Safe Reinforcement Learning for Emergency LoadShedding of Power Systems

TL;DR

This paper proposes a safe reinforcement learning approach for emergency load shedding in power systems, improving grid safety and resilience amid uncertainties and rapid changes.

Contribution

It introduces a novel safe RL method specifically designed for load shedding, enhancing safety guarantees during grid emergency control.

Findings

Effective voltage recovery demonstrated in simulations

Adaptive to unseen faults in the IEEE benchmark

Outperforms traditional control methods

Abstract

The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid's security and resilience. In particular, the increased uncertainties and rapidly changing operational conditions in power systems have revealed outstanding issues in terms of either speed, adaptiveness, or scalability of the existing control methods for power systems. On the other hand, the availability of massive real-time data can provide a clearer picture of what is happening in the grid. Recently, deep reinforcement learning(RL) has been regarded and adopted as a promising approach leveraging massive data for fast and adaptive grid control. However, like most existing machine learning (ML)-basedcontrol techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel method for safe RL-based load shedding of power systems that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.