Planned Island Formation for Distribution Networks with High Penetration of Distributed Energy Resources Using Graph Attention Network and Multi-Layer Perceptron

Abstract

High penetration of distributed energy resources (DERs) creates opportunities for planned island formation and operation in distribution networks, which can help mitigate voltage fluctuations, reduce reverse power flow, and improve renewable energy utilization. This paper proposes a rapid planned island formation method based on a graph attention network (GAT) and a multi-layer perceptron (MLP). First, considering the technical operation requirements of distribution networks, the factors affecting island formation, including topological connectivity, power balance, and DERs, are analyzed. Based on graph theory, the topology connections and node information of the distribution network are represented by an adjacency matrix, a node feature matrix, and an edge physical feature matrix, respectively, to construct the distribution network graph model. Second, the GAT is employed to learn the correlations among distribution network nodes and obtain a node feature matrix incorporating relational information. The obtained representations are fed into the MLP to learn the mapping between node representations and branch connectivity states, and an island partitioning scheme is generated based on the output probabilities. Finally, considering the power balance constraints of the distribution network, the boundary nodes of the islands are adjusted to obtain the final feasible island partitioning scheme. Simulation results on a modified IEEE 33-bus distribution system show that the proposed method achieves an F1 of 92.37%. Furthermore, the model maintains an average F1 of 91.32% in generalization tests, demonstrating its effectiveness and robustness for rapid planned island formation in distribution networks with high penetration of DERs.