Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights

TL;DR

This paper evaluates the practical application of deep reinforcement learning for voltage control in real-world power systems, highlighting challenges, limitations, and insights from experiments on multiple grid models.

Contribution

It provides a comprehensive assessment of DRL's performance in actual power system operations and identifies key bottlenecks and areas for technological advancement.

Findings

DRL shows promise but faces performance challenges in real-world systems.

Identifies specific bottlenecks in applying DRL to power grid control.

Highlights the need for advancing AI to handle modern power system complexities.

Abstract

Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL's performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL's effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability.