Wind storage capacity configuration method based on genetic algorithm

Abstract

The invention relates to a wind storage capacity configuration method based on a genetic algorithm, and belongs to the technical field of automated analysis of an electricity system. The method includes acquiring network parameters and system parameters of the system, and defining algorithm parameters; coding decision variables; randomly generating a population including M initial individuals, and performing feasibility detection on the randomly generated population including the M initial individuals, wherein each individual comprises codes of the decision variables; substituting all individuals in the feasible population and tide solutions into a fitness function, and calculating fitness of each individual; performing heritable variation calculation on the current feasible population to form a next generation of population; judging whether or not a current genetic algebra is identical to a maximum genetic algebra N, and if yes, finishing calculation and taking the value of the decision variable contained in the individual, with the highest fitness, in the last generation of population as a final calculation result. The wind storage capacity configuration method has the advantages that the problem of difficulty in large-scale concentrated grid connection of wind power is solved, a calculation method is simple and application of the actual system is facilitated.

Description

technical field [0001] The invention belongs to the technical field of wind power generation, and in particular relates to the iterative optimization of the generation-by-generation genetic variation of the population by using a genetic algorithm to obtain the optimal configuration location and configuration capacity of the energy storage device in the system while taking into account the economical efficiency of the wind power generation system and the maximum input and output capacity to solve the difficulty of large-scale wind power centralized grid connection. Background technique [0002] Since the beginning of the 21st century, global environmental pollution and energy crisis have promoted the vigorous development of renewable energy, among which wind power has developed most rapidly. The centralized grid connection of large-scale wind power is convenient for scheduling and management, but it also brings a series of challenges, including power peak regulation and wind ...

AI Technical Summary

Problems solved by technology

[0004] Although the above methods can obtain the capacity of the energy storage device, they do not consider the impact of the layout of the energy storage device on the wind power generation system.

After the energy storage device is introduced into the wind power generation system, the distribution of the system's active power flow and reactive power flow will vary with the layout of the energy storage device, which may cause some economic and safety problems

Moreover, in the problem of comprehensively considering the layout and capacity of energy storage devices, the decision variables include both integer variables and continuous variables, which belong to the mixed integer programming problem, which increases the difficulty of the problem, and the traditional optimization algorithm is difficult to solve

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