Power system load uncertainty analysis method

Abstract

The invention discloses a power system load uncertainty analysis method. A load sample is decomposed into a typical scene (a central sample), an extreme scene (an extreme sample) and a common scene (acommon sample). In order to ensure robustness, an extreme scenario (extreme sample) is used to determine a plant combination scheme. A typical scene (central sample) with a higher probability of occurrence is used to calculate a corresponding scheduling problem. Considering the characteristic that each load sample is high-dimensional data, a PCA technology is adopted to reduce an original load sample to a low-dimensional space so as to better reveal the relationship between the data. In this way, a scheduling scheme with high robustness and economy can be obtained.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to an analysis method for power system load uncertainty. Background technique [0002] Nowadays, with the development of economy and society, the demand for electricity has increased dramatically. The emergence of some new load sources, such as high-speed railways and electric vehicles, has brought great difficulties to load forecasting. These difficulties are mainly reflected in the highly variable nature of these loads. However, the traditional unit combination determines the on / off state of the generator through a certain load forecast value. Uncertainty caused by load forecast errors is not considered. However, the uncertainty of load has brought great challenges to the economy and reliability of the grid, such as frequency drop, overloading of transmission lines, and even cascading failures when the system is under heavy load conditions, etc. In order to effectively solve th...

AI Technical Summary

Problems solved by technology

These methods do not consider the relationship between scenes, and there is no clear scene division method

And the scene information is not fully used

For example, robust optimization and some improved methods only consider the use of the worst scenario, ignoring the information of other scenarios

In addition, the scene division of all methods is carried out in high-dimensional space, ignoring the fact that the relationship between scenes is easily revealed in low-dimensional space

Images