Hierarchical Partition-Based Many-Objective Day-Ahead Scheduling for Active Distribution Networks

Active Distribution Networks (ADNs) rely on the precise coordination of flexible resources to mitigate the stochasticity of high-penetration renewables. However, the hierarchical and partitioned nature of modern ADNs transforms the day-ahead scheduling problem into a high-dimensional many-objective optimization task, typically involving conflicting objectives across multiple regions. Standard evolutionary algorithms often struggle with the “curse of dimensionality” in such scenarios. To address this limitation, this study formulates a hierarchical partition-based scheduling model for many-objective optimization and introduces a novel adaptive MOEA/D algorithm. Specifically, a double-layer weight generation method and an adaptive neighborhood adjustment strategy are introduced to balance global search capability with local convergence speed. The methodology is validated using a practical 47-node ADN case study in Panzhihua, China. Comprehensive analysis of evaluation metrics (e.g., Hypervolume and IGD) indicates that the proposed algorithm achieves enhanced performance at the expense of a marginal increase in cost. Furthermore, it demonstrates strong competitiveness against advanced heuristic algorithms in solving high-dimensional scheduling problems, effectively balancing economic efficiency and voltage stability under renewable uncertainty.