Expert-guided optimization for load transfer in distribution networks assisted by virtual power plants

Abstract

The rapid expansion of distribution networks and the increasing complexity of their topological structures pose significant challenges to fast and reliable post-fault service restoration. Meanwhile, driven by carbon neutrality goals, the large-scale integration of distributed energy resources (DERs) enhances operational flexibility but also introduces pronounced intermittency and uncertainty, further complicating post-fault load transfer decision-making. To address these challenges, this paper proposes an expert-guided and virtual power plant (VPP)-assisted load transfer optimization framework based on hierarchical graph reinforcement learning. A topology-aware graph neural network (GNN)–based state representation is developed, in which buses are modeled as nodes and switches as controllable edges, enabling explicit modeling of network connectivity and electrical coupling. On this basis, a hierarchical decision-making architecture is constructed: the upper-level agent, guided by expert knowledge, dynamically selects the restoration task type to coordinate the timing of network reconfiguration and VPP-assisted DER regulation; driven by this high-level directive, two specialized lower-level agents respectively execute the specific switch operations and stepwise DER power adjustments, ensuring power balance and voltage security. Simulation results on a practical distribution network demonstrate that, under high DER penetration, the proposed method achieves faster service restoration, higher load recovery ratios, and significantly fewer voltage violation events than conventional reinforcement learning approaches, exhibiting improved operational safety and scheduling stability.