Research on Optimization Scheduling of Microgrid Based on Improved Moss Algorithm

This paper proposes a comparative strategy for seasonal optimization scheduling of microgrids, based on an improved moss optimization algorithm, to address the operational costs and seasonal electricity consumption issues of microgrids. Construct a comparative model for optimizing microgrid scheduling based on the generation characteristics and seasonal influencing factors of each power generation unit in the microgrid. To tackle the challenges posed by an uneven starting distribution of individuals and limited global exploration capacity in the moss optimization algorithm, we incorporate the optimal Latin hypercube method for population initialization, and enhance the search mechanism through the application of t-distribution perturbation mutation. The case analysis shows that the improved moss optimization algorithm has better optimality, convergence, and stability than the original moss optimization algorithm. The improved moss optimization algorithm can significantly reduce the operating costs of microgrids when the output of each micro power source meets its working characteristics.