Research On Optimal Power Flow Of Power Grid Based On Moth-flame Optimization Algorithm

The optimal power flow(OPF)problem is a vital decision-making method for the economic indexes such as power generation cost,real-time electricity price or power loss in power system,on which basis the safety and stability are considered as well.With the rapid development of the economy and the continuous progress of society,heuristic intelligent optimization(HIO)algorithms have been developed to solve related optimal problems such as parameter optimization and system performance enhancement in power system.Because of the easy programming,the robust parallel search capability and the flexibility under complex constraints,it can obtain the global optimal solution effectively instead of falling into the local optimal solution in the OPF problem.The biological background of the MFO algorithm is introduced,and on this basis,the basic mathematic model of the algorithm is established.According to the detailed description of the solution process,the optimal mechanism of the MFO algorithm is revealed.By analyzing the results of unimodal function and multi-modal function,it shows that MFO algorithm has particular advantages in dealing with complex and non-convex optimization problems,which guarantees a desirable convergence performance.This paper shows that MFO algorithm can avoid the optimization algorithm from falling into the local optimal solution more effectively comparing with different algorithms.The exploration ability and the exploitation ability of the optimization process could be well balanced by MFO algorithm,which provides a useful approach for industrial applications of OPF problems.To solve the problem of OPF in power system,this paper proposes an MFO-based optimal solution scheme.In the scheme,the generation cost and its weighted sum with active power loss or node voltage deviation or valve point are acted as the objective functions of the optimization problem,respectively.The complex constraints in OPF are taken into account.The simulation results indicate that MFO algorithm has the advantages of efficient convergence speed,high search precision and strong robustness in solving the OPF problem which is high-dimensional,nonlinear and non-convex.It is worth pointing out that the MFO algorithm does not require too much on the initial value,while the number of moths should be selected moderately in the optimization process,or it will reduce the performance of the algorithm.A probability optimal power flow(POPF)model considering the random factors of the load power and wind power and the correlation is proposed.The probabilistic model based on the Latin hypercube sampling is combined with the important sampling method.And the correlation factors of the input variables are considered by its ranking process.The simulation results verify the distinct advantages on the robustness and global optimization abilities of MFO algorithm under the influence of random variables in POPF problem.If wind farms are connected to the power system,the random and the correlation of wind power will increase the uncertainty of the power system which will enforce the fluctuation of the system.Therefore,random and correlation factors should be considered in modern power systems.