Method and system for multi-wind farm power modeling, pdf construction, and forecast scenario generation

Abstract

The invention discloses a multi-wind farm power modeling, PDF construction, and prediction scene generation method and system, belonging to the field of wind power scene prediction. The invention establishes the probability distribution model of the prediction error, and adds the probability distribution of the prediction error and the point prediction power of each wind farm power as the edge distribution model of each wind farm power. Calculate the cumulative probability of the power data of each wind farm as the input data of the time-varying R-Vine Copula model. By combining the ARIMA-GARCH-t model and the time-varying R-vine Copula model, a joint probability distribution model of high-dimensional wind power data is established. Based on the fitting model parameters of the historical power data of each wind farm, combined with the point forecast power data of each wind farm in the next day, a method for generating multi-wind farm power day-ahead forecast scenarios is proposed. The day-ahead prediction scenario generation model established by the present invention can better fit the time-space correlation characteristics of multi-wind farm power, and improve the accuracy and effectiveness of multi-wind farm power day-ahead prediction scenarios.

Description

technical field [0001] The invention belongs to the field of scenario prediction of wind power, and more particularly, relates to a method and system for multi-wind farm power modeling, PDF (joint probability density function, probability density function) construction, and prediction scenario generation. Background technique [0002] In order to achieve clean and low-carbon energy supply, wind power generation has received extensive attention in recent years. However, because it is still difficult to achieve accurate prediction of wind power, in the context of the rapid growth of wind power grid-connected capacity, the problem of uncertainty on the power supply side caused by wind power prediction errors has become increasingly prominent. From the perspective of power system operation, this will affect the reliability of the dispatch plan, which may not only cause serious wind curtailment problems, but also bring potential risks to the safe and stable operation of the power...

AI Technical Summary

Problems solved by technology

[0008] In view of the defects and improvement needs of existing models that cannot fully describe the time-space correlation characteristics of multi-wind farm power, the present invention provides multi-wind farm power modeling, PDF construction, forecasting scene generation methods and system methods, the purpose of which is to It is to improve the accuracy of multi-wind farm power day-ahead prediction scenarios

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