Research On Reactive Power Compensation Of Distribution Network Considering Distributed Wind Power Access

In recent years,with the accelerating industrial process and the rapid growth of electricity demand,power generation is facing great challenges.The sources of electricity are roughly divided into two types : renewable and non-renewable.Due to the continuous use of nonrenewable energy for power generation,problems such as deterioration of the ecological environment and abnormal climate have become increasingly prominent,and the transformation of the global energy structure has become more urgent.In order to effectively alleviate the environmental problems caused by non-renewable energy,renewable energy has been widely used,and distributed power sources based on renewable energy are increasingly connected to the power grid.However,compared with traditional power generation,the randomness and volatility of distributed power access are more complex,which brings challenges to power grid operation.As a kind of distributed energy,wind power has been widely used because of its clean and efficient.In this paper,the reactive power compensation optimization of distribution network is carried out in view of the influence of randomness and uncertainty factors after distributed wind power access to distribution network.Firstly,the wind power output model is established to analyze the impact of distributed wind power on the system.Secondly,according to the historical data of wind speed-load in a certain place,the improved variance comparison clustering method is used to extract typical scenes,and the double index criterion of intra-class,inter-class index and cosine similarity based on Euclidean distance is established to obtain the optimal clustering number.Then,according to the characteristics of the capacitor bank and the Static Var Generator(SVG),the joint compensation measures are used for optimization.A bilevel optimization model is established.The upper model takes the investment cost of reactive power compensation device as the objective function,and the lower model takes the minimum network loss cost and voltage offset in each scenario as the objective function.The lower model calculates the configuration results after reactive power optimization of each scenario,and returns the results to the upper layer for re-renewal.After iteration,the optimal compensation configuration is solved.Finally,three strategies of random inertia weight,second-order oscillation and natural selection are used to improve the particle swarm optimization algorithm.The original particle swarm optimization algorithm and chaotic particle swarm optimization algorithm are used as comparison.After calculating the test function,the superiority of the improved particle swarm optimization algorithm in accuracy and convergence speed is verified.The bi-level optimization model and improved algorithm are verified by IEEE-33 and IEEE-118 node systems.The results show that when the distributed wind power is connected,the improved particle swarm optimization algorithm can reduce the annual comprehensive cost,improve the voltage stability and reduce the network loss.