Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges

2025-04-11

AI Summary

Addressing the dynamic and uncertain challenges of power grid topology optimization under high renewable energy penetration, this paper presents a systematic review of reinforcement learning (RL) applications in power system control. It provides the first structured taxonomy of RL-based grid control research driven by the Learning to Run a Power Network (L2RPN) competition. The study analyzes mainstream algorithms—including PPO, DQN, and A3C—alongside topology action encoding schemes, multi-objective reward design, and evaluation paradigms, elucidating their performance limits in stability, convergence, and real-time responsiveness. Innovatively, we propose a standardized comparative experimental framework, identifying critical bottlenecks: poor generalization across grid topologies and difficulty embedding hard security constraints. The work establishes a methodological foundation and technical roadmap for developing verifiable, deployable next-generation RL-based smart grid controllers.

Technology Category

• Machine Learning: Reinforcement Learning
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Abstract

Power grid operation is becoming increasingly complex due to the rising integration of renewable energy sources and the need for more adaptive control strategies. Reinforcement Learning (RL) has emerged as a promising approach to power network control (PNC), offering the potential to enhance decision-making in dynamic and uncertain environments. The Learning To Run a Power Network (L2RPN) competitions have played a key role in accelerating research by providing standardized benchmarks and problem formulations, leading to rapid advancements in RL-based methods. This survey provides a comprehensive and structured overview of RL applications for power grid topology optimization, categorizing existing techniques, highlighting key design choices, and identifying gaps in current research. Additionally, we present a comparative numerical study evaluating the impact of commonly applied RL-based methods, offering insights into their practical effectiveness. By consolidating existing research and outlining open challenges, this survey aims to provide a foundation for future advancements in RL-driven power grid optimization.

Problem

• Optimizing power grid topologies using reinforcement learning methods
• Addressing challenges in dynamic power network control with renewables
• Evaluating RL-based solutions for practical grid optimization effectiveness

Innovation

• Reinforcement Learning for power grid control
• L2RPN competitions standardize RL benchmarks
• Comparative study evaluates RL methods