A Critical Line Identification Method In Power System Based On Probabilistic Load Flow

In recent years,the study of power system security analysis brought new challenges by a series of large-scale blackouts.Research indicates that the cause of the blackout accident is mostly a chain malfunction caused by a few critical line malfunction.Therefore,identifying critical lines is important to improving the overall safety level of the grid.The criticality of the line is closely related to its role in system power transmission.Meanwhile,due to the continuous improvement of new energy generation penetration rate and the diversification of the load nature,many uncertainties have been brought to the power grid,making the power system more complex and changeable,and the role of each line in the system power transmission is also changed,the criticality of the line also changes with the system’s operating state at any time.When determining critical lines of the power system,the influence of the uncertainty factor must be considered to more accurately identify critical lines of the power system.Probabilistic load flow algorithm can comprehensively reflect the influence of uncertain factors on power transmission of power system.Based on the probabilistic power flow algorithm,taking into account the uncertain factors to carry out the identification of key lines of the power system,more comprehensive and accurate identification results can be obtained,and the operation and planning of the power system can be guided.Based on probabilistic load flow(PLF)analysis of Monte Carlo simulation method,this paper analyzes the influence of wind power and load uncertainty on the distribution of branch flow.the risk theory is introduced to put forward,and probabilistic flow betweenness considering uncertainty of wind power and load is presented for critical line identification,which reflects the influence of power grid structure characteristics,change of system running mode and uncertain factors on power transmission.It can reflect the differences in critical line identification of the grid under different operating conditions.It can effectively identify the potential vulnerability of the power grid affected by random factors such as wind power output and user load demand.It compensates for the insufficiency of critical lines identified in existing literature for deterministic environments,making the identification of critical lines more comprehensive and accurate.In this paper,a probabilistic load flow algorithm with improved Cumulant method.For the power system containing multiple random variables simultaneously,the proposed method classifies multiple random variables according to their probability distribution functions(PDFs).In order to reduce linearization errors of cumulant method(CM),the proposed method uses multiple CMs to calculate power flower responses caused by multiple random variables respectively.Meanwhile,for decreasing series expansion errors of CM,type C Gram-Charlier series is applied to fit PDFs of various types of power flow responses.The test,on accuracy and efficiency of the proposed method,is carried in the improved IEEE 14-bus system.And the test demonstrates that: compared with the standard results obtained by Monte Carlo method(MC),the results obtained by the proposed method is matching and computational efficiency is higher than MC;compared with the results obtained by conventional CM,the proposed method has higher computational accuracy for PLF problems.Based on the analysis of probabilistic load flow algorithm with improved Cumulant method,taking into account wind power fluctuations and load fluctuations,the power flow response distribution function is obtained based on its probability distribution function,and then the wave power of the line flow is obtained,and the power is further constructed using the power flow fluctuation power of the line.The integrated transmission intermeshing indicators that take into account uncertainties are applied to the identification of key lines in power systems that contain multiple random variables to fully reflect the effects of changes in grid structure,operating modes,and uncertainties.Complex large power grids have better practicality.The analysis of the example shows that this index is equally effective for identifying critical lines,and avoids the huge amount of calculations caused by the Monte Carlo method for identifying multipl loops in critical lines.