Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm

Mi Zhang, Guosheng Zhou, Bei Liu, Dajun Huang, Hao Yu and Li Mo ()
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Mi Zhang: Hubei Key Laboratory of Digital Valley Science and Technology, School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Guosheng Zhou: Hubei Energy Group Loushui Hydropower Co., Ltd., Enshi 445800, China
Bei Liu: Changjiang Survey, Planning, Design and Research Co., Ltd., Wuhan 430010, China
Dajun Huang: Hubei Energy Group Loushui Hydropower Co., Ltd., Enshi 445800, China
Hao Yu: Hubei Energy Group Loushui Hydropower Co., Ltd., Enshi 445800, China
Li Mo: Hubei Key Laboratory of Digital Valley Science and Technology, School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2025, vol. 18, issue 7, 1-19

Abstract: 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.

Keywords: optimal scheduling of hydropower plants; generation maximization criterion; multiple timescale nesting; whale optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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