PROBABILITY CHARACTERISTIC ANALYSIS AND SCENARIO MODELING METHOD FOR MAIN-NOISE COMPONENTS OF PHOTOVOLTAIC POWER OUTPUT CONSIDERING SEASONAL FACTORS
The proportion of photovoltaic power generation systems connected to the transmission and distribution grid has increased year by year.Because the output of photovoltaic power stations in different seasons usually has a large difference in output characteristics,the traditional universal photovoltaic output simulation method is difficult to cope with the requirements of simulation scenarios under various conditions.Addressing this challenge,the present study introduces a multi-scenario simulation approach for accurately modeling the seasonal power outputs of photovoltaic plants.This approach involves a detailed analysis of data gathered across different seasons,deconstructing it into fundamental(principal)components and stochastic(noise)elements.Thus,the output power of photovoltaic power stations can be effectively modeled in different seasons.In our proposed framework,firstly,a probabilistic model based on Beta distribution is adopted to simulate the optimal base component of PV output,and the Beta parameter interval estimation method under different seasons is given.Secondly,the colored noise model is used to simulate the uncertainty characteristics of the random component of photovoltaic power output.According to the different fluctuation intensity of photovoltaic power output in different seasons,the construction method of random component noise parameters is given.Then,the base component and random component are used to simulate the output distribution of photovoltaic power stations in different seasons,and the construction of photovoltaic seasonal scenarios is completed.Finally,a case study based on the measured data of a photovoltaic power station in southeast China is carried out to verify the applicability of the proposed method.
photovoltaic power systemsphotovoltaic effectspower generationBeta distributioncolor noise modelscenarios generation
NETL
NSTL
万方数据
