Scenario Sampling Method For Irregular Probability Distribution Of Wind Power And Its Application

In this paper,the uncertainty of wind power is modeled from the perspective of wind speed and wind power in wind farms.The model is a typical irregular distribution with characteristics of pulse and multi-peak,which can describe the actual wind power uncertainty more accurately.Based on the non-regular distribution of wind power,this paper studies different application fields involving wind power,analyzes and discusses the requirements of different sampling methods for different application fields,and analyzes and discusses the sampling methods suitable for their own applications.Therefore,based on the above brief description of the content of this article,the main work of this paper are as follows:1.Aiming at the uncertainty of wind power,this paper first establishes the probability distribution model of wind power according to the relationship between wind speed and wind power.Compared with the general model,the established model is a typical non-regular distribution with pulse,multi-peak and other features.2.Based on the non-regular description of wind power proposed in this paper,we can still use Monte-Carlo sampling method to study the probability power flow when considering the probability power flow problem of wind power,although the wind power distribution has irregular characteristics.The simulation results of probability trend also illustrate the correctness of the above theory.3.Based on the non-regular description of wind power proposed in this paper,when considering the probabilistic unit combination problem of wind power,the analytical method is difficult to apply due to the non-regular characteristics of the distribution,and the scene sampling method is needed.However,the traditional scene sampling methods are mostly proposed for the rule distribution.Therefore,for this non-regular distribution,the original scene sampling method is improved to ensure that the number of samples at the pulse point is 1.and for the area between the pulse points,The uniform sampling is performed after the normalization process.Through using this process,the improved scene sampling method is more suitable for the probabilistic unit combination problem.4.This paper compares the traditional Latin hypercube scene sampling method and the improved scene sampling method proposed for probabilistic unit combination from several metrics through several examples.Through comparison,the advantages of the improved scene sampling method are verified.Firstly,through the case analysis of the probabilistic unit combination based on scene method,the improved scene sampling method and the traditional Latin hypercube sampling method are compared respectively from the solving time and iteration times of the model.Secondly,by setting up an intra-day scheduling model,the above two sampling methods are compared from the coal consumption cost,switch-on cost,abandonment loss,cut load loss,total operation cost and wind power utilization rate of the intraday scheduling model.Lastly,a scene calibration model is established to compare the two sampling methods from the wind farm scene passing rate.