A generation scheduling method based on high-dimensional wind power forecasting error model and dimensionality reduction technology

Abstract

The invention discloses a power generation scheduling method based on a high-dimension wind-electricity prediction error model and the dimensionality reduction technology. The method comprises steps of: acquiring history output data of each hour in one year of multiple wind power plant and corresponding point prediction data; using a mixed skewness model to carry out modeling on accumulated distribution functions of actual output and predicted output of each wind power plant; using the CDF of each wind power plant to convert the actual output value and the prediction value into data points distributed in 0-1 intervals; by matching all data points obtained in the previous step, finding out the optimal Copula function and carrying out parameter estimation; establishing high dimension condition probability model of multiple wind power plant prediction errors, and obtaining edge condition probability models subjected to dimensionality reduction trough edge conversion; and according to the edge condition probability models of the wind power plant prediction errors, calculating the current scheduling plan of the generator unit and the rotation standby capacity. Compared with the common gauss distribution and beta distribution, the power generation scheduling method is quite high in precision, and effects of relevance between multiple wind power plants can be considered.

Description

technical field [0001] The invention relates to the field of new energy power generation in power systems, in particular to a power generation scheduling method based on a high-dimensional wind power prediction error model and dimensionality reduction technology. Background technique [0002] With the increasing energy consumption, energy supply continues to be tense. Renewable energy represented by wind power has been greatly developed. However, wind power output has the characteristics of strong random fluctuations and poor power regulation capabilities, and is greatly affected by the meteorological environment and wind farm layout. Come to great challenge. In addition, the current wind power construction planning has "heavy power generation, light supply, and no use". The development of wind power is ahead of the planning of the corresponding regional power grid, and the planning and development of the two are not coordinated. The main reason for wind curtailment cause...

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Problems solved by technology

However, the solution process of the β distribution is very complicated, and there will be anomalies with infinite error probability density in some predicted power intervals; the Cauchy distribution and the Laplace distribution are both symmetrical distributions, the shape is not flexible enough, and the scope of application is relatively fixed

In view of the diversity of short-term wind power forecasting error distribution, there is no distribution model that can adapt to more error distribution characteristics, which can describe both symmetric distribution and unimodal distribution, as well as asymmetric distribution and multimodal distribution.

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