Unit commitment solution using an optimized genetic system
This paper presents an investigation into the application of an optimized Genetic Algorithm (GA) to solve the Thermal Unit Commitment (UC) problem. A Parallel structure was first developed to handle the infeasibility problem in a structured and improved GA which provides an effective search process...
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my.utm.299062020-10-30T05:13:29Z http://eprints.utm.my/id/eprint/29906/ Unit commitment solution using an optimized genetic system Abookazemi, Kaveh Ahmad, Hussein Tavakolpour, Alireza Hassan, Mohammad Yusri TK Electrical engineering. Electronics Nuclear engineering This paper presents an investigation into the application of an optimized Genetic Algorithm (GA) to solve the Thermal Unit Commitment (UC) problem. A Parallel structure was first developed to handle the infeasibility problem in a structured and improved GA which provides an effective search process and therefore greater economy. The proposed methodology resulted in a better performance with faster operation by using both computational methods and classification of unit characteristics. Typical constraints such as system power balance, minimum up and down times, start-up and shut-down ramps, have also been considered. A number of important parameters (standard and new parameters) of the UC problem have been identified. The proposed method is implemented and tested using a C# program. The tests are carried out using two systems including 10 and 20 units during a scheduling period of 24 h. The results are finally compared with those obtained from genetic schemes in other similar investigations through which the effectiveness of the proposed scheme is affirmed. Elsevier Ltd. 2011-05 Article PeerReviewed Abookazemi, Kaveh and Ahmad, Hussein and Tavakolpour, Alireza and Hassan, Mohammad Yusri (2011) Unit commitment solution using an optimized genetic system. International Journal of Electrical Power and Energy Systems, 33 (4). pp. 969-975. ISSN 0142-0615 http://dx.doi.org/10.1016/j.ijepes.2011.01.009 DOI:10.1016/j.ijepes.2011.01.009 |
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TK Electrical engineering. Electronics Nuclear engineering Abookazemi, Kaveh Ahmad, Hussein Tavakolpour, Alireza Hassan, Mohammad Yusri Unit commitment solution using an optimized genetic system |
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This paper presents an investigation into the application of an optimized Genetic Algorithm (GA) to solve the Thermal Unit Commitment (UC) problem. A Parallel structure was first developed to handle the infeasibility problem in a structured and improved GA which provides an effective search process and therefore greater economy. The proposed methodology resulted in a better performance with faster operation by using both computational methods and classification of unit characteristics. Typical constraints such as system power balance, minimum up and down times, start-up and shut-down ramps, have also been considered. A number of important parameters (standard and new parameters) of the UC problem have been identified. The proposed method is implemented and tested using a C# program. The tests are carried out using two systems including 10 and 20 units during a scheduling period of 24 h. The results are finally compared with those obtained from genetic schemes in other similar investigations through which the effectiveness of the proposed scheme is affirmed. |
format |
Article |
author |
Abookazemi, Kaveh Ahmad, Hussein Tavakolpour, Alireza Hassan, Mohammad Yusri |
author_facet |
Abookazemi, Kaveh Ahmad, Hussein Tavakolpour, Alireza Hassan, Mohammad Yusri |
author_sort |
Abookazemi, Kaveh |
title |
Unit commitment solution using an optimized genetic system |
title_short |
Unit commitment solution using an optimized genetic system |
title_full |
Unit commitment solution using an optimized genetic system |
title_fullStr |
Unit commitment solution using an optimized genetic system |
title_full_unstemmed |
Unit commitment solution using an optimized genetic system |
title_sort |
unit commitment solution using an optimized genetic system |
publisher |
Elsevier Ltd. |
publishDate |
2011 |
url |
http://eprints.utm.my/id/eprint/29906/ http://dx.doi.org/10.1016/j.ijepes.2011.01.009 |
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1683230685861511168 |
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13.211869 |