Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm
Exploring efficient and stable solution methods for hydropower generation optimization models is crucial for enhancing reservoir power generation efficiency and achieving the sustainable use of water resources. However, existing studies predominantly focus on single-timescale scheduling models, fail...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/7/1780 |
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| author | Mi Zhang Guosheng Zhou Bei Liu Dajun Huang Hao Yu Li Mo |
| author_facet | Mi Zhang Guosheng Zhou Bei Liu Dajun Huang Hao Yu Li Mo |
| author_sort | Mi Zhang |
| collection | DOAJ |
| description | Exploring efficient and stable solution methods for hydropower generation optimization models is crucial for enhancing reservoir power generation efficiency and achieving the sustainable use of water resources. However, existing studies predominantly focus on single-timescale scheduling models, failing to fully exploit multi-timescale runoff information. Additionally, commonly used solution algorithms often face challenges such as premature convergence, susceptibility to local optima, and dimensionality issues. To address these limitations, this paper proposes the Migrating Particle Whale Optimization Algorithm (MPWOA), which initializes the population using chaotic mapping, incorporates a particle swarm mechanism to enhance exploitation during the spiral predation phase, and integrates the black-winged kite migration mechanism to improve stochastic search performance. Validation on classical test functions and the Jiangpinghe River of the multi-timescale nested optimal scheduling model demonstrates that MPWOA exhibits faster convergence and stronger optimization capabilities and significantly improves power generation. The multi-timescale nested scheduling scheme derived from this algorithm effectively utilizes runoff information, offering a practical and highly efficient solution for hydropower scheduling. |
| format | Article |
| id | doaj-art-eb8922669489496bb56ec46f03efb665 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-eb8922669489496bb56ec46f03efb6652025-08-20T02:17:00ZengMDPI AGEnergies1996-10732025-04-01187178010.3390/en18071780Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization AlgorithmMi Zhang0Guosheng Zhou1Bei Liu2Dajun Huang3Hao Yu4Li Mo5Hubei Key Laboratory of Digital Valley Science and Technology, School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaHubei Energy Group Loushui Hydropower Co., Ltd., Enshi 445800, ChinaChangjiang Survey, Planning, Design and Research Co., Ltd., Wuhan 430010, ChinaHubei Energy Group Loushui Hydropower Co., Ltd., Enshi 445800, ChinaHubei Energy Group Loushui Hydropower Co., Ltd., Enshi 445800, ChinaHubei Key Laboratory of Digital Valley Science and Technology, School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaExploring efficient and stable solution methods for hydropower generation optimization models is crucial for enhancing reservoir power generation efficiency and achieving the sustainable use of water resources. However, existing studies predominantly focus on single-timescale scheduling models, failing to fully exploit multi-timescale runoff information. Additionally, commonly used solution algorithms often face challenges such as premature convergence, susceptibility to local optima, and dimensionality issues. To address these limitations, this paper proposes the Migrating Particle Whale Optimization Algorithm (MPWOA), which initializes the population using chaotic mapping, incorporates a particle swarm mechanism to enhance exploitation during the spiral predation phase, and integrates the black-winged kite migration mechanism to improve stochastic search performance. Validation on classical test functions and the Jiangpinghe River of the multi-timescale nested optimal scheduling model demonstrates that MPWOA exhibits faster convergence and stronger optimization capabilities and significantly improves power generation. The multi-timescale nested scheduling scheme derived from this algorithm effectively utilizes runoff information, offering a practical and highly efficient solution for hydropower scheduling.https://www.mdpi.com/1996-1073/18/7/1780optimal scheduling of hydropower plantsgeneration maximization criterionmultiple timescale nestingwhale optimization algorithm |
| spellingShingle | Mi Zhang Guosheng Zhou Bei Liu Dajun Huang Hao Yu Li Mo Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm Energies optimal scheduling of hydropower plants generation maximization criterion multiple timescale nesting whale optimization algorithm |
| title | Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm |
| title_full | Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm |
| title_fullStr | Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm |
| title_full_unstemmed | Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm |
| title_short | Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm |
| title_sort | multi timescale nested hydropower station optimization scheduling based on the migrating particle whale optimization algorithm |
| topic | optimal scheduling of hydropower plants generation maximization criterion multiple timescale nesting whale optimization algorithm |
| url | https://www.mdpi.com/1996-1073/18/7/1780 |
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