All-vanadium redox flow battery energy storage system operation optimization method based on genetic algorithm

Abstract

The invention discloses an all-vanadium flow battery energy storage system operation optimization method based on a genetic algorithm. The all-vanadium flow battery energy storage system operation optimization method comprises the steps: 1, establishing a mathematical model of an all-vanadium flow battery according to an equivalent circuit model of the all-vanadium flow battery; 2, performing parameter identification; 3, constructing an instantaneous energy efficiency equation; 4, constructing a cost function of the highest instantaneous energy efficiency to minimize energy loss, and determining constraint conditions; 5, constructing a fitness function, and selecting a probability function and a variation probability function; and 6, calculating by adopting a genetic algorithm, and solvingthe input power value PCh / Dis and the residual electric quantity SOC to obtain the highest instantaneous energy efficiency when the absolute value of the instantaneous energy efficiency error is smaller than a specified numerical value, and corresponding flow velocity, temperature and voltage. According to the all-vanadium flow battery energy storage system operation optimization method, the energy storage system of the all-vanadium redox flow battery is optimized by adopting a genetic algorithm according to three factors of temperature, flow velocity and current, so that the instantaneous energy efficiency of a galvanic pile is improved, and the system efficiency of the all-vanadium redox flow battery is further improved.

Description

technical field [0001] The invention relates to the technical field of power system energy storage, in particular to an operation optimization method for an all-vanadium redox flow battery energy storage system based on a genetic algorithm. Background technique [0002] The genetic algorithm was proposed in the 1960s. This algorithm is a global probability search algorithm developed by referring to the natural selection and genetic evolution mechanism of organisms. It imitates the principle of "natural selection and survival of the fittest" in the process of biological genetic evolution in nature, borrows the viewpoint of biological genetics, and uses computers to find the best individuals in the population. The genetic algorithm does not have high requirements for solving the problem itself, all it needs is to evaluate each individual produced by the algorithm, and make the individual with good "fitness" (used to measure the adaptability of a species to the living environme...

AI Technical Summary

Problems solved by technology

In fact, the various factors are interrelated, and optimizing one of them may lead to increased losses in the other, reducing energy efficiency in the other

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