Hierarchical Reinforcement Learning for Power Network Topology Control

1 code implementation • 3 Nov 2023 • Blazej Manczak, Jan Viebahn, Herke van Hoof

Whereas at the highest level a purely rule-based policy is still chosen for all agents in this study, at the intermediate level the policy is trained using different state-of-the-art RL algorithms.

Hierarchical Reinforcement Learning reinforcement-learning +1

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

1 code implementation • 3 Apr 2023 • Malte Lehna, Jan Viebahn, Christoph Scholz, Antoine Marot, Sven Tomforde

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.

Management Reinforcement Learning (RL)

Learning to run a Power Network Challenge: a Retrospective Analysis

no code implementations • 2 Mar 2021 • Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold, Adrian Kelly, Aïdan O'Sullivan, Jan Viebahn, Mariette Awad, Isabelle Guyon, Patrick Panciatici, Camilo Romero

Motivated to investigate the potential of AI methods in enabling adaptability in power network operation, we have designed a L2RPN challenge to encourage the development of reinforcement learning solutions to key problems present in the next-generation power networks.

Exploring grid topology reconfiguration using a simple deep reinforcement learning approach

no code implementations • 26 Nov 2020 • Medha Subramanian, Jan Viebahn, Simon H. Tindemans, Benjamin Donnot, Antoine Marot

The behaviour of this agent is tested on different time-series of generation and demand, demonstrating its ability to operate the grid successfully in 965 out of 1000 scenarios.

reinforcement-learning Reinforcement Learning (RL) +2