Research On Reactive Power Optimization Of Distribution Network Based On Ant Colony Algorithm

Due to the lack of reactive power and the unreasonable distribution in the power system,problems such as high line network loss,low voltage level,and poor power quality have occurred.Reactive power optimization is an effective means to ensure safe and economic operation of the system,and is one of the important measures to improve power quality.Therefore,how to use and optimize reactive power resources,further reduce distribution network losses,and improve voltage qualification rate is of great significance.According to whether the load distribution of the distribution network is considered,the reactive power optimization problem can be divided into two categories: static reactive power optimization and dynamic reactive power optimization.In this paper,starting with static reactive power optimization,the goal of reactive power optimization is to obtain the highest investment income.At the same time,considering the economics and safety of the system,a static reactive power optimization objective function was established.The genetic algorithm and the algorithm in this paper are selected to calculate the optimal position and compensation capacity of the reactive power compensation device to be installed when the load level is unchanged,and a comparative analysis is performed to verify the feasibility of the ant colony algorithm.Introduce the actual data of the annual load in a certain area to study the dynamic optimization problem under the change of the load level.Due to the huge amount of load data and the characteristics of randomness and volatility of load changes,scene analysis technology is used to convert complex models that are difficult to model into deterministic models that are easy to solve,which reduces the difficulty of modeling and solving.By means of K-means clustering algorithm with daily data as clustering unit,this paper carries out clustering analysis on annual load change data to realize the construction of typical scenes,and obtains six typical time series scenes which can characterize annual data characteristics and the probability of each scene.When considering a single scene,each scene can be equivalent to a static state.Therefore,based on the static reactive power optimization objective function,the objective function of dynamic reactive power optimization is obtained,and the optimal position and compensation capacity of the reactive power compensation device to be installed under the load level change are obtained by using the ant colony algorithm.Finally,after the IEEE-33 node system simulation calculation,the results show that: Compared with the ant colony algorithm and the genetic algorithm to calculate the static reactive power optimization of the distribution network,using the optimization results of the ant colony algorithm to reduce the overall operating costs,reduce network losses,and improve voltage stability.By comparing the results of dynamic reactive power optimization and static reactive power optimization based on K-means clustering and typical scene analysis,it can be seen that the dynamic optimization is more suitable for the actual situation of load change,the investment income is higher,the number of nodes to be compensated is reduced by one time,the total compensation capacity is reduced,the total network loss is smaller,and the voltage is more stable.