Microgrid optimal dispatching has become one of the core issues of microgrid energy management and integrated control, which is of great significance to reduce energy consumption and environmental pollution. As a natural heuristic algorithm, the butterfly optimization algorithm (BOA) has the advantages of simple adjustment parameters and fast convergence speed. It is widely used to solve nonlinear programming problems. However, BOA is easy to fall into local optimization and poor convergence accuracy. Therefore, an improved butterfly optimization algorithm (IBOA) based on skew tent chaotic map, Cauchy mutation, and simplex method is proposed, and compared with particle swarm optimization (PSO), whale optimization algorithm (WOA), sparrow search algorithm (SSA), and BOA, the results show that the IBOA has high convergence speed and optimization accuracy. Finally, the IBOA is used to solve the optimization model. The simulation results show that the IBOA can effectively reduce the power consumption cost of the system, promote the effective utilization of renewable energy, and improve the operation stability of the microgrid cluster system.

In nature, the variation of wind speed is characterized by randomness, fluctuation, and intermittence. In order to suppress the power fluctuation caused by wind speed changes in the process of wind turbine grid connection, a wind power smooth grid-connected control strategy based on the adaptive variational modal decomposition algorithm and the hybrid energy storage system is proposed. For the problem that the selection of variational modal decomposition parameters is subjective and experiential, which leads to the poor signal decomposition reduction degree, the sparrow search algorithm is proposed to optimize variational modal decomposition to realize the adaptive selection of key variational modal decomposition parameters k and α. First, the reference power of the hybrid energy storage system conforming to the grid-connected power fluctuation standard is obtained by the adaptive moving average algorithm. Then, adaptive variational modal decomposition of the reference power was performed to obtain a set of inherent modal functional components, and the low frequency and high frequency components of the modal components were allocated for lithium batteries and the supercapacitor, respectively. Finally, Matlab/Simulink was used to simulate and compare with the control strategy of low-pass filtering. The results show that the proposed algorithm realizes the selection of adaptive decomposition parameters of variational modal decomposition, solves the lag and frequency aliasing problems existing in power distribution of the low-pass filtering algorithm, and realizes the high reduction solution of reference power. The fluctuation of wind power connected to the grid is effectively suppressed.

Aiming at the problems of system response lag, poor anti-interference ability, and poor recovery ability of fuzzy dual closed-loop control, this paper proposes a fuzzy dual closed-loop control based on a whale optimization algorithm to optimize the control effect by simulating the foam attack mechanism of humpback whales, solving mathematical and structural optimization problems, adjusting quantization factor and scaling factor, and adjusting PI parameters in real-time. Matlab/Simulink is used to create the model. The experimental results show that the system has a quicker reaction, smaller overshoot, greater anti-interference capability, and improved robustness in various situations under the new control strategy.