Research on water resources dispatch model based on improved genetic algorithm – water resources dispatch model
According to the research on reservoir water resources scheduling and distribution, the aim is to balance the water supply and demand in each period, and consider the total water supply and the annual external water withdrawal of the reservoir in each period as water rights. The decision-making vari...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IWA Publishing
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/fd19dd300eb9441790540bdf671c7d49 |
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| Sumario: | According to the research on reservoir water resources scheduling and distribution, the aim is to balance the water supply and demand in each period, and consider the total water supply and the annual external water withdrawal of the reservoir in each period as water rights. The decision-making variables are provided for the water supply of the reservoir in this paper, so that water demand of the water-receiving area can be better met to alleviate the water shortage at various stages and realize the effective use of water resources. Moreover, through the constraints of reservoir operation rules and other constraints, a mathematical model is established for optimal operation of water resources in the reservoir system. Meanwhile, optimized genetic algorithms are applied to solve the model according to the characteristics of the model. After simulation tests, compared with the traditional linear binary algorithm used in the reservoir, the improved genetic algorithm studied in the paper improves the accuracy of data calculation and data convergence, which proves that the research results of the paper provide theoretical and practical significance for improving the level of reservoir water resources management and solving the problem of optimal water resources scheduling. HIGHLIGHTS
This paper realizes the supply decision variables.;
The data index takes the historical data of the past as a reference to compare the algorithm.;
The solution model of this algorithm makes use of the genetic effect of the improved genetic algorithm.;
The constraint conditions are used as the rules of data allocation.;
A multi-group genetic algorithm based on real coding is proposed.; |
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