Method for constructing power generation time sequence simulation scene of multiple wind power plants

Abstract

The invention discloses a method for constructing a power generation time sequence simulation scene of multiple wind power plants. The method comprises the following steps: historical wind power sequences PN(t) are decomposed into low-frequency trend sequences Pl(t) and high-frequency random sequences Ph(t); the low-frequency trend sequences Pl(t) of different wind power plants are subjected to wind power fluctuation pairing; rising and falling durations of different wind power fluctuation processes are subjected to statistics, thereby obtaining a rising duration set {Trise} and a falling duration set {Tfall} of a time parameter set which embodies a fluctuation time-shifting characteristic; fitting is carried out on the paired wind power fluctuation processes through a Logistic function toobtain fitting parameters which are the fluctuation amplitude {L}, the rising steepness {Krise} and the falling steepness {Kfall} respectively; and an optimal Copula function model is built and parameters are extracted from the model, thereby constructing a power generation time sequence simulation scene of the multiple wind power plants. The method can simulate change rules of historical outputsof the multiple wind power plants at the same time, and has important significance for long-term planning, year / month scheduling and safe and stable operation in a power system.

Description

technical field [0001] The invention belongs to the technical field of new energy power generation, and in particular relates to a method for constructing a sequence simulation scene of power generation of multiple wind farms. Background technique [0002] At present, the commonly used evaluation method of new energy consumption capacity is the time-series production simulation method, which has high calculation accuracy and clear physical meaning. However, there are few studies on the time-series production simulation method considering the correlation of multiple wind farms, and there are many problems to be solved urgently. First of all, the traditional wavelet decomposition and reconstruction algorithm has poor resolution in the high-frequency band, which makes the filtering effect unsatisfactory; secondly, the traditional time series simulation method mostly uses Gaussian function to fit the wind power fluctuation process, but because the wind power fluctuation process ...

AI Technical Summary

Problems solved by technology

However, there are few studies on the time-series production simulation method considering the correlation of multiple wind farms, and there are many problems to be solved urgently

First of all, the traditional wavelet decomposition and reconstruction algorithm has poor resolution in the high frequency band, which makes the filtering effect unsatisfactory; secondly, the traditional time series simulation method mostly uses Gaussian function to fit the wind power fluctuation process, but because the wind power fluctuation process is asymmetrical, it is inevitable There are situations where the rise is fast and the fall is slow, or the rise is slow and the fall is slow. These situations cannot be solved by the Gaussian function, and there must be large errors; at the same time, a single Copula function cannot accurately describe the complexity of multiple wind farms. relevance of

Images