Method for predicating interval probability of short-term wind power

Abstract

The invention discloses a method for predicating interval probability of short-term wind power. The method comprises the following steps: acquiring a number of historical wind power from a wind power plant as a sample set; establishing optimization criteria according to the prediction interval coverage probability, the prediction interval bandwidth mean square root and the prediction interval average offset; establishing the interval predicating model of the short-term wind power based on an artificial bee colony nerve network, optimizing and updating a nerve network weight threshold to the optimization criteria through an artificial bee colony algorithm; according to the optimal weight threshold, establishing a nerve network and performing interval predication to the wind power to be predicated; performing state division to the historical wind power, establishing a Markov chain prediction model, and calculating the transition probability of each status; predicating the wind power interval according to the Markov chain status transition probability and the interval predication, and calculating the probability of the numerical point in the predication interval. When the short-term wind power interval predication is executed, the probability distribution of the numerical point in the interval is considered, thus the method can provide the basis to an optimization system.

Description

technical field [0001] The invention belongs to the technical field of power systems, and in particular relates to a short-term wind power interval probability prediction method. Background technique [0002] Wind energy is an ideal clean energy. With the continuous increase of wind power installed capacity, the intermittent and uncertain nature of wind power brings new challenges to the stable operation of the power system and grid dispatching. Accurate prediction of wind power is of great significance to the power balance, economic dispatch and equipment safety of the power system. It can not only optimize the reserve capacity and reduce the operating cost of the power grid, but also reduce the impact of wind power on the power grid and improve the reliability of power grid operation. Wind power prediction methods can generally be divided into two categories: one is to use numerical weather forecasting to establish a prediction model to convert weather data into wind pow...

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Problems solved by technology

[0003] At present, wind power forecasting is generally a single point forecast, and wind power output is affected by many uncertain factors such as turbulence, wind turbine status and background noise, and a single point forecast cannot accurately reflect the uncertain factors.

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