A Bionic-Intelligent Scheduling Algorithm for Distributed Power Generation System
DOI:
https://doi.org/10.13052/dgaej2156-3306.3727Keywords:
DWMFO, dynamic adjustment, bionic-intelligent, adaptive, distributed energy.Abstract
With the introduction of the new power system concept, diversified dis-
tributed 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 Schedul-
ing 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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