Microgrid Capacity Configuration Optimization Based on Multi-population Cooperative Grey Wolf Algorithm

With the rapid development of renewable energy, independent microgrids integrating distributed energy sources such as wind and solar power have become a research focus due to their excellent cost-effectiveness and energy efficiency. In microgrid design, optimal capacity allocation is crucial for improving resource utilization and reducing operational costs. Grey Wolf Optimization (GWO) algorithms which show great potential in solving optimal allocation problems are prone to early convergence and local optima. To address these issues, this study proposes four strategies to enhance global search capability and population diversity: (1) multi-population strategy to increase diversity; (2) t-distribution mutation to disturb optimal individuals, improving early global search and late-stage local optimization, thereby accelerating convergence; (3) hybrid operation to introduce diversity and prevent local convergence; and (4) replacement strategy to maintain population diversity and exploration ability. Taking the optimal configuration of a wind-solar-diesel-storage microgrid as an example, experimental results show that the multi-population collaborative GWO algorithm reduces annual costs by 3,720 RMB, 10,470 RMB, and 57,350 RMB compared to traditional GWO, PSO, and DE algorithms, respectively. These results demonstrate the superiority of the proposed algorithm in microgrid optimization, highlighting its advantages in enhancing global optimization capabilities and reducing operational costs.