The Research On Probability Optimal Power Flow Of Power System Considering Wind Speed Correlation

Due to the uncertainty of wind power generation,the large-scale integration of wind power mades the planning and economic operation of the power system more difficult.The traditional optimal power flow calculation without considering uncertainties in the power system cannot reflect the power system' s overall operating status,and cannot provide accurate and reasonable decision-making information for the economic operation and safety analysis of the power system.Therefore,in order to achieve the safe and economical operation of the power system,it is necessary to combine the probability theory with the traditional optimal power flow calculation to establish a probabilistic optimal power flow model utilizing optimization algorithm to easonably arrange the output of the conventional generator.In addition to randomness,there is a certain correlation between the wind speeds of neighboring wind farms.The results of probabilistic optimal power flow calculation will be inaccurate without considering the correlation of wind speeds.Therefore,this paper studies the probabilistic optimal power flow calculation of power system considering wind speed correlation.First,the probability theory is introduced to simulate the random input of power system,and the probability models of load,conventional generator output and fan output are established.The three-point estimation method is used to calculate the probabilistic power flow of the wind farm.The node voltage statistics are obtained by simulation in the IEEE3 O-bus system and compared with the Monte Carlo method.The results show that the three-point estimation method used to calculate the probabilistic power flow is feasible and effective.Then,there is a certain correlation between the wind speeds between adjacent wind farms,but the joint probability density function between wind speeds cannot be obtained in practical applications.Thus the Nataf transform is introduced to deal with the relevant wind speed variables.The boundary probability density function of the wind speed and the correlation coefficient matrix are need in the Nataf transformation,both of which can be obtained actually.In this paper,the three-point estimation method based on Nataf transform is used to calculate the probabilistic power flow considering the wind speed correlation.And the simulation analysis is performed in IEEE30 node system with two wind farms.The output variables' statistical characteristics of the probabilistic load flow are obtained,and the impact of wind speed correlation on node voltage and branch transmission power is analyzed.Compared with the Monte Carlo method,the adopted calculation method is more accurate.Finally,with consideration of the wind speed correlation,a probabilistic optimal power flow modelcombining probabilistic power flow with optimal power flowis established.On the basis of the Nataf transform-based three-point estimation method,a new intelligent optimization algorithm,i.e.,differential search algorithm,is used to solve the problem.Due to unique search mechanism,the differential search algorithm is not completely dependent on the optimal solution,and has better optimization ability,convergence speed and simple calculation process without complicated formula derivation.Compared the simulation results of the PSO,GA and GSOon the benchmark function,the differential search algorithm has better performance.Thus this paper applies the differential search algorithm to probabilistic optimal power flow calculations and study.Two examples of simulation analysis are performed on the IEEE 30bus system with two wind farms.The effects of wind speeds with different correlations on the output of conventional generators and the cost of power generation is studied.Then the further study on the influence of load correlation on the probabilistic optimal power flow results is carried,when the wind speed is highly correlated.The simulation results show that the model and the solution algorithm established in this paper can provide better decision-making basis for power system operation.