Agency optimization dimension reduction method for combined scheduling of reservoir group of large-scale hydropower station

Abstract

The invention discloses an agency optimization dimension reduction method for combined scheduling of reservoir groups of a large-scale hydropower station and belongs to the technical field of optimization scheduling of hydropower systems. The method comprises the following steps: selecting a hydropower station, setting related constraints and parameters, and calculating an initial scheduling process O and a search step length h; according h and O, generating a sample set S1 within a neighborhood domain range, calculating real target functions of samples in S1, inputting sample points in S1 andthe target functions into a neural network, and carrying out fitting so as to obtain a corresponding agency optimization model N; generating a sample set S2 of a certain scale, inputting sample points in S2 into N so as to obtain possible target functions, screening an optimal sample set S3 according to the target functions, calculating real target functions of sample points in S3, finding an improvement solution with an optimal target function in S3, if the improvement solution is prior to an initial solution, updating the initial solution, and carrying out iterative calculation, or else judging whether a step length meets precise requirements or not, if the step length meets the precise requirements, outputting the optimal solution, or else updating and replenishing the initial solution, and carrying out iteration. The method disclosed by the invention is small in parameter calculation quantity, high in search precision, short in optimization time and large in dissolution scale.

Description

technical field [0001] The invention belongs to the technical field of optimal dispatching of hydropower systems, and more specifically relates to an agent optimization dimension reduction method for joint dispatching of large-scale hydropower station reservoir groups. Background technique [0002] After more than 20 years of rapid development, especially the concentrated production of hydropower bases such as the Jinsha River, Hongshui River, and Lancang River in the past ten years, my country has formed a large-scale hydropower station and reservoir group joint dispatching pattern, and has entered a large-scale The age of hydropower. With the expansion of the scale of the system, the value of joint dispatching of large-scale hydropower station reservoirs has become increasingly prominent, which can not only increase the cascade water energy utilization efficiency of major river basins and the overall economic benefits of the hydropower system, but also promote the steady tr...

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Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides an agent optimization dimensionality reduction method for joint dispatching of large-scale hydropower station and reservoir groups, the purpose of which is to select a set number of sample points to construct an agent optimization model, and in this Perform secondary optimization on the above, and iteratively find the optimal process, thereby solving the technical problem of large computational overhead in existing methods

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