Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems

TL;DR

This paper introduces a hybrid imitation learning framework for real-time service restoration in resilient distribution systems, enabling efficient decision-making under complex $N-k$ scenarios by learning from expert policies.

Contribution

It proposes a novel hybrid policy network for discrete-continuous actions and applies imitation learning to improve training efficiency in complex distribution system restoration tasks.

Findings

Effective restoration policy learned from expert demonstrations

Improved training efficiency over traditional reinforcement learning

Validated on a 33-node system under $N-k$ disturbances

Abstract

Self-healing capability is one of the most critical factors for a resilient distribution system, which requires intelligent agents to automatically perform restorative actions online, including network reconfiguration and reactive power dispatch. These agents should be equipped with a predesigned decision policy to meet real-time requirements and handle highly complex $N-k$ scenarios. The disturbance randomness hampers the application of exploration-dominant algorithms like traditional reinforcement learning (RL), and the agent training problem under $N-k$ scenarios has not been thoroughly solved. In this paper, we propose the imitation learning (IL) framework to train such policies, where the agent will interact with an expert to learn its optimal policy, and therefore significantly improve the training efficiency compared with the RL methods. To handle tie-line operations and reactive power dispatch simultaneously, we design a hybrid policy network for such a discrete-continuous hybrid action space. We employ the 33-node system under $N-k$ disturbances to verify the proposed framework.