Research On Optimal Power Flow Based On Modified Crisscross Optimization Algorithm

Optimal power flow(OPF)is one of the core and most important problems in the field of optimal dispatching of power grids.In response to this complex nonlinear problem that takes into account multiple operating constraints of large-scale transmission networks,swarm intelligence optimization algorithms often appear premature when optimizing them.In addition,since the optimization operation of swarm intelligence optimization algorithms need evaluate fitness repeatedly,and every fitness evaluation in the OPF problem is accompanied by time-consuming power flow calculations,which leads to a general longer optimization time and reduces the timeliness of final solution.The above two problems have become a major obstacle restricting the practical application of swarm intelligence optimization algorithms.To this end,this paper has performed a series of researches in OPF based on crisscross optimization algorithm(CSO).Aiming at the problem of premature convergence,this paper proposes the CSO based grey wolf optimizer(CS-GWO)to improve GWO’s ability of global convergence and avoiding to fall into the local optimum when solving OPF problem.The hunting operation in GWO is firstly modified by introducing a greedy mechanism and the horizontal crossover operator is added subsequently to refine the first three ranking wolves.The vertical crossover operator,as a mutation operator,added lastly not only guarantees the diversity of the population,but also has a chance to successfully get rid of the local optimum.The three operators execute alternately and perform their duties.The proposed CS-GWO is validated on IEEE 30-bus system and IEEE 118-bus system,the experimental results demonstrate that CS-GWO has obvious advantage over the original GWO and other state-of-art heuristic algorithms.Aiming at the problem of long time-consuming,this paper explores from two directions.Firstly,an improved method—faster crisscross optimization algorithm(FCSO)is proposed based on the point-to-point operation characteristics of horizontal crossover operator.In FCSO,a new operator—the central crossover operator is proposed,which alternates with horizontal crossover operator in a certain pattern.Every individual performs crossover operation with the current global optimal individual in turn and selectively move closer to the global optimal individual to improve the quality of each iteration and accelerate convergence.The simulation results on IEEE-118 bus system show that FCSO can significantly accelerate the convergence speed and greatly shorten the consuming time without losing the convergence accuracy.Secondly,the technology of Multi-Agent is introduced to construct a distributed computing platform(DCP).The computing task is distributed to each computing node.So,the computing power of multiple computers is used simultaneously to solve a single problem in parallel to shorten the calculation time.The simulation experiment is carried out on IEEE-300 bus system,the results under equal computing power prove the effectiveness of DSP,and the results under non-equal computing power show the unique advantage of CSO(i.e.non-global control)in the development of distributed computing.