Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

Abstract

The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production. As a consequence, active grid management is reaching its limits with conventional approaches. In the context of the Learning to Run a Power Network challenge, it has been shown that Reinforcement Learning (RL) is an efficient and reliable approach with considerable potential for automatic grid operation. In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach. The main improvement is a N-1 strategy, where we consider topology actions that keep the grid stable, even if one line is disconnected. More, we also propose a topology reversion to the original grid, which proved to be beneficial. The improvements are tested against reference approaches on the challenge test sets and are able to increase the performance of the rule-based agent by 27%. In direct comparison between rule-based and RL agent we find similar performance. However, the RL agent has a clear computational advantage. We also analyse the behaviour in an exemplary case in more detail to provide additional insights. Here, we observe that through the N-1 strategy, the actions of the agents become more diversified.

12 pages plus references and appendix, 11 figures, submitted to the journal Energy and AI

References in corpus (4)

• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
• Population Based Training of Neural Networks
• Learning to run a power network challenge for training topology controllers
• Power Grid Congestion Management via Topology Optimization with AlphaZero

Cited by in corpus (4)

• Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
• HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
• Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach
• Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control