Joint probability density forecasting method for output power of multi-wind farms

Abstract

A method for predicting the joint probability density of output power of multi-wind farms includes such steps as establishing a prediction model of a sparse Bayesian learn machine, predicting the probability density of output power of wind farms in multiple independent time periods in the future, predicting the probability density of output power of multi-wind farm, predicting the probability density of output power of sparse Bayesian learning machine, predicting the probability density of output power of wind farm in multiple independent time periods in the future, predicting the probabilitydensity of output power of sparse Bayesian learning machine, predicting the probability density of output power of multi-wind farm. The sparse Bayesian learning machine is used to get the prediction error samples, and then the correlation coefficient matrix between prediction errors is obtained according to the prediction error samples. The sparse Bayesian learning machine is used to forecast themean and variance of wind farm output power, and the covariance matrix is obtained by combining the mean and variance predicted with correlation coefficient matrix, and the joint probability density prediction is completed. The method improves the accuracy and effectiveness of wind farm output power prediction by forecasting the output power of each period of wind farm and the correlation betweenthe output power of each period of wind farm, makes the prediction more close to the actual situation of the real wind farm, and provides more abundant and accurate information for the dispatching decision-making of the power system with wind farms.

Description

technical field [0001] The embodiment of the present invention relates to the technical field of electric power system, and specifically relates to a method for predicting joint probability density of output power of multiple wind farms. Background technique [0002] With the gradual depletion of fossil energy and the accelerated deterioration of the global environment, energy and environmental issues have become increasingly prominent. Vigorously developing renewable energy power generation technologies and new electric energy utilization technologies, and accelerating the promotion of clean substitution and electric energy substitution have gradually become an important way to deal with energy and environmental crises. As a new energy power generation, wind energy has the advantages of being clean and renewable. However, because wind energy is significantly affected by the external natural environment, its output power is highly uncertain, and large-scale grid-connected po...

AI Technical Summary

Problems solved by technology

However, judging from the current research status, most of the existing distribution predictions are carried out on a time-by-period basis, without considering the correlation characteristics of wind farm output power in different time periods

This often leads to frequent and large fluctuations in the predicted value of the output power of the wind farm, which does not conform to the actual operating characteristics of the wind farm.

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