A Bionic-Intelligent Scheduling Algorithm for Distributed Power Generation System
With the introduction of the new power system concept, diversified distributed power generation systems, such as wind power, photovoltaics, and pumped storage, account for an increasing proportion of the energy supply side. Facing objective issues such as distributed energy decentralization and remote location, exploring what kind of algorithm to use to dispatch nearby distributed energy has become a hot spot in the current electric power field. In view of the current situation, this paper proposes a Bionic Intelligent Scheduling Algorithm (DWMFO) for distributed power generation systems. On the basis of the Moth Flame Algorithm (MFO), in order to solve the problem of low accuracy and slow convergence speed of the algorithm in scheduling distributed energy, we use the adaptive dynamic change factor strategy to dynamically adjust the weighting factor of the MFO. The purpose is to assist the power dispatching department to dispatch diversified distributed energy sources such as wind power, photovoltaics, and pumped storage in a timely manner during the peak power consumption period. In the experiment, we compared with 4 algorithms. The simulation results of 9 test functions show that the optimization performance of DWMFO is significantly improved, the convergence speed is faster, the solution accuracy is higher, and the global search capability is stronger. Experimental test results show that the proposed bionic intelligent scheduling algorithm can expand the effective search space of distributed energy. To a certain extent, the possibility of searching for the global optimal solution is also increased, and a better flame solution can be found.
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