Research On Probabilistic Power Flow Of Power Systems Considering Large-scale Access Of Renewable Energy

Facing the worse and worse problems of energy shortage and environmental pollution,large-scale of access of renewable energy has become a main trend of the development of power systems.On the one hand,large-scale access of renewable energy brings us lots of clean energy.On the other hand,it brings more random factors,making the operation status of power systems more complex.Probabilistic power flow is an important tool for power system planning and operation state analysis.However,current probabilistic power flow algorithms have many disadvantages,such as large computation error,low operation efficiency and only considering parts of random variables.More importantly,current probabilistic power flow algorithms usually ignore the influence of input variable correlation on power systems.Related researches show that wind speeds in the same area have certain correlation.With the increase of installed capacity of wind power,the influence of correlation on power systems has become a problem that should not be neglected.Therefore,in order to solve above problems of probabilistic power flow algorithms,this thesis puts forward a hybrid probabilistic power flow algorithm with high accuracy and efficiency.And on this basis,combined with Cholesky decomposition method,this thesis studies the influence of wind power correlation on power systems.First of all,a hybrid probabilistic power flow algorithm,which considers continuous and discrete random variables,is proposed in this thesis.The proposed algorithm combines cumulant method and multiple deterministic power flow calculations.Compared with Monte Carlo method and cumulant method,the calculation accuracy and operation efficiency of the proposed algorithm are verified in the IEEE 14-bus power system.This thesis investigates two issues affecting power systems:(1)What are effects of a discrete variable under different rated powers on power systems;and(2)How can we address multiple discrete variables in power systems.In the IEEE 118-bus power system,the accuracy,efficiency and applicability of the proposed algorithm are verified.Secondly,on the basis of the proposed hybrid probabilistic power flow algorithm,this thesis combines Cholesky decomposition method to study the influence of wind power correlation on power systems.First,when two or three wind power fields exist in power systems,the accuracy of Cholesky decomposition method which generates wind data with the arbitrary correlation is verified.Second,in the IEEE 14-bus power system,the accuracy of the proposed algorithm is verified when the proposed algorithm takes into account wind power correlation.Third,the influence of wind power correlation on means and standard deviations of bus voltage and branch power flow is studied in the IEEE 14-bus power system.Finally,the conclusions of the hybrid probabilistic power flow algorithm and wind power correlation are summarized in this thesis.