Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

TL;DR

This paper introduces a deep meta reinforcement learning approach for grid emergency control that can quickly adapt to changing conditions, improving reliability and security in power systems with uncertainties.

Contribution

The paper develops a novel DMRL algorithm that combines meta strategy optimization with DRL, enabling rapid adaptation to new grid scenarios.

Findings

Demonstrates fast adaptation of policies to new conditions

Achieves superior performance over existing DRL and MPC methods

Validates effectiveness on IEEE 300-bus system

Abstract

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method and achieve superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.