Research On Reactive Power Optimization Of Distribution Network With Distributed Generation

Reactive power optimization of distribution network is an important means to reduce network loss,improve power quality and improve power system stability.In recent years,Distributed generation(DG)has attracted more and more attention because of its green security,wide energy sources and high conversion efficiency.Under the background of intelligent distribution network,the combination of DG and traditional centralized generation will become the mainstream of power system in the future.DG grid-connected will affect power flow distribution,voltage offset and active power loss of distribution network.Therefore,it is of great theoretical significance and practical value to study the reactive power optimization of distribution network with distributed generation.Firstly,this paper expounds the characteristics of several common DGs,and qualitatively analyses the influence of DG grid connection on node voltage,active power loss and stability of distribution network.Then the mathematical models are established according to the different DG types,and they are equivalent to four types of nodes: PQ,PQ(V),PV and PI.Aiming at the problem that PV nodes can not directly calculate power flow,the flow of power flow calculation with DG is given.The simulation results show that the mathematical model of DG is effective,and DG can increase the voltage of system nodes and reduce the active power loss of distribution network.Secondly,in order to solve the problem of slow convergence speed and easy to fall into local optimum of existing multi-objective reactive power optimization algorithms,based on the standard differential evolution for multi-objective optimization(DEMO),chaos theory and co-evolution mechanism are introduced to increase the diversity of population and ensure that the algorithm is not easy to fall into local optimum.In order to improve the convergence speed of the algorithm,a strategy pool consisting of several commonly used evolutionary strategies is presented,which guides the population to approach the optimal evolutionary strategy through the individuals who have successfully evolved.In addition,the adaptive strategy of algorithm control parameters is introduced.The improved chaotic differential evolution for multi-objective optimization(ICDEMO)is applied to the test function ZDT1~ZDT4.The evaluation of convergence and uniformity shows that the algorithm has strong optimization ability.Finally,a multi-objective reactive power optimization model with active power loss and voltage deviation as objective functions is established.The effectiveness and practicability of ICDEMO algorithm are validated by using IEEEE30 bus system with DG and IEEE118 bus system,and the computational performance of the algorithm is better.Figure [26] table [6] reference [80].