Probabilistic Power Flow Calculation Of Power System Considering Wind Power Based On Sparse Expansion Of Chaotic Polynomials

With the concept of green,environmental protection and sustainable development deeply rooted in the hearts of people,the new type of green energy accounts for an increasing proportion in the power generation industry.Compared with traditional fossil energy power generation,new energy power generation technology such as photovoltaic power generation and wind power generation has the advantages of green and pollution-free,but its intermittent and random characteristics due to natural conditions make the operation of power system more complex.The introduction of the large number of random factors make the trend of the power system solution is no longer a certain value,and shows the characteristics of probability,thus introduce the concept of probability power flow.Based on the advantages and disadvantages of the traditional probabilistic power flow algorithm and the uncertainty quantification theory,this thesis improves the traditional algorithm,and studies the influence of the randomness of wind power output and the correlation due to the geographical environment on the power flow response,such as node voltage and line flow.The specific research contents and methods are as follows:(1)The traditional Monte Carlo simulation needs a large number of samples to ensure the convergence of the results.In this thesis,the low deviation sequence is used to replace the traditional pseudo-random sequence in the sampling process of Monte Carlo to achieve the convergence of the results with a small number of samples.Considering the correlation of wind power generation,Copula theory is used to establish a joint normal distribution model to reasonably describe the correlation between wind speeds.Combined with the above two steps,the existing Monte Carlo probabilistic power flow algorithm is improved,and then the IEEE-14 node test system is used as the carrier to verify the accuracy and efficiency of the proposed algorithm.(2)In order to solve the problem that the model building process is complex or even impossible in the multi-dimensional stochastic variable system,the first-order sensitivity coefficient of input variables(node loads)to power flow responses(node voltage magnitude,phase angle,branch power)is calculated in the standard test system by combining the variance decomposition technology and the concept of sensitivity coefficient and the expansion of chaotic polynomials is simplified and its sparse expression is obtained.Nataf transform is used to control the correlation between the input variables,so that it can meet the requirements of independent normal as the inputs of chaotic polynomial expansion model.In IEEE-9 and IEEE-118 nodes test system,the accuracy and validity of the algorithm are verified,and the influence of fluctuation about loads and wind power outputs on node voltage and branch power is analyzed.The case study shows that the time needed to build the algorithm model is far less than that of the original algorithm in the system of multi-dimensional random variables.(3)In IEEE-30 nodes system,the influence of the correlation between wind power outputs on power flow responses is analyzed by the chaos polynomial sparse expansion model.The results of case study show that the mean value of power flow responses does not change with correlation coefficients of wind speeds,and the standard deviation of power flow responses changes linearly with the correlation coefficient of wind speed.