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With economic and industrial restructuring, the number and size of commercial parks are increasing. The most basic function of energy storage system in commercial parks is to carry out the load side, Which also brings economic benefits and ensures the reliability of power grid. Therefore, it is of practical significance to study the control strategy of energy storage system in business parks. In this paper, a variable parameter power difference control strategy for energy storage system is proposed. Based on the typical daily load of the park, it divides the load interval and the SOC of the energy storage system. Different subspaces are provided with different strategies. Finally, a simulation analysis, taking a Shanghai business park as an actual data sample shows that the proposed method for the energy storage system is effective.

The study of the uncertainty of wind power is very important for power system planning and operation decision. This paper presents a method of wind power probability interval prediction based on Bootstrap quantile regression method. Due to the large difference in wind power data, the method first divides the power at equal intervals. And then use the Bootstrap method to extract the data in different power intervals to form the pseudo samples. Then use the quantile method to obtain the upper and lower limits of the confidence interval of each power interval. Finally, the upper and lower limits of the confidence intervals of multiple power intervals are combined by using the quantile regression method to obtain the probabilistic prediction interval of wind power. Let the interval coverage and average bandwidth as evaluation index. Let the interval coverage rate and average bandwidth as evaluation index. Through the Bootstrap quantile regression method, the interval coverage rate of wind power probability interval prediction is higher, the average bandwidth is narrower, the precision is higher and the effect is better.

In order to reduce the fluctuation rate of wind power output and improve the reliability when connected to grid, this paper puts forwards a control method of smoothing wind power output. This method is used to smooth wind power fluctuation with battery energy storage system(BESS) based on Empirical Mode Decomposition (EMD). After wind power is decomposed by EMD, the low-frequency parts are used as wind power grid-connected value and the high-frequency parts are stored by BESS; The orders of EMD filter can be adaptively adjusted according to the state of charge (SOC). Compared with another method of constant order with EMD filter, simulation results show that the method proposed in this paper can avoid the over-charging and over-discharging of battery energy storage system so as to keep SOC stable.

Photovoltaic (PV) power forecasting is one of the effective ways to decrease the influence caused by large-scale PV power station connecting to the grid. Currently, there are various forecasting methods. However, single forecasting method tends to perform unsatisfactorily. In this paper, a combination method in PV power forecasting based on the correlation coefficient was proposed. Firstly, the persistence method, support vector machine (SVM) method and prediction method based on similar data were used to forecast the PV power separately. Then the weight of single prediction method was determined by the correlation coefficient of prediction and actual values. Single prediction method with larger correlation coefficient has greater weight. Finally, a combination model based on the correlation coefficient was established. By means of simulation and analysis, the combination prediction method based on the correlation coefficient was proved to be effective and the prediction accuracy was improved.

The wind speed prediction method based on improved neural network was proposed in this paper by analyzing the relationships between wind direction and wind speed changes. A new network model was created by customizing the network object properties. Wind speed and wind direction were regarded as the network input at the same time using a single network layer to deal with trends in wind direction so as to realize wind speed prediction. Three different kinds of improved network models were provided in this paper, the validity of which was verified by the actual data in wind farm. Simulation results show that: It can significantly improve the network prediction accuracy by continuously improving the structure of neural networks.