基于多保真度模型的高比例新能源配电网潮流不确定性表征方法OA北大核心CSTPCD

In the context of the decarbonization transition of energy production and consumption,it is important to quantify the impacts of source-load uncertainty and realize the uncertainty representation of power flow efficiently and accurately for the safe and reliable operation of the distribution network.This paper classifies the existing uncertainty representation methods into two categories including high and low fidelity models based on their computational efficiency and accuracy,and comprehensively analyzes the disadvantages of both methods.On this basis,an uncertainty representation method of power flow in distribution network with high percentage of renewable energy based on the multi-fidelity model is proposed to achieve high-precision estimation of the statistical moment of the power flow status variables and depict the probability distribution function.In terms of statistical moment estimation,an optimal input sample allocation method combining the characteristics of high and low fidelity models is proposed to achieve unbiased estimation of the statistical moment of output variable with a given computational budget.In terms of probability distribution function fitting,a method based on the comprehensive startup function is proposed to improve the accuracy of the fitting result by using the prior information provided by the multi-fidelity model.The effectiveness of the proposed method is verified by simulation calculations of 118-node distribution network.