Reactive Power Optimization Of Active Distribution Network Based On Distributed Generation Access

The depletion and pollution of traditional fossil energy has promoted the vigorous development of clean energy represented by wind power and photovoltaic.It is connected to the distribution network in the form of distributed power.The access of the distributed power supply makes the power flow of the distribution network change from one-way to two-way,and the control of the distribution network becomes more complicated.Reactive power optimization is an important part of active distribution network control,and it is the premise of realizing safe,economic and stable operation of distribution network.Therefore,it is of great significance to carry out research on it.In this paper,the reactive power optimization of the active distribution network for distributed power access is studied.The main work is summarized as follows:(1)The impact of distributed power access on the distribution network is studied.This includes the impact on distribution network voltage,power quality,and grid reliability.The characteristics and control methods of the active distribution network are analyzed.The control model and existing problems of the distributed power supply in the active distribution network are studied,which lays a theoretical foundation for the subsequent chapters.(2)In order to realize the decoupling of active distribution network space,a reactive power partitioning method for active distribution network is proposed.The ordered sample space is constructed by using the electrical distance between nodes,and the active distribution network is partitioned by Fisher optimal segmentation method.The simulation is performed using the IEEE 33 node as an example.The simulation results show that the proposed partitioning method has the characteristics of short calculation time and no manual intervention,and it can ensure sufficient reactive reserve in each sub-area after partitioning.(3)An improved teaching and learning algorithm is proposed and applied to the reactive power optimization with the minimum active power loss as the objective function.Numerical experiments were performed on the improved teaching and learning algorithms.The experimental results show that the improved teaching and learning algorithms have greatly improved the convergence speed and convergence precision.The improved teaching and learning algorithm is used to optimize the reactive power distribution of the partitioned active distribution network,and the improved IEEE 33-node system is used to simulate the example.The simulation results show that the first partition and then reactive power optimization can greatly reduce the optimization time,and its active network loss is also smaller than the reactive power optimization method without partitioning.The voltage of each node of the system is increased and not exceeded.