The new energy photovoltaic Direct Current (DC) microgrid system is highly susceptible to environmental factors, leading to substantial nonlinearity and fluctuations in its output power.This situation severely constrains its stable operation and energy utilization efficiency in complex application scenarios.Consequently, this study initially enhances the traditional lion swarm algorithm by employing Tent mapping and dynamic probability factors to efficiently track the maximum power point of new energy DC microgrids.Subsequently, by integrating the Grey Wolf Optimizer algorithm with variational mode decomposition, frequency domain decomposition of the output power of new energy photovoltaics is conducted, ultimately attaining precise energy-storage control and dynamic compensation for highand low-frequency power.The results indicate that under standard lighting, local occlusion, and dynamic change conditions, the research algorithm can achieve maximum power point tracking within 0.04 s, 0.064 s, and 0.35 s, respectively, reaching power peaks of 859.2 W, 581.6 W, and 614.9 W. The tracking accuracy is improved to 99.91%, 99.91%, and 99.89%.Compared with other algorithms, the research algorithm exhibits significant advantages in response speed, dynamic stability, and oscillation control.Moreover, after implementing the research method, the number of charge-discharge switching times of the battery pack is notably reduced to only 22 times.In conclusion, the research method not only shows superiority in maximum power point tracking performance but also realizes efficient integration and allocation of multi-source fluctuating power in energy management, presenting favorable practical prospects for promotion.
Meng et al. (Thu,) studied this question.