Distribution Network Reconstruction Considering The Uncertainty Of Wind Power Output

With the continuous expansion of the scale of new energy resources connected to the distribution network,the volatility of its output brings great pressure to the power network.Therefore,it is necessary to deal with the uncertainty of new energy resources output.This paper studies the distribution network reconstruction considering the uncertainty of wind power output.There are mainly the following points:Firstly,around the problem of distribution network reconfiguration with distributed generation,the significance of distribution network reconfiguration and the impact of distributed generation are analyzed.The topological structure of the distribution network and the power flow calculation method based on the forward and backward substitution method of branch current are studied,which lays the foundation for the reconstruction of the distribution network.Secondly,a scenario analysis method of wind power output is proposed.The wind power output is divided into several intervals according to the predicted value.Then,fit the measured output value in each interval.In this paper,the Gaussian kernel density estimation method is chosen.The probability distribution function of wind power output is obtained.Through the scene generation method of Latin hypercube sampling,thousands of output samples were extracted at each time period to form the initial scenes.The improved K-means clustering method was used to reduce the initial scenes.And the typical scene set describing the randomness of wind power output was obtained.Taking the historical data of wind farm in Northern Ireland as an example.The simulation results show that typical scenarios have strong ability to describe the uncertainty of wind power output.Thirdly,the improved particle swarm optimization is studied.In this paper,the basic particle swarm optimization algorithm is deeply analyzed and studied.Aiming at the defects of the basic particle swarm optimization algorithm,chaos optimization and the principle of biological immune system are studied,and combined with the particle swarm optimization algorithm.So,the immune particle swarm optimization algorithm based on chaos optimization is obtained.The algorithm speeds up the convergence speed,is easier to get the global optimal solution.The effectiveness of the improved algorithm is proved by the reconfiguration simulation of IEEE33 node system.Finally,the dynamic reconfiguration model of distribution network is established.A threelayer optimization strategy of multi-time dynamic reconstruction is proposed.In the first layer,static reconstruction is carried out for each time period,aiming at network loss and voltage offset index,to form the sequential reconstruction results.In the second layer,the typical scenario set of wind power output are used to optimize the reconstruction results of each period.In the third layer optimization,with the economics of the distribution network as the goal,the whole-time overall optimization is carried out through the collaborative optimization function.And the final dynamic reconstruction results are obtained.The simulation of IEEE33 node system and an actual example verify that the dynamic reconfiguration strategy can effectively reduce the operation cost of distribution network.