Data-driven Under Frequency Load Shedding Using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based approach for underfrequency load shedding that uses a machine learning classifier to reduce training time and improve real-time response in power system stability.

Contribution

It presents a novel RL training method that incorporates a classifier to efficiently model system response, enhancing UFLS performance and computational efficiency.

Findings

RL-based UFLS outperforms traditional schemes in stability and load shedding efficiency.

The classifier reduces training time and enables faster real-time application.

Simulation on IEEE 68-bus system validates improved performance.

Abstract

Underfrequency load shedding (UFLS) is a critical control strategy in power systems aimed at maintaining system stability and preventing blackouts during severe frequency drops. Traditional UFLS schemes often rely on predefined rules and thresholds, which may not adapt effectively to the dynamic and complex nature of modern power grids. Reinforcement learning (RL) methods have been proposed to effectively handle the UFLS problem. However, training these RL agents is computationally burdensome due to solving multiple differential equations at each step of training. This computational burden also limits the effectiveness of the RL agents for use in real-time. To reduce the computational burden, a machine learning (ML) classifier is trained to capture the frequency response of the system to various disturbances. The RL agent is then trained using the classifier, thus avoiding multiple computations during each step of agent training. Key features of this approach include reduced training time, as well as faster real-time application compared to other RL agents, and its potential to improve system resilience by minimizing the amount of load shed while effectively stabilizing the frequency. Comparative studies with conventional UFLS schemes demonstrate that the RL-based strategy achieves superior performance while significantly reducing the time required. Simulation results on the IEEE 68-bus system validate the performance of the proposed RL method.