Multi-wind-power-plant combined output prediction method and device based on dynamic R-Vine Copula model

Abstract

The invention discloses a multi-wind-power-plant combined output prediction method and device based on a dynamic R-Vine Copula model, which belong to the field of wind power interval prediction in an electric power system. The method comprises the steps of S1, taking prediction output data of multiple wind power plants and prediction errors corresponding to combined output as first input data, S2, inputting the first input data into a dynamic edge distribution function model established based on an ARIMA-GARCH model, so that the dynamic edge distribution function model converts the first input data into a cumulative probability sequence, S3, inputting the cumulative probability sequence into a pre-established dynamic R-Vine Copula model so as to enable the dynamic R-Vine Copula model to output joint output prediction results corresponding to multiple wind power plants under different confidence coefficients, wherein the model parameters of the dynamic edge distribution function model and the model parameters of the dynamic R-Vine Copula model are subjected to rolling calculation and updating based on a phase space reconstruction method. Thus, the ultra-short-term interval prediction accuracy of multi-wind-power-plant combined output can be improved.

Description

technical field [0001] The invention belongs to the field of wind power interval forecasting in electric power systems, and more specifically relates to a method and device for joint output forecasting of multiple wind farms based on a dynamic R-Vine Copula model. Background technique [0002] At present, my country's wind power installed grid-connected capacity is continuing to grow steadily, which has strongly promoted the transformation of the existing power supply and demand structure to a green and low-carbon direction. However, accurate prediction of wind power power is still difficult to achieve. In the context of rapid growth of installed capacity, the limited prediction accuracy makes the uncertainty of wind power power difficult to ignore. To deal with the above problems, interval forecasting of wind power is an effective method. Different from traditional point forecasting methods, interval forecasting can provide complete probability distribution information of ...

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Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a method and device for predicting the joint output of multiple wind farms based on the dynamic R-VineCopula model, aiming at solving the problem that the existing model cannot fully consider the joint output of multiple wind farms. Dimensional and time-varying correlation characteristics to improve the accuracy of ultra-short-term interval forecasting of joint output of multiple wind farms

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