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Open AccessArticle

Long-Term Scheduling of Large-Scale Cascade Hydropower Stations Using Improved Differential Evolution Algorithm

1
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
China Three Gorges Corporation, Beijing 100038, China
3
Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China
4
China Institute of Water Resources and Hydropower Research, Beijing 100038, China
*
Author to whom correspondence should be addressed.
Water 2018, 10(4), 383; https://doi.org/10.3390/w10040383
Received: 1 March 2018 / Revised: 18 March 2018 / Accepted: 20 March 2018 / Published: 26 March 2018
(This article belongs to the Special Issue Adaptive Catchment Management and Reservoir Operation)
Long-term scheduling of large cascade hydropower stations (LSLCHS) is a complex problem of high dimension, nonlinearity, coupling and complex constraint. In view of the above problem, we present an improved differential evolution (iLSHADE) algorithm based on LSHADE, a state-of-the-art evolutionary algorithm. iLSHADE uses new mutation strategies “current to pbest/2-rand” to obtain wider search range and accelerate convergence with the preventing individual repeated failure evolution (PIRFE) strategy. The handling of complicated constraints strategy of ε-constrained method is presented to handle outflow, water level and output constraints in the cascade reservoir operation. Numerical experiments of 10 benchmark functions have been done, showing that iLSHADE has stable convergence and high efficiency. Furthermore, we demonstrate the performance of the iLSHADE algorithm by comparing it with other improved differential evolution algorithms for LSLCHS in four large hydropower stations of the Jinsha River. With the applications of iLSHADE in reservoir operation, LSLCHS can obtain more power generation benefit than other alternatives in dry, normal, and wet years. The results of numerical experiments and case studies show that the iLSHADE has a distinct optimization effect and good stability, and it is a valid and reliable tool to solve LSLCHS problem. View Full-Text
Keywords: energy; hydropower stations; differential evolution algorithm; optimal scheduling; ε-constrained method energy; hydropower stations; differential evolution algorithm; optimal scheduling; ε-constrained method
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Wen, X.; Zhou, J.; He, Z.; Wang, C. Long-Term Scheduling of Large-Scale Cascade Hydropower Stations Using Improved Differential Evolution Algorithm. Water 2018, 10, 383.

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