An improved binary African vultures optimization approach to solve the UC problem for power systems
Unit commitment (UC) is one of the most crucial problems in electrical power systems. It concerns finding the optimal schedule for generating units such that the production cost is minimized over a given period. This paper aims to solve the unit commitment problem by proposing a binary version of a...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023004814 |
| _version_ | 1797682477964197888 |
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| author | Ahmad Abuelrub Boshra Awwad |
| author_facet | Ahmad Abuelrub Boshra Awwad |
| author_sort | Ahmad Abuelrub |
| collection | DOAJ |
| description | Unit commitment (UC) is one of the most crucial problems in electrical power systems. It concerns finding the optimal schedule for generating units such that the production cost is minimized over a given period. This paper aims to solve the unit commitment problem by proposing a binary version of a recent metaheuristic algorithm, the African Vultures Optimization Algorithm (AVOA), which is a nature-inspired metaheuristic algorithm that mimics the hunting mechanism and behavior of African vultures. This algorithm has the advantage of creating various phase shift approaches to avoid premature convergence and local optimum trapping. AVOA uses two mechanisms for the exploration phase and four for the exploitation phase that ensure the algorithm's ability to diversify and intensify. The AVOA is a continuous algorithm that cannot tackle the mixed-integer nature of the UC. In this paper, a sigmoid transfer function is used to convert the algorithm into a binary algorithm to decide the on/off status of the generating units. To further enhance the performance of the proposed algorithm, new values of the controlling parameters are proposed. Finally, the performance of the proposed algorithm is tested on IEEE 30-, 14-, and 57-bus systems. The proposed Binary AVOA (BAVOA) has superiority over the other algorithms given in the case study. In addition, results revealed that the BAVOA gives better results in the discrete search space (DSS) compared to the continuous search space (CSS). For instance, the operation cost of an IEEE 30-bus system in the DSS is 12,768 dollars which is 7% less than the CSS. |
| first_indexed | 2024-03-12T00:00:18Z |
| format | Article |
| id | doaj.art-a26990a8775448ffbd3ef7ed630961ee |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-12T00:00:18Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-a26990a8775448ffbd3ef7ed630961ee2023-09-18T04:30:51ZengElsevierResults in Engineering2590-12302023-09-0119101354An improved binary African vultures optimization approach to solve the UC problem for power systemsAhmad Abuelrub0Boshra Awwad1Corresponding author.; Department of Electrical Engineering, Jordan University of Science and Technology, JordanDepartment of Electrical Engineering, Jordan University of Science and Technology, JordanUnit commitment (UC) is one of the most crucial problems in electrical power systems. It concerns finding the optimal schedule for generating units such that the production cost is minimized over a given period. This paper aims to solve the unit commitment problem by proposing a binary version of a recent metaheuristic algorithm, the African Vultures Optimization Algorithm (AVOA), which is a nature-inspired metaheuristic algorithm that mimics the hunting mechanism and behavior of African vultures. This algorithm has the advantage of creating various phase shift approaches to avoid premature convergence and local optimum trapping. AVOA uses two mechanisms for the exploration phase and four for the exploitation phase that ensure the algorithm's ability to diversify and intensify. The AVOA is a continuous algorithm that cannot tackle the mixed-integer nature of the UC. In this paper, a sigmoid transfer function is used to convert the algorithm into a binary algorithm to decide the on/off status of the generating units. To further enhance the performance of the proposed algorithm, new values of the controlling parameters are proposed. Finally, the performance of the proposed algorithm is tested on IEEE 30-, 14-, and 57-bus systems. The proposed Binary AVOA (BAVOA) has superiority over the other algorithms given in the case study. In addition, results revealed that the BAVOA gives better results in the discrete search space (DSS) compared to the continuous search space (CSS). For instance, the operation cost of an IEEE 30-bus system in the DSS is 12,768 dollars which is 7% less than the CSS.http://www.sciencedirect.com/science/article/pii/S2590123023004814African vulture optimizationMixed-integer optimizationUnit commitment |
| spellingShingle | Ahmad Abuelrub Boshra Awwad An improved binary African vultures optimization approach to solve the UC problem for power systems Results in Engineering African vulture optimization Mixed-integer optimization Unit commitment |
| title | An improved binary African vultures optimization approach to solve the UC problem for power systems |
| title_full | An improved binary African vultures optimization approach to solve the UC problem for power systems |
| title_fullStr | An improved binary African vultures optimization approach to solve the UC problem for power systems |
| title_full_unstemmed | An improved binary African vultures optimization approach to solve the UC problem for power systems |
| title_short | An improved binary African vultures optimization approach to solve the UC problem for power systems |
| title_sort | improved binary african vultures optimization approach to solve the uc problem for power systems |
| topic | African vulture optimization Mixed-integer optimization Unit commitment |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023004814 |
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