Parallel multi-objective optimized scheduling method for cascaded hydropower station group

Abstract

The invention relates to a parallel multi-objective optimized scheduling method for a cascaded hydropower station group. A multi-population evolution strategy is used to ensure the relative independence of small-scale subpopulations, elite individuals of a Pareto solution set are coupled to an inter-population annular migration mechanism in the evolution process, information is transmitted and back fed mutually among the subpopulations, and the individual diversity and guidance quality of the solution set are ensured; and a multi-core parallel calculation technology is used to realize synchronized evolution of the subpopulations, waste of calculation resources under in the serial calculation mode is avoided, and calculation is accelerated. According to the invention, the calculable scale of optimized scheduling of the cascaded hydropower station group is further enlarged, a reasonable and feasible scheduling scheme set is provided for a decision maker, the calculation efficiency is ensured, and the method of the invention is a feasible method to realize multi-objective optimized scheduling for the cascaded hydropower station group.

Description

technical field [0001] The invention relates to the field of hydropower system power generation scheduling, in particular to a parallel multi-objective optimal scheduling method for cascade hydropower station groups. technical background [0002] As the renewable clean energy with the highest proportion in the current power system, hydropower energy needs to balance the two kinetic energy indicators of power generation and guaranteed output in the optimization dispatching process. The maximum power generation model can maximize the use of hydropower resources and maximize the benefits of power generation companies; the maximum minimum output model can increase the guaranteed output of the hydropower system and enhance the compensation and regulation of hydropower abundance and decline. Using an optimized scheduling scheme that takes into account power generation and guaranteed output to guide the power production of hydropower station groups can effectively weaken the negati...

AI Technical Summary

Problems solved by technology

The standard multi-objective genetic algorithm is typically represented by Non-dominated Sorting Genetic Algorithm II (NSGA-II), which has been widely used in scientific research and engineering practice due to its multi-objective solution advantages, but there are still two shortcomings in the following aspects : (1) After a certain number of generations of evolution, the phenomenon of individual convergence gradually becomes prominent, and the diversity of the population is continuously lost, so it is very easy to obtain the pseudo-Pareto optimal solution of the original problem; When it is large, it faces disadvantages such as long calculation time and low calculation efficiency.

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