Markov chain modeling and predicating method based on wind power variable quantity

Abstract

The invention discloses a Markov chain modeling and predicating method based on wind power variable quantity. The method comprises the steps that firstly, linear transformation is carried out on existing original power data to obtain a wind power variable quantity data sample, then, according to the variable quantity data size and a statistical result of probability distribution, state space of a Markov chain model is divided as fine as possible, after the state is determined, a transition probability matrix of the variable quantity is obtained through statistical calculation, and Markov chain model construction is completed. The Markov chain model can be used for constructing a short-period and ultra-short period wind power predication method, and the theoretical basis is laid for real-time economic dispatch comprising a wind power system, optimized decision and model prediction control based on a Markov chain.

Description

technical field

[0001] The invention relates to a Markov chain modeling and prediction method based on wind power variation. Background technique

[0002] With the increasingly prominent energy and environmental issues, wind power has developed rapidly due to its clean, renewable, and huge reserves. According to the latest statistics from the China Wind Energy Association, in 2013, China (excluding Taiwan) newly added installed capacity of 16,088.7MW, a year-on-year increase of 24.1%; cumulative installed capacity of 91,412.89MW, a year-on-year increase of 21.4%. Both new installed capacity and cumulative installed capacity rank first in the world. Although the wind power generation technology continues to mature, the randomness, volatility and uncontrollability of wind power output still bring many problems to the large-scale grid connection of wind power. Therefore, it is of great significance to accurately model the fluctuation characteristics of wind power to realize t...

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