Self-adaptive dynamic planning control method and system for large-scale energy storage power station

Abstract

The invention provides a self-adaptive dynamic planning control method and system for a large-scale energy storage power station. The method includes steps of setting a structure and control target parameters of a self-adaptive dynamic planning control system; initiating the parameters and importing an initial state of a controlled object; calculating the original wind electricity power fluctuation rate of a current moment t and performing smoothing treatment on the original wind electricity power by using a change rate control method; calculating the wind storage power fluctuation rate subjected to the smoothing treatment, the energy storage power and the SOC (State Of Charge) of the energy storage system; initiating a training evaluation module and an evaluation module; calculating and saving the control strategy, the wind storage power fluctuation rate, the energy storage power and the SOC of the energy storage system of each moment; outputting the control strategy, the wind storage power fluctuation rate subjected to the smoothing treatment, the energy storage power and the SOC of the energy storage system of each moment. The system includes a parameter initializing module, a data acquisition and calculation module, an execution module, an evaluation module and an output module and the like. By adopting the method and system provided by the invention, optimization control of the large-scale energy storage system is realized and the technical economical efficiency of the energy storage system is improved.

Description

technical field [0001] The invention belongs to the technical fields of smart grid, energy internet and energy storage, and in particular relates to a control method and system for adaptive dynamic programming of a large-scale energy storage power station. Background technique [0002] With the continuous development of wind power and photovoltaic power generation technology, the large-scale grid connection of wind power and photovoltaic power generation and other new energy power generation, the fluctuation of its output is becoming more and more serious. After wind power, photovoltaic power generation and other new energy power generation are connected to the power grid on a large scale, their volatility and intermittency will adversely affect the safety, stability and power quality of power grid operation. Therefore, in current practical applications, the grid connection of wind power and photovoltaic power generation is greatly restricted, which is not conducive to the d...

AI Technical Summary

Problems solved by technology

[0006] When smoothing large-scale new energy generation output fluctuations in large-scale battery energy storage power stations, the traditional first-order low-pass filtering or output filtering method with variable time constant (T) is used, which sometimes leads to control sensitivity due to the time lag of the method itself. bad

The energy storage output target value output by the general moving average filtering algorithm is also largely limited by the input of new energy generation power such as wind power and photovoltaic power generation. These traditional methods encounter sudden changes in the output of new energy power generation. The performance is degraded and affects the subsequent filtering effect

On the other hand, when improving the output control of energy storage power stations that are friendly to new energy power generation, the traditional control method needs to be further improved in the aspect of adaptive control of the overall output of energy storage power stations based on self-learning intelligent optimization control

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