Application of differential evolution algorithm in transmission expansion planning
Transmission expansion planning has become a complicated procedure more than it was. The rapid growth of the transmission networks and the deregulation has introduced more objectives and uncertainties to the transmission network planners. As a result of that, new approach and criteria that can repla...
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my.utm.509072017-09-02T18:12:49Z http://eprints.utm.my/id/eprint/50907/ Application of differential evolution algorithm in transmission expansion planning Al Hamrouni, Ibrahim Khairuddin, Azhar Salem, M. TK Electrical engineering. Electronics Nuclear engineering Transmission expansion planning has become a complicated procedure more than it was. The rapid growth of the transmission networks and the deregulation has introduced more objectives and uncertainties to the transmission network planners. As a result of that, new approach and criteria that can replace the old ones are needed for TEP problem. The main goal of this process is to locate the additional transmission lines that must be added to meet the forecasted load in the system adequately with minimum cost. There have been several methods applied for this purpose; mathematical optimization methods, heuristic and Meta heuristic methods. This paper reviews the use of Meta heuristic method by the means of differential evolution algorithm (DEA) to solve this multi objective optimization problem. In addition, some suggestions have been made by the author that can make the DEA more efficient and applicable in the real world networks. 2013-10 Conference or Workshop Item PeerReviewed Al Hamrouni, Ibrahim and Khairuddin, Azhar and Salem, M. (2013) Application of differential evolution algorithm in transmission expansion planning. In: 2013 International Conference on Mechatronics, Applied Mechanics and Energy Engineering, MAMEE 2013, 27th-29th July 2013, Singapore. https://pure.utm.my/en/publications/application-of-differential-evolution-algorithm-in-transmission-e |
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TK Electrical engineering. Electronics Nuclear engineering Al Hamrouni, Ibrahim Khairuddin, Azhar Salem, M. Application of differential evolution algorithm in transmission expansion planning |
description |
Transmission expansion planning has become a complicated procedure more than it was. The rapid growth of the transmission networks and the deregulation has introduced more objectives and uncertainties to the transmission network planners. As a result of that, new approach and criteria that can replace the old ones are needed for TEP problem. The main goal of this process is to locate the additional transmission lines that must be added to meet the forecasted load in the system adequately with minimum cost. There have been several methods applied for this purpose; mathematical optimization methods, heuristic and Meta heuristic methods. This paper reviews the use of Meta heuristic method by the means of differential evolution algorithm (DEA) to solve this multi objective optimization problem. In addition, some suggestions have been made by the author that can make the DEA more efficient and applicable in the real world networks. |
format |
Conference or Workshop Item |
author |
Al Hamrouni, Ibrahim Khairuddin, Azhar Salem, M. |
author_facet |
Al Hamrouni, Ibrahim Khairuddin, Azhar Salem, M. |
author_sort |
Al Hamrouni, Ibrahim |
title |
Application of differential evolution algorithm in transmission expansion planning |
title_short |
Application of differential evolution algorithm in transmission expansion planning |
title_full |
Application of differential evolution algorithm in transmission expansion planning |
title_fullStr |
Application of differential evolution algorithm in transmission expansion planning |
title_full_unstemmed |
Application of differential evolution algorithm in transmission expansion planning |
title_sort |
application of differential evolution algorithm in transmission expansion planning |
publishDate |
2013 |
url |
http://eprints.utm.my/id/eprint/50907/ https://pure.utm.my/en/publications/application-of-differential-evolution-algorithm-in-transmission-e |
_version_ |
1643652881565351936 |
score |
13.251813 |