Optimization model of power grid partitioning based on bionic joint algorithm
WU Guilian
LAI Sudan
NI Shiyuan
LI Yuange
HOU Siwei
Abstract：[Objective]Under the background of new power system construction,the randomness and fluctuation of the system are significantly exacerbated by the large-scale integration of high-proportion renewable energy and widespread popularity of flexible loads.Coupled with the continuously expanding power grid scale,control variables increase sharply,thus posing a severe challenge to the control strategies of traditional power grid voltage and tidal currents.The electrical distance between nodes is mainly relied on the existing power grid partitioning methods for reactive partitioning,which is difficult to adapt to the operation requirements for new power systems with drastic source-load changes.To this end,a power grid partitioning optimization method comprehensively considering multiple factors was proposed to lower the overall control difficulty of power grids under the penetration of high-proportion new energy and improve the autonomous operation capability of partitioning.[Methods]The core of this study is to build a set of partitioning index system and optimization model,and break through the traditional partitioning's limitation of only focusing on the topological association.Additionally,the tightness of internal electrical connections and the degree of source-load matching were creatively considered,with the reactive partitioning indexes based on electrical distance and active partitioning indexes based on source-load matching constructed respectively.On this basis,the optimization model of power grid partitioning was built to minimize the reactive partitioning index,thus aiming to maximize the electrical tightness inside the partitioning and simplify reactive power and voltage control.Meanwhile,the key constraint that the active partitioning indexes satisfied the requirements was employed to limit the frequent interaction of active power between partitions,reduce the violent fluctuation of net loads within partitions,and ensure the source-load balance within partitions.At the same time,a bionic joint optimization algorithm was proposed,in which the global search ability of genetic algorithms and fast local refinement ability of firefly algorithms were fully used to efficiently solve the built nonlinear complex optimization model,improve the optimization speed,and avoid falling into the local optimal solutions.[Results]The standard IEEE 39-node system was adopted to verify the case example.The simulation results show that by adopting this algorithm,the source-load matching degree within partitions can be significantly improved,the fluctuation of net loads between and within partitions can be reduced,and unnecessary tidal current interaction can be decreased.Additionally,the difficulty of reactive control in the system can be lowered,the electrical tightness of nodes within the partitions can be enhanced,and the voltage and reactive regulation process within the partitions can be simplified by employing the algorithm.The proposed firefly-genetic bionic joint optimization algorithm exhibits excellent solution performance and can obtain the optimized partitioning scheme rapidly and effectively.[Conclusions]There are two main innovative points in this study.Firstly,the optimization objective of reactive control based on electrical distance and the constraint of active balance based on source-load matching are integrated in the power grid partitioning model,which overcomes the defect of insufficient adaptability of traditional methods to source-load changes.Secondly,an efficient and robust firefly-genetic bionic joint optimization algorithm was proposed to solve the partitioning model,thus effectively improving the optimization speed and accuracy.This algorithm provides a new technical way to solve the problem of partitioning operation control under the complex network structure of new power systems,and holds theoretical and practical significance for improving the safe and stable operation of power grids and promoting efficient consumption of new energy.
Keywords：power grid partitionelectrical distancesource-load matchingfirefly-genetic bionic joint algorithmreactive partitioningactive partitioningsensitivityNewton-Raphson tidal current
Publication Date：2026-01-25
Online Publishing Date：2026-03-17(First online date of this platform, not the publication date of the document)
Pages：9( 37-45 )