Multi-wind-power-plant power modeling method and system, PDF construction method and system, and prediction scene generation method and system

Abstract

The invention discloses a multi-wind-power-plant power modeling method and system, a PDF construction method and system and a prediction scene generation method and system, and belongs to the field of scene prediction of wind power. According to the method, a probability distribution model of prediction errors is established, and the sum of the probability distribution of the prediction errors and the point prediction power of the power of each wind power plant is used as an edge distribution model of the power of each wind power plant. The cumulative probability of the power data of each wind power plant is calculated, and the cumulative probability is taken as the input data of the time-varying R-vine Copula model. The joint probability distribution model of the high-dimensional wind power data is established by combining the ARIMA-GARCH-t model and the time-varying R-vine Copula model. Model parameters are fit based on historical power data of each wind power plant, and a multi-wind power plant power day-ahead prediction scene generation method is provided by combining point prediction power data of each wind power plant in the next day on this basis. The day-ahead prediction scene generation model established by the invention can better fit the time-space correlation characteristics of the power of the multiple wind power plants, and the accuracy and effectiveness of the day-ahead prediction scene of the power of the multiple wind power plants are improved.

Description

technical field [0001] The invention belongs to the field of scene prediction of wind power, and more specifically, relates to multi-wind farm power modeling, PDF (joint probability density function, probability density function) construction, and prediction scene generation methods and systems. Background technique [0002] In order to realize clean and low-carbon energy supply, wind power generation has received extensive attention in recent years. However, due to the fact that the accurate prediction of wind power is still difficult to achieve, in the context of the rapid growth of wind power grid-connected capacity, the uncertainty of 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 grid. ...

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

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