지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.13 No.3
- 발행연도
- 2024.6
- 수록면
- 273 - 284 (12page)
- DOI
- 10.5573/IEIESPC.2024.13.3.273
이용수
초록· 키워드
오류제보하기
Power dispatch is an effective measure to achieve the rational utilization of power resources and reduce economic costs. On the other hand, existing power resource scheduling technologies have problems, such as low efficiency and inability to meet actual power demand. Therefore, search factors and selection strategies are introduced based on the preprocessing of power load data to improve the artificial bee colony (ABC) algorithm, solve this problem, and meet the power load demand. A power resource-scheduling model based on the ABC algorithm is constructed to achieve reasonable utilization of resources. According to the findings, the optimized method showed the best convergence performance among the six benchmark function tests. In the f2 function, it converged from 108. The minimum loss value was 104 when the function iteration value was 103. This suggests that the power resource scheduling technology based on improved methods has good application effects and can effectively achieve the reasonable allocation and utilization of power resources.
목차
Abstract
1. Introduction
2. Related Works
3. Construction of Power Dispatching Method based on Improved ABC
4. Performance Analysis of Power Dispatching Model based on Improved ABC
5. Conclusion
References
참고문헌 (20)
참고문헌 신청
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N. Zhou, C. Zhang, and S-L Zhang, “A multi-strategy firefly algorithm based on rough data reasoning for power economic dispatch,” Mathematical biosciences and engineering: MBE, Vol. 19, no. 9, pp. 8866-8891, 2022.
F. Gami, Z-A. Alrowaili, M. Ezzeldien, M. Ebeed, S. Kamel, S. Oda Eyad, and A. Mohamed Shazly, “Stochastic optimal reactive power dispatch at varying time of load demand and renewable energy resources using an efficient modified jellyfish optimizer,” Neural Computing and Applications, Vol. 34, no. 22, pp. 20395-20410, 2022.
Z-Y. Qu, Y-C. Dong, S. Mugemanyi, T. Yu, X-Y. Bo, H-H. Li, Y. Li, F-X. Rugema, and C. Bananeza, “Dynamic exploitation Gaussian bare‐bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power system,” IET Renewable Power Generation, Vol. 16, no. 7, pp. 1401-1424, 2022.
M. Wang, T. Zhou, H-H. Wang, Y-H. Zhai, and X-B. Dong, “Chinese power dispatching text entity recognition based on a double-layer BiLSTM and multi-feature fusion,” Energy Reports, Vol. 8, no. 5, pp. 980-987, 2022.
N. Zhou, C. Zhang, and S-L Zhang, “A multi-strategy firefly algorithm based on rough data reasoning for power economic dispatch,” Mathematical biosciences and engineering: MBE, Vol. 19, no. 9, pp. 8866-8891, 2022.
F. Gami, Z-A. Alrowaili, M. Ezzeldien, M. Ebeed, S. Kamel, S. Oda Eyad, and A. Mohamed Shazly, “Stochastic optimal reactive power dispatch at varying time of load demand and renewable energy resources using an efficient modified jellyfish optimizer,” Neural Computing and Applications, Vol. 34, no. 22, pp. 20395-20410, 2022.
Z-Y. Qu, Y-C. Dong, S. Mugemanyi, T. Yu, X-Y. Bo, H-H. Li, Y. Li, F-X. Rugema, and C. Bananeza, “Dynamic exploitation Gaussian bare‐bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power system,” IET Renewable Power Generation, Vol. 16, no. 7, pp. 1401-1424, 2022.
M. Wang, T. Zhou, H-H. Wang, Y-H. Zhai, and X-B. Dong, “Chinese power dispatching text entity recognition based on a double-layer BiLSTM and multi-feature fusion,” Energy Reports, Vol. 8, no. 5, pp. 980-987, 2022.
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