Research On Reactive Power Optimization Of Distribution Network With DG Based On Improved Artificial Bee Colony Algorithm

The access of DG into the distribution network can effectively alleviate the problem of energy shortage in the main form of generation where thermal power takes the large part and reduce environmental pollution.However,the access of DG will change the distribution of the power flow of the distribution network and thereby affect the operation mode of the distribution network.Therefore the voltage of the distribution network is closely related to the reactive power in the system,the optimization of the reactive power in the distribution network is an important means to improve the power quality and ensure the safe and stable operation of the system.One of the important parts in the optimization process is the algorithm.In recent years,the research on intelligent algorithms attracted researchers from home and abroad.Synthesizing the above two problems,this paper studies the reactive power optimization of distribution networks with DG.The main work is as follows:1)The research status of several common DG types is analysed,and explain the characteristics of various types,which are equivalent to four node types of PQ,PV,PI,and PQ(V),and establish corresponding models for power flow calculation Based on the established DG calculation model,a forward calculation method is used to compile a flow calculation program containing DG.Then perform power flow calculation on the IEEE33 node system with DG,and analyze the impact of DG grid connection on the distribution network.The calculation results show that when the DG access location is different,the DG access type is different,and the DG access capacity is different.The influence of the direction and size of the power flow are different,and thus the impact on the safe and stable operation of the distribution network,is also different.2)Brief introduce and analysis of the basic concepts.Implementation steps and advantages and disadvantages of the standard ABC algorithm is stated.Introduce a reverse learning strategy during the initialization phase of the algorithm to improve the quality of the initial population and enhance the search efficiency of the algorithm.The cross mechanism is used to cross the current global optimal solution in the process of searching for the solution,so that the algorithm can quickly bias the optimal solution and speed up the convergence speed.In the surveillance bee stage of the algorithm,the taboo search algorithm is used ideas to enhance algorithm development capabilities.3)Analyzed the reactive power adjustment measures of the distribution network,explain the applicable scope and limitations of each measure,and then establish a single-objective reactive power optimization model with the smallest network loss The target reactive power optimization model is calculated using the standard ABC algorithm and the improved ABC algorithm in the improved IEEE33 node test system with DG.The calculation results verify that the reactive power optimization algorithm proposed in this paper is not only applicable to the single-objective optimization model,but also applicable based on the multi-objective optimization model,the feasibility and effectiveness of the reactive power optimization model and the improved ABC algorithm proposed in this paper are verified.