Research On Intelligent Control Strategy Of Large-scale Battery Energy Storage System

Today,when China’s policy is vigorously developing new energy power generation,the share of power generation in the power system was increasing.However,because of its intermittent randomness and other characteristics,combined with new energy power stations,can effectively reduce the amount of abandoned wind and light,and provide peak regulation,frequency modulation,load tracking and rotating reserve for the main network.The main research contents of this paper include:The main contents are as follows:(1)The structure of a large-scale photovoltaic energy storage power station is introduced,and the basic units of the large-scale energy storage system are sorted out and analyzed.A photovoltaic power generation model,a large-scale energy storage system model,and a largescale energy storage system economic model were established respectively.(2)Taking the maximum annual benefit of the energy storage system and the minimum waste light of the photovoltaic power station as the optimization goals,the life cycle cost and k Wh cost of different types of electrochemical energy storage power stations are analyzed and calculated in detail.Choose a lithium iron phosphate battery energy storage station for power/capacity optimization.Then the economic benefit is analyzed through the relationship between the power purchase cost and the annual benefit of each power station,and combined with the data of the energy storage power station in Qinghai as an example,it is calculated that if the annual return rate of the energy storage power station is more than 8%,and the investment is recovered in the life cycle,the power purchase price of the energy storage power station should not be more than 0.4 yuan / k Wh.(3)The active power control strategy of cutting peak and filling valley is optimized,and the variable parameter power difference control strategy is adopted.Compared with the common constant power control strategy and constant parameter power difference control strategy,on the basis of dividing the load,taking into account the charge state,the SOC state interval is divided,the load is divided into 3 intervals,the SOC is divided into 5 intervals,and the charge and discharge strategies are established for 15 state sub-intervals.Two charge state control parameters and four charge and discharge control parameters are introduced,and the values of these six parameters are determined by ultra-short-term prediction,and then the six parameters are scrolled and optimized by the improved adaptive particle swarm optimization algorithm,which achieves the effect of SOC state energy storage participating in peak-cutting and valleycutting and smoothing photovoltaic output.Based on an example of an optical storage system in Qinghai,the effectiveness of the strategy is verified.