Optimal Capacity Configuration of a Microgrid Based on an Improved Black-Winged Kite Algorithm

To ensure the power supply stability and economic efficiency of a hybrid AC/DC microgrid, it is essential to perform optimal capacity configuration of the hybrid energy storage system. In response to the limitations of traditional optimization algorithms, such as low convergence accuracy and a tendency to fall into local optima, this paper proposes an improved Black-Winged Kite Algorithm (IBKA) for the optimal configuration of hybrid energy storage systems. A power fluctuation penalty cost is incorporated into the objective function to enhance the rationality of capacity allocation. The Logistic chaotic map is employed to increase the diversity of the population during initialization. A nonlinear balancing factor is introduced to dynamically adjust the step size of the iterative search process. Additionally, a plane mirror-based opposition learning strategy is integrated to expand the algorithm’s search space. Simulation results demonstrate that IBKA achieves a 50.7% improvement in convergence speed compared to benchmark algorithms, exhibiting superior global search capability and faster convergence.