Variable-stage gradual optimization based joint optimization scheduling method for cascaded hydropower stations

Abstract

The present invention discloses a variable-stage gradual optimization based joint optimization scheduling method for cascaded hydropower stations. The method comprises: step 1, acquiring an initial scheduling process of each hydropower station, and performing water level regulation in a constraint search manner; step 2, fixing water levels of an ith node and an (i+VP)th node, and performing optimization calculation for problems in VP stages by adopting a differential evolution algorithm; step 3, selecting a population size NP, wherein initial individuals are adjustable points of the cascaded hydropower stations; step 4, obtaining a water level process line of each cascaded hydropower station; step 5, by using the water level process line of each hydropower station obtained in the step 4 as an initial trajectory, repeating the steps 2 to 5 until a variable-stage convergent condition is satisfied; step 6, setting VP to be equal to the sum of VP and 1; repeating the steps 2 to 5 until a final convergent condition is satisfied; and step 7, ending the calculation, and obtaining an optimal water level process line of each hydropower station. The method disclosed by the present invention has very good global search capability for solving the problem in joint optimization scheduling of the cascaded hydropower stations, and effectively solves the local convergence problem of a conventional algorithm.

Description

technical field [0001] The invention belongs to the field of optimized operation of hydropower energy, and more specifically relates to a cascade power station joint optimal dispatching method based on step-by-step optimization of variable stages. Background technique [0002] The joint optimal dispatching of cascade hydropower stations is a large-scale, multi-constraint, dynamic and nonlinear optimization problem. With the gradual expansion of the system scale, it becomes more and more difficult to solve. Although the traditional dynamic programming algorithm has a "curse of dimensionality" in solving cascade optimization problems, the solution takes a long time. Although the intelligent optimization algorithms currently used to solve the joint optimization scheduling problem of cascade hydropower stations include ant colony algorithm, genetic algorithm, discrete differential dynamic programming method, and differential evolution algorithm, these intelligent optimization al...

AI Technical Summary

Problems solved by technology

Enhance the search ability of the algorithm and solve the local convergence problem caused by factors such as the overlapping of water heads between steps and the optimization order of the step-by-step optimization algorithm

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