A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm.

Microgrids possess robust self-operation and management capabilities, enabling effective utilization of renewable energy sources and enhancing the reliability and security of power supply within power systems. However, challenges such as low economic benefits and inefficient operation have long plagued microgrids. To address these issues, this paper presents a microgrid scheduling strategy based on the Non-Dominated Sorting Dung Beetle Optimization Algorithm (NSDBO). By incorporating the non-dominated sorting mechanism into the dung beetle optimization algorithm, the solution set is divided into different tiers. Solutions within each tier demonstrate superior performance compared to those in other tiers. Moreover, when tackling multi-objective optimization problems, this approach effectively avoids falling into local optima and significantly enhances the algorithm's global search capability. Experimental results reveal that, in comparison to other algorithms, the proposed NSDBO algorithm outperforms them in terms of hypervolume (HV) and generational distance (GD), indicating its advantages in both convergence and diversity. Specifically, compared with the Grey Wolf Optimizer (GWO) and the standard Dung Beetle Optimization (DBO) algorithm, the overall operation cost of NSDBO is reduced by 52% and 8.1%, respectively. These findings fully demonstrate the effectiveness of the proposed algorithm in improving users' economic benefits and reducing environmental pollution.