一著者: Tan, Wen Shan
フォーマット: 学位論文
出版事項: 2013
主題:
Q Science
オンライン･アクセス:http://eprints.utm.my/id/eprint/42180/
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spelling my.utm.421802020-08-17T01:01:30Z http://eprints.utm.my/id/eprint/42180/ Optimising distributed generation planning using hybrid artificial intelligence approach Tan, Wen Shan Q Science Distributed Generation (DG) has become more recognised in the power sector due to its capability of reducing power loss, having low investments cost and most significantly, able to exploit renewable-energy resources. DG is a small scale source, which is not centrally planned or dispatched. It is generally directly connected to the power system near the point of the end users. The optimal siting and sizing of DG are required for maximising the DG potential benefits in a power system. This thesis proposes a multi-objective index-based approach to determine the optimal placement and size of multiple DG in distribution systems, including the voltage rise phenomenon. The proposed approach considers multiple technical aspects, such as the total real power losses, voltage profile, Mega Volt Ampere (MVA) intake by the grid and greenhouse gases emission. Two novel hybrid population-based algorithms: Hybrid Particle Swarm Optimisation-Gravitational Search Algorithm (PSOGSA) and Hybrid Genetic-Firefly Algorithm (GAFA) are introduced in this thesis. An analysis was carried out by using Matlab programming to reveal the validity of the hybrid algorithms on 69 bus systems, for both time invariant and time variant load models, in which the results were obtained by using pre-combined methods. The results show that the proposed algorithms are robust, efficient and capable of solving mixed integer nonlinear optimisation problem. Moreover, the PSOGSA has parallel structure that gave better quality results with lowest multi-objective performance index (MPI) values of 0.4322 and shorter 25 seconds processing time as compared to the sequential structure of GAFA, with lowest MPI values of 0.4439 and 40 seconds processing time. This indicates that placement of multiple DG is more beneficial than the placement of single DG. 2013 Thesis NonPeerReviewed Tan, Wen Shan (2013) Optimising distributed generation planning using hybrid artificial intelligence approach. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:77863
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science
spellingShingle Q Science
Tan, Wen Shan
Optimising distributed generation planning using hybrid artificial intelligence approach
description Distributed Generation (DG) has become more recognised in the power sector due to its capability of reducing power loss, having low investments cost and most significantly, able to exploit renewable-energy resources. DG is a small scale source, which is not centrally planned or dispatched. It is generally directly connected to the power system near the point of the end users. The optimal siting and sizing of DG are required for maximising the DG potential benefits in a power system. This thesis proposes a multi-objective index-based approach to determine the optimal placement and size of multiple DG in distribution systems, including the voltage rise phenomenon. The proposed approach considers multiple technical aspects, such as the total real power losses, voltage profile, Mega Volt Ampere (MVA) intake by the grid and greenhouse gases emission. Two novel hybrid population-based algorithms: Hybrid Particle Swarm Optimisation-Gravitational Search Algorithm (PSOGSA) and Hybrid Genetic-Firefly Algorithm (GAFA) are introduced in this thesis. An analysis was carried out by using Matlab programming to reveal the validity of the hybrid algorithms on 69 bus systems, for both time invariant and time variant load models, in which the results were obtained by using pre-combined methods. The results show that the proposed algorithms are robust, efficient and capable of solving mixed integer nonlinear optimisation problem. Moreover, the PSOGSA has parallel structure that gave better quality results with lowest multi-objective performance index (MPI) values of 0.4322 and shorter 25 seconds processing time as compared to the sequential structure of GAFA, with lowest MPI values of 0.4439 and 40 seconds processing time. This indicates that placement of multiple DG is more beneficial than the placement of single DG.
format Thesis
author Tan, Wen Shan
author_facet Tan, Wen Shan
author_sort Tan, Wen Shan
title Optimising distributed generation planning using hybrid artificial intelligence approach
title_short Optimising distributed generation planning using hybrid artificial intelligence approach
title_full Optimising distributed generation planning using hybrid artificial intelligence approach
title_fullStr Optimising distributed generation planning using hybrid artificial intelligence approach
title_full_unstemmed Optimising distributed generation planning using hybrid artificial intelligence approach
title_sort optimising distributed generation planning using hybrid artificial intelligence approach
publishDate 2013
url http://eprints.utm.my/id/eprint/42180/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:77863
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score 13.251813

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