Address Selection And Capacity Determination Of Distributed Power Sources Based On Group Intelligence Algorithm

With the continuous development of power systems,traditional centralized power generation grids are increasingly failing to meet people's expectation.Simultaneously,Distributed Generation(DG)technology has emerged in the new arena of power systems and has flourished.However,to some extent,the access of the DG to the distribution network will increase the complexity of the power system.For example,it will have a large impact on voltage stability,line current,network loss,relay protection characteristics,and harmonic problem.In addition,there are differences in the equivalent models of various types of DGs of different node types.Therefore,in order to minimize the adverse effects of DG access on the distribution network,it is particularly important to scientifically plan the type,location,and capacity of DG access.As a kind of algorithm,Group intelligence simulates group activities in the biological community.This kind of algorithm has simple principle,less parameter setting and better optimization effect.Thus it is suitable for solving the complex optimization problem of location selection and capacity determination.The whale algorithm is simple in principle,and the genetic algorithm can search quickly in the early stage of algorithm optimization and can jump out of the local optimal solution with a large probability.This paper proposes a whale genetic hybrid algorithm based on the advantages of these two algorithms.Finally,this paper constructs a mathematical optimization model for the location selection and capacity determination of DGs.The objective function contains both energy and economic indicators,they are distribution network loss and DG operating costs.In addition,this paper establishes a multi-objective function by considering the voltage deviation and the network loss of the distribution network.For the above objective function,several restrictions such as access nodes of the distribution network are established.In order to verify the validity of the algorithm and model in the paper,the verification is carried out in the IEEE33 node distribution network.The simulation results show that reasonable distributed power location and constant capacity can effectively reduce the loss and cost of the distribution network and greatly improve the power quality.