Adaptive hierarchical learning for uncertainty-aware distributed energy resource planning.

ABSTRACT: The proliferation of distributed energy resources introduces multi-source uncertainties, including implicit uncertainties arising from third-party operators' partial observability of security constraints, challenging traditional distribution network planning methods dependent on model simplification and predefined scenarios. We address this gap via an adaptive hierarchical learning architecture that co-optimizes distributed energy resources location, capacity, and operational strategies data-drivenly, enabling autonomous learning of implicit constraints without full model knowledge. Our framework embeds a bi-level Stackelberg structure where Monte Carlo Tree Search autonomously generates planning schemes at the upper level, while multi-agent reinforcement learning directly learns operation

SUBMITTER: Xiang Y 

PROVIDER: S-EPMC12932729 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature