A real-time control method for grid-connected intelligent energy storage system based on artificial fish swarm algorithm

Abstract

An intelligent energy storage system grid-connected real-time control method based on an artificial fish swarm algorithm adopts an Elman neural network to predict intraday power load real time data on the base of power load historical data, then utilizes the artificial fish swarm algorithm to plan the optimal charge-discharge time and the optimal power of intraday power load prediction data, and performs comparison with the electric power real time data through an intelligent electric meter, so as to determine the optimal charge-discharge time node. The invention achieves automatic grid-connected discharge in the peak of power utilization and achieves charge in the low ebb of power utilization, achieves peak load shifting on the user side, and improves the utilization efficiency of electric power resources.

Description

technical field [0001] The invention belongs to the field of grid-connected control of energy storage systems, and in particular relates to an intelligent energy storage system grid-connected real-time control method based on an artificial fish swarm algorithm. Background technique [0002] With the rapid development of my country's economy, the national grid is getting bigger and bigger, and the problems brought about by large-scale power grids are gradually revealed, such as scheduling difficulties, low safety and reliability factors, etc. At the same time, the imbalance of the overall layout of the power grid, the irrationality of the local structure, and the exponentially increasing power demand have gradually become prominent, and have become important problems that need to be solved urgently. At present, with the popularization of electric vehicles and UPS, more and more families have energy storage devices such as large-capacity lithium batteries. These distributed en...

AI Technical Summary

Problems solved by technology

The advantage of the evolutionary calculation method is that the model can contain discontinuous and nonlinear constraints; but for problems with multiple local optimal solutions, the evolutionary calculation method algorithm cannot guarantee convergence to the global optimal solution

In addition, most of the current strategies adopted for energy storage system peak shaving and valley filling are to make overall planning of charging and discharging power in different periods through optimization algorithms, rather than real-time adjustment of charging and discharging based on real-time data of electric loads, which cannot adaptively find The time node of daily charge and discharge greatly reduces the effect of peak shaving and valley filling and the real-time utilization rate of the energy storage system

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