Research On The Improved Differential Evolution Algorithm Of Active Power Optimization Of Power System

The optimal power flow problem considering the economy and security is one of the most important tools used in operation and planning for energy systems.For optimizing a specific objective function,the active power optimization problem based on OPF problem is aimed to adjust the settings of control variables,which include active power generation at PV buses except the slack bus,voltage magnitude at PV buses,transformer tap settings and shunt VAR compensation,and ensure that the system satisfies security constraints under such settings.In this paper,the differential evolution(DE)algorithm,which can overcome the shortcomings of traditional algorithms,is selected to solve the active power optimization problem.Firstly,the related concepts of optimal power flow and the solving model of power flow calculation are introduced,and the mathematical model of active power optimization is established.Then,the internal and external improvements are proposed based on the basic components of DE algorithm described previously.In the internal improvement,additional perturbation variables and the difference between outstanding individuals and other individuals are added to the mutation strategy according to the use of memory characteristic;and the parameter values of the scale factor F and crossover operator C_R are varied according to the characteristics of the optimization process,forming internal improved differential evolution(IDE)algorithm.The external improvement means that the proposed bystander mechanism is added to the DE algorithm,forming the external improved differential evolution(EDE)algorithm.All of the internal and external improvements are added to the DE algorithm to form a new Improved Differential Evolution(IEDE)algorithm.In order to verify the effectiveness of the improvement measures,five benchmark functions are introduced and used to test the performance of the following four algorithms:DE,IDE,EDE,and IEDE.Experimental results show that the IEDE algorithm is superior to other three algorithms.On the one hand,the comparison between IDE and DE algorithms reflects the efficiency of the internal improvement,and the comparison between EDE and DE algorithms reveals the effectiveness of the external improvement.On the other hand,the optimization results of IEDE algorithm show that the improvement measures improve the performance of DE algorithm from inside and outside.Next,the IEDE algorithm cannot directly solve the active power optimization problem due to the variable constraints handling problem of optimal power flow.Therefore,a new constraint handling strategy(NC)is proposed and combined with the IEDE algorithm to form the improved differential evolution algorithm with new constraint handling strategy(NC-IEDE).Meanwhile,the penalty function method of constraint handling is introduced and combined with the IEDE algorithm to form an improved differential evolution algorithm with penalty function(F-IEDE);the NC is combined with the DE algorithm to form a differential evolution algorithm with new constraint handling strategy(NC-DE).Finally,for verifying the superiority of NC and the effectiveness of the improvements,the NC-IEDE,F-IEDE and NC-DE algorithms proposed in this paper are respectively applied on MATLAB software for active power optimization simulation which includes 10 different cases.The simulation systems contain three systems with different sizes as follows:IEEE 30 buses,IEEE 57 buses and IEEE 118 buses test systems,and the optimal goals include 7 different objective functions.Experimental results reveal that the NC-IEDE algorithm can solve the active power optimization problem successfully and have higher efficiency,and NC can deal with the multi-constrained optimization problem better than the traditional penalty function method.Furthermore,compared with other methods reported in the recent literatures,the NC-IEDE algorithm also has superiority,indicating that the proposed algorithm is a breakthrough to some extent.