Multi-objective nutcracker optimization algorithm based on fast non-dominated sorting and elite strategy for grid-connected hybrid microgrid system scheduling

The increasing demand for clean and sustainable energy has driven the development of hybrid microgrid systems that integrate multiple energy sources, offering a promising solution to mitigate climate change and environmental degradation. This paper presents an innovative approach to optimizing hybrid microgrid systems with flexible renewable energy configurations, tailored to address seasonal variations. A multi-objective optimization model is proposed to efficiently schedule the hybrid systems, minimizing operational costs while maximizing environmental benefits. To solve this complex optimization problem, we propose a novel multi-objective non-dominated sorting nutcracker optimization algorithm (NSNOA). In NSNOA, the fast non-dominated sorting method is used to rank the population to speed up its convergence and the crowding distance is utilized to preserve the population’s diversity. In addition, an elite strategy is employed to assist individuals in exploring better candidate solutions. The algorithm is validated through extensive testing on 12 benchmark functions, demonstrating superior accuracy and computational efficiency compared to existing optimization algorithms. Then, NSNOA is applied to optimize hybrid system scheduling, analyzing diverse scenarios and comparing results across multiple objectives. The experimental results indicate that the most optimal microgrid configuration based on PV/wind/turbine/diesel/battery achieves investment costs of 81,411.95 yuan in summer and 76,607.70 yuan in winter. The findings of this study support the viewpoint that the advancement and utilization of renewable energy can protect the environment and reduce operational costs.