Application research of multi-strategy snake optimization algorithm in hybrid energy storage configuration
A hybrid energy storage system(HESS) consisting of batteries and supercapacitors effectively mitigates the impact of wind power integration on the grid.To address the difficulty in selecting parameter values K and α in Variational Mode Decomposition (VMD),this paper proposes Multi-Strategy Optimization Algorithm (MISOA) to optimize the parameters,so as to accurately achieve a single allocation of HESS power.HESS consists of two or more energy storage technologies of different structures with matching characteristics.By combining the power outputs of different energy storage technologies,it is possible to achieve complementary advantages of different energy storage technologies,effectively expand the advantage range provided by a single energy storage technology,enhance the working performance of the energy storage system,and reduce the research costs for fundamental development of the storage mechanisms.This paper mainly studies the power allocation strategy of the HESS,introduces the basic principles of the variational mode decomposition and points out a snake optimization algorithm (SOA) to optimize the parameters of VMD on the basis of objective function of minimizing the entropy envelope.But the traditional SOA has a slow convergence rate and is prone to falling into a local optimal solution.Three optimization methods are employed to ensure the algorithm maintains good global search capabilities during the iterative process and its convergence ability is improved.First,reverse difference after initialization population variation is used to increase the population diversity.Then,through the position update formula of the fusion subtraction average optimization algorithm (SABOA),the global search capability of the algorithm is improved.The final stage is putting up the strategy of survival of the fittest to avoid prematurity.Based on SOA optimization,we propose the multi-strategy optimization algorithm (MISOA) to optimize the parameters and obtain the optimal parameter combination.HESS target power after the decomposition of each modal component Hilbert transform,regarding the modal mixing degree of the least mixed modal point as the boundary point of the high and low frequency power,thus completing the primary allocation of target power of the HESS.The battery-supercapacitor hybrid energy storage system and the data of a 22 MW wind farm in northwest China are taken as the research object and basis.Our results show compared with SOA,MISOA-VMD enhances the convergence speed by 70％ and improves the convergence accuracy;compared with Empirical Mode Decomposition (EMD),the mode aliasing phenomenon is reduced,thus verifying the effectiveness of our strategy.
MISOAvariational mode decompositionhybrid energy storage systemparameter optimizationpower distribution
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