Joint probability density prediction method for short-term output power of multiple wind farms

Abstract

The invention discloses a joint probability density prediction method of short-term output power of a plurality of wind power plants. The method comprises the following steps: carrying out single point value prediction on output power of each wind power plant by using a support vector machine regression prediction model; building a sparse bayesian learning model as to a prediction error to carry out probability density prediction of the error, so as to obtain an expected value and a variance of marginal probability density function prediction of the output power of a single wind power plant; carrying out statistic analysis on the prediction error characteristics of the output power of the plurality of wind power plants, building a dynamic conditional correlation-multivariate generalized autoregressive condition heteroscedasticity model, and integrating a marginal probability density prediction result of the output power of the single wind power plant and a correlation coefficient matrix to obtain a joint probability density function of the output power of the plurality of wind power plants; forming a multidimensional scene including space-time correlation characteristics by using a sampling technique. By adopting the joint probability density prediction method, a mean prediction value and prediction uncertainty information of the output power of the single wind power plant can be provided; the dynamic space-time correlation characteristics between output power prediction of the plurality of wind power plants also can be quantitatively described.

Description

technical field [0001] The invention relates to a method for predicting joint probability density of short-term output power of multiple wind farms. Background technique [0002] The large-scale grid-connection of wind power has relieved my country's energy pressure and brought huge economic and environmental benefits. It is currently the most mature technology and most suitable for large-scale development of renewable energy. However, wind power is an intermittent and uncontrollable power source, and its large-scale integration into the grid will inevitably increase the difficulty of system operation and control, and increase the burden of system backup. Therefore, it is very important to predict the output power of wind farms and wind farm groups [Lei Yazhou. Research topics related to wind power grid integration [J]. Electric Power System Automation, 2003,27(8):84-89.] . [0003] Short-term wind power prediction is generally to predict the active power of wind turbines ...

AI Technical Summary

Problems solved by technology

None of these research works took into account the correlation between forecast periods and the spatio-temporal correlation characteristics between the output power forecasts of multiple wind farms.

Images