A solving method of a cascade reservoir group scheduling model based on a multi-objective optimization algorithm

Abstract

The invention belongs to the technical field of solving of multi-objective optimization scheduling models of cascade reservoir groups, and discloses a solving method of a cascade reservoir group scheduling model based on a multi-objective optimization algorithm, which comprises the following steps of of firstly, collecting power station basic data and hydrological data of a cascade reservoir group; secondly, establishing a scheduling target according to specific requirements, and adding the scheduling constraint conditions; taking the water level at each moment in the scheduling period as a decision variable, and solving the established cascade reservoir group scheduling model by adopting an R dominated improved moth fire-fighting multi-objective optimization algorithm (R-NSIMFO); and finally, obtaining a corresponding scheduling scheme set according to a solving result. The solving method is improved from two aspects of an evolutionary algorithm and a multi-objective mechanism, can obtain a non-inferior scheduling scheme set with excellent convergence and distribution, and plays a crucial role in realizing the maximization of the comprehensive benefits of the whole cascade reservoir group.

Description

technical field [0001] The invention belongs to the technical field of solving multi-objective optimization dispatching models of cascade reservoir groups, and in particular relates to a method for solving the dispatching model of cascade reservoir groups based on a multi-objective optimization algorithm. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] The research on multi-objective joint dispatching of cascade reservoirs is of great significance to the optimal allocation of water resources and the efficient utilization of hydropower energy. The multi-objective optimal dispatching model of cascade reservoirs is an optimization problem with multiple objectives, constraints and decision variables, and its solution is very complicated. The early methods used to solve the reservoir scheduling model are mainly dynamic programming, linear programming, nonlinear programming and stochastic dynamic programmi...

AI Technical Summary

Problems solved by technology

The moth-flame optimization algorithm (MFO), which was proposed in 2015, has gradually attracted widespread attention because of its fast convergence speed, but some literature shows that when applying MFO to practical problems, it is easy to fall into local optimum and premature

[0010] In the prior art, when the number of targets increases, the non-dominated sorting method will face huge selection pressure, which leads to poor distribution of solution sets obtained when solving many target problems (more than 3 targets).

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