Synchronization-Safe Dynamic Microgrid Formation for DER-Led Distribution System Restoration With Constraint-Aware Graph Learning

TL;DR

This paper introduces a novel synchronization-safe microgrid formation framework for distribution system restoration, leveraging graph learning and neural networks to improve speed and safety during blackouts.

Contribution

It develops a constraint-aware graph learning approach and a spatio-temporal graph convolutional network to enhance solution efficiency and ensure synchronization safety in microgrid restoration.

Findings

Ensures synchronization-safe microgrid formation during restoration.

Achieves significant computational speed-ups without losing optimality.

Validates effectiveness through case studies on IEEE 123-node feeder.

Abstract

Prolonged blackouts in distribution systems (DSs) with high penetration of distributed energy resources (DERs) necessitate novel restoration strategies to rapidly restore loads. However, the resulting complex optimization problem significantly limits scalability. This paper proposes a synchronization-safe dynamic microgrid (MG) formation (SSDMGF)-enabled restoration framework, in which a constraint-aware graph learning approach is developed to enhance solution efficiency. To characterize the restoration status of systems with evolving boundaries, the concepts of system mode and system class are defined. To ensure synchronization safety during restoration, the transitions of system mode and class for dynamically formed MGs are explicitly restricted. To further accelerate the solution process, a constraint-aware spatio-temporal graph convolutional network (STGCN) is designed to partially generate high-quality warm-start solutions, where synchronization-related constraints are embedded into a differentiable feasibility-resolving layer based on the straight-through estimator (STE). Case studies on a modified IEEE 123-node feeder validate that the proposed method ensures synchronization-safe MG formation and improves restoration performance. Meanwhile, the proposed acceleration framework achieves significant computational speed-ups without compromising final optimality.