An optimal configuration method of independent wind-solar-storage micro-grid based on improved fish swarm algorithm

Abstract

The invention discloses an optimal configuration method of an independent wind-solar-storage micro-grid based on an improved fish swarm algorithm, which uses particle flight speed v and inertia weight w to improve the foraging behavior, clustering behavior, and tail-tracking behavior of an artificial fish swarm algorithm. The particle swarm algorithm is used to update the elite artificial fish swarm with the highest fitness value. In the artificial fish swarm algorithm, the artificial fish with the highest fitness value is found, and the particle swarm algorithm is used to update until the required accuracy or iteration number is met. At this time, the location obtained by iteration is obviously better than the initial point, but multiple iterations are required to obtain the current best solution. Considering the overall efficiency, the termination condition of the improved particle swarm artificial fish swarm algorithm is formed by using the accuracy and the number of iterations together. The invention applies the above algorithm to configure the independent wind-solar-storage micro-grid. The optimal configuration of the independent micro-grid is based on the normal operation of the micro-source and the satisfaction of various constraints, so as to achieve the lowest total operating cost of the micro-grid and the lowest abandonment rate of new energy generation. Require.

Description

technical field [0001] The invention relates to the field of application of the improved fish swarm algorithm, in particular to an optimal configuration method of an independent wind-solar-storage micro-grid based on the improved fish-swarm algorithm. Background technique [0002] Inspired by the social attributes and social behavior of fish swarms, Li Xiaolei proposed a kind of swarm intelligence optimization algorithm based on animal behavior-artificial fish swarm algorithm. Each artificial fish is an autonomous body of artificial life. It perceives the external environment through its own sensory behavior. Foraging, flocking, tail-stalking and random behavior are the basic social attributes and basic behaviors of fish in the evolution process of nature. [0003] At present, large-scale distributed wind power and photovoltaic power grid connection and consumption are difficult, and power curtailment is serious. Configuring energy storage with a reasonable capacity in the ...

AI Technical Summary

Problems solved by technology

At this stage, due to the high price of energy storage devices, the configuration of energy storage capacity should not be too large, which will significantly increase the investment cost; the configuration of energy storage capacity should not be too small, as too small energy storage capacity cannot guarantee the system Stable operation during the off-grid period is also not conducive to promoting the consumption of photovoltaics

[0004] In addition, the basic artificial fish swarm algorithm has a slow convergence speed in the later stage of optimization for high-dimensional extreme value functions, and the accuracy needs to be improved

When the basic artificial fish swarm algorithm is applied to the optimal configuration of the independent wind-solar-storage micro-grid, there is a problem of unreasonable configuration, and the utilization rate of distributed energy generation and the total configuration cost cannot meet the established requirements

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