Distributed hybrid energy storage photovoltaic microgrid control based on MPPT algorithm and equilibrium control strategy

With the rapid advancement of the new energy transformation process, the stability of photovoltaic microgrid output is particularly important. However, current photovoltaic microgrids suffer from unstable output and power fluctuations. To improve the stability and system controllability of photovoltaic microgrid output, this study constructs an optimized grey wolf optimization algorithm. Using the idea of small step perturbation, it is applied to the maximum power point tracking solar controller to construct a maximum power point controller algorithm based on the improved algorithm. Secondly, the algorithm is combined with photovoltaic arrays to construct a maximum tracking point control system for photovoltaic arrays based on the algorithm. Finally, the system is combined with low-pass filtering power allocation and secondary power allocation strategies, as well as a hybrid storage system, to construct a photovoltaic microgrid control model. In the performance comparison analysis of the research algorithm, the average accuracy and average loss value of the algorithm were 98.2% and 0.15, respectively, which were significantly better than the compared algorithms. The performance analysis of the photovoltaic microgrid control model showed that the model could effectively regulate and control the output power of the microgrid under two operating conditions, demonstrating its effectiveness. The above results indicate that The proposed algorithm and the improved algorithm of the PV microgrid control model can not only improve the steady-state tracking accuracy, but also have better dynamic performance and improve the tracking speed. The control strategy can maintain the operational stability of the microgrid system and realize the smooth switching control of each mode, meeting the stability and flexibility requirements of the PV microgrid system. The novelty of this study is that the improved Grey Wolf optimization algorithm enhances the global search ability by introducing the random jump mechanism of Levy flight algorithm and the combination of particle swarm optimization algorithm and Grey Wolf optimization algorithm to avoid falling into the local optimal. The randomness and ergodicity of Levy flight algorithm enable the hybrid algorithm to quickly adapt to the changes of light intensity and environmental conditions, and maintain the efficient operation of MPPT. Moreover, particle swarm optimization has strong local search ability, and gray Wolf optimization improves local search accuracy. The combination of the two improves local search accuracy. By combining the characteristics of Levy flight algorithm, the parameters of PSO and GWO algorithm, such as inertia weight and convergence factor, are dynamically adjusted to adapt to different working conditions of MPPT. The optimal solution is output as the optimal strategy of MPPT through collaboration. The potential practical impact is that the improved MPPT control strategy can track the maximum power point more effectively, improve the efficiency and stability of the photovoltaic power generation system, reduce energy waste by improving the tracking accuracy and convergence speed of the photovoltaic system, and improve the robustness of the system.