A Bionic-Intelligent Scheduling Algorithm for Distributed Power Generation System

Abstract

With the introduction of the new power system concept, diversified dis-tributed power generation systems, such as wind power, photovoltaics, andpumped storage, account for an increasing proportion of the energy supplyside. Facing objective issues such as distributed energy decentralization andremote location, exploring what kind of algorithm to use to dispatch nearbydistributed energy has become a hot spot in the current electric power field. Inview of the current situation, this paper proposes a Bionic Intelligent Schedul-ing Algorithm (DWMFO) for distributed power generation systems. On thebasis of the Moth Flame Algorithm (MFO), in order to solve the problemof low accuracy and slow convergence speed of the algorithm in schedulingdistributed energy, we use the adaptive dynamic change factor strategy todynamically adjust the weighting factor of the MFO. The purpose is to assistthe power dispatching department to dispatch diversified distributed energysources such as wind power, photovoltaics, and pumped storage in a timelymanner during the peak power consumption period. In the experiment, wecompared with 4 algorithms. The simulation results of 9 test functions showthat the optimization performance of DWMFO is significantly improved, theconvergence speed is faster, the solution accuracy is higher, and the global search capability is stronger. Experimental test results show that the proposedbionic intelligent scheduling algorithm can expand the effective search spaceof distributed energy. To a certain extent, the possibility of searching for theglobal optimal solution is also increased, and a better flame solution can befound.