Research On Reactive Power Optimization Of Distribution Network With Distributed Generators Based On Improved Differential Evolution Algorithm

Reactive power optimization is an important means to decrease active power loss,improve power quality and increase the stability of the power system.Traditional reactive power optimization problem mainly reduce network loss and improve power quality by adjusting the generator terminal voltage,the number of switching groups of the tap changer and the reactive power compensation device.In recent years,distributed generation technology has received extensive attention from researchers across the world,due to its advantages of low cost,high utilization rate of energy and small environmental pollution.Distributed power grid has changed the traditional one-way flow characteristics of the distribution network,part of the distributed power can even through control the reactive power output within a certain scope to participate in the reactive power optimization.All of this has brought some variables to power flow calculation and reactive power optimization.With the expansion of distribution network and the large number of access to distributed generation,it is of great theoretical significance and practical value to study multi-objective reactive power optimization of distribution network containing distributed generation.This paper mainly studies the problem of reactive power optimization in distribution network with distributed generators based on modified differential evolution algorithm.The power flow algorithm with different types of distributed power supply is improved,and the influence of the distributed power grid on the power flow,voltage and network loss of the distribution network is analyzed.Establishing a reactive power optimization mathematical model with the object of active network loss,in order to solve reactive power optimization problem,the chaotic self-adaptive control parameters modified differential evolution algorithm is proposed.Compared with the traditional differential evolution algorithm,self-adaptive strategy is introduced to control parameters.Combining differential evolution algorithm with chaos optimization algorithm,chaotic mapping principle is applied to generate the initial value of differential evolution algorithm.When the variance of the population’s fitness is less than a certain value at present,local depth search will be proceeded around the optimal value based on chaos disturbance.Based on the IEEE33 node distribution network system model,the simulation results show that the proposed chaotic self-adaptive control parameter modified differential evolution algorithm has excellent convergence and stability,and it can effectively solve the single objective reactive power optimization problem with the object of active network loss.Establishing a reactive power optimization mathematical model with the object of active network loss minimum,voltage stability best and reactive power compensation investment minimum,Chaotic self-adaptive control parameter modified differential evolution algorithm is further improved to solve multi-objective optimization problems based on Pareto optimal theory.Based on the simulation analysis of IEEE-33 node distribution network system,the results verify the feasibility and validity of the proposed multi-objective optimization model and the improved algorithm.The Chaotic self-adaptive control parameter modified differential evolution algorithm proposed in this paper can provide a set of reactive power optimization schemes for decision-makers to choose flexibly according to their needs,and all optimization schemes can significantly improve the active power loss and voltage stability of the system.