Probabilistic Load Flow Algorithm And Impact Of Distribution Networks With Distributed Generators And Electric Vehicles Integration

Probabilistic Load Flow(PLF)calculations are important tools for analysis of the uncertainty of electrical energy networks.In recent years,renewable energy sources such as distributed photovoltaic array and wind turbines have been rapidly developing and electric vehicles(EVs)are also largely adopted in daily life,which Leading to increase complexity and randomness of power systems.In order to guarantee the economic operation and security and stability analysis of power system better,higher demands are put forward for the accuracy,speed and cost of probabilistic load flow calculation,In the existing probabilistic power flow method,analytical method and approximation method have small calculation,but the approximate treatment must be carried out.Most scholars use the simulation method,The Monte-Carlo simulation(MCS)method can handle a variety of complex conditions without simplification,but the amount of calculation is large.Although the sampling speed of LHS has been greatly improved,it needs to be brought into the power flow calculation program after sampling.The calculation results are obtained through several load flow calculations to fit the probability distribution of node random variables.The total time of probabilistic load flow calculation is too long,which affects the simulation efficiency.Traditional LHS sampling methods are often combined with Cholesky decomposition,and the Cholesky decomposition adopted by the traditional LHS has limitations in that it is only applicable for positive definite matrices.The main content of this paper:(1)An improved LHS based cumulant method(ILHS-CM)PLF algorithm is proposed.The random walk theory is applied to LHS,which has high sampling efficiency and can overcome the limitations of Cholesky decomposition.The improved LHS method is used to calculate the cumulant of the input variables and the probability distribution of the output variables is obtained using Gram-Charlier series expansion,which not only solves the problem of correlation of input variables,but also inherits the advantages of cumulant PLF.(2)After distributed power and random loads such as electric vehicles are connected to the distribution network,it will have a great impact on the voltage of the distribution network.Based on the above-mentioned probabilistic load flow sampling method,the influence of distributed power and electric vehicle integration on the power system is analyzed.By analyzing and calculating various factors such as integration location and integration quantity,different conclusions about the distributed power connected to the grid are obtained.(3)In consideration of electrical energy distribution networks with distributed wind power,solar PV and EV integration,the correlation between them is analyzed.In addition to traditional wind speed correlation of wind turbines,and the power correlation of DGs and EVs,spatial correlation is introduced to obtain the correlation matrix,based on the connection impedance of DGs and EVs.Furthermore,this paper proposes the deviation index to quantitatively describe of the impact of the correlation on PLF.(4)Dynamic PLF is considered with EV integration,based on the obtained probability distribution function(PDF)of PLF outputs:uncoordinated and coordinated charging of the electric vehicle are analyzed.Indicators,which describe the coordinated charging that improves the operating performance of energy networks,are proposed.