Optimal Placement And Capacity Of Combined DG And SC Based On Modified Root Growth Algorithm

With the development of modern smart grid technology and the continuous increase of power demand,the scale of power system has become more and more large and complex.To ensure the safe,reliable and stable operation of power grid has always been the research goal of many scholars.Reasonable application of distributed generation technology in power distribution network can greatly improve power flow distribution,increase node voltage in power system,and reduce active power loss in the process of power transmission.The Optimal Allocation Distributed Generation(OADG)optimization problem refers to the optimization of one or several performance indexes of the power system under the premise of stable operation of the power grid by adjusting the installation location of Distributed Generation(DG)and selecting the optimal installed capacity and other variables.In this study,the Root Growth Algorithm(RGA)is used to solve the OADG problem,which overcomes the problems encountered in the traditional method.At first,the concepts related to OADG are introduced,and the necessary methods and mathematical models for power flow calculation are introduced.On this basis,combined with the special radial distribution of the distribution network,the mathematical model of optimal DG allocation of the radial distribution network is established,and then,the related formula derivation and proof are given.Then,the study describes the optimization principle and implementation process of the basic RGA algorithm,and gives the Modified Root Growth Algorithm(MRGA)to improve the search mechanism of the Algorithm itself.The root growth rate of RGA itself is adjusted to make the growth rate correlate with the current fitness value distribution,so as to achieve the purpose of real-time dynamic adjustment of root growth rate with the progress of the algorithm.Secondly,an Adaptive Modified Root Growth Algorithm(AMRGA)is proposed based on the characteristics of OADG problem.The network loss sensitivity factor is introduced,and the morphogen concentration in the growth process is affected by the network loss sensitivity factor of candidate nodes and the fitness value of candidate solutions,so as to achieve the objective of selective growth and improve the search efficiency.The candidate node parameters and optimal configuration capacity parameters are optimized by parts,and the local search ability of the algorithm is enhanced by controlling the root growth Angle.At the same time,the dimension of optimization space is reduced and the convergence speed is increased.At the same time,aiming at the irrationality of traditional penalty coefficient in dealing with OADG problems,a new Feasibility-prior Rule(FPR)was proposed in this study to deal with state variable constraints.By applying this Rule to the newly proposed AMRGA algorithm in this study,a new method for solving OADG problem is formed:Adaptive Modified Root Growth Algorithm with Feasibility-prior Rule(AMRGA-F).Finally,with the help of Matlab simulation software,this study carried out simulation experiments on three standard radial distribution network test systems(IEEE-33,IEEE-69,IEEE-119)of different sizes to verify the rationality and effectiveness of the improved algorithm.Taking the minimum network active power loss as the objective function,and the voltage level of system nodes and the system reactive power loss as the grid performance reference indexes,this study verified the superior performance of AMRGA-F algorithm by comparing the results of the algorithms in the references.At the same time,in order to verify that the proposed algorithm is also suitable for dealing with complex OADG problems,this study selects other representative intelligent algorithms and the original RGA algorithm to carry out 30 repeated independent experiments in the IEEE-119 system to achieve the purpose of performance comparison.The results show that AMRGA-F algorithm performs well in high search dimensions,with faster convergence speed,stronger global search ability and higher effective search efficiency.At the same time,AMRGA-F algorithm can not violate the constraints of state parameter equality and inequality on the premise of obtaining a better solution to the objective function.All the above proved that the new AMRGA-F algorithm proposed in this study to deal with OADG problem has a certain technical breakthrough.