AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs
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Abstract (arXiv)
This paper presents a novel AI-based approach for maximizing time-series available transfer capabilities (ATCs) via autonomous topology control considering various practical constraints and uncertainties. Several AI techniques including supervised learning and deep reinforcement learning (DRL) are adopted and improved to train effective AI agents for achieving the desired performance. First, imitation learning (IL) is used to provide a good initial policy for the AI agent. Then, the agent is trained by DRL algorithms with a novel guided exploration technique, which significantly improves the training efficiency. Finally, an Early Warning (EW) mechanism is designed to help the agent find good topology control strategies for long testing periods, which helps the agent to determine action timing using power system domain knowledge; thus, effectively increases the system error-tolerance and robustness. Effectiveness of the proposed approach is demonstrated in the "2019 Learn to Run a Power Network (L2RPN)" global competition, where the developed AI agents can continuously and safely control a power grid to maximize ATCs without operator's intervention for up to 1-month's operation data and eventually won the first place in both development and final phases of the competition. The winning agent has been open-sourced on GitHub.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2019.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 2; 2024: 3; 2025: 4.
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- 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 52
- 2021Learning to run a power network with trustcited by 17
- 2022PowRL: A Reinforcement Learning Framework for Robust Management of Power Networkscited by 17
- 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 15
- 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
- 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approachcited by 10
- 2022Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Designcited by 8
- 2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Controlcited by 5
- 2024Blackout Mitigation via Physics-guided RLcited by 3
- 2024Imitation Learning for Intra-Day Power Grid Operation through Topology Actionscited by 3
- 2025Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representationscited by 2
- 2025Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Controlcited by 2
- 2025Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control26 Mar 2025
- 2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control12 Feb 2025
- 2025Multi-Objective Reinforcement Learning for Power Grid Topology Control27 Jan 2025
- 2025Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations13 Jan 2025
- 2024Imitation Learning for Intra-Day Power Grid Operation through Topology Actions29 Jul 2024
- 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach1 May 2024
- 2024Blackout Mitigation via Physics-guided RL17 Jan 2024
- 2023Hierarchical Reinforcement Learning for Power Network Topology Control3 Nov 2023
- 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents3 Apr 2023
- 2022PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks5 Dec 2022
- 2022Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design21 Jul 2022
- 2022Towards Practical Physics-Informed ML Design and Evaluation for Power Grid7 May 2022
2 more
- 2021Learning to run a power network with trust21 Oct 2021
- 2021Learning to run a Power Network Challenge: a Retrospective Analysis2 Mar 2021
- 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 52
- 2021Learning to run a power network with trustcited by 17
- 2022PowRL: A Reinforcement Learning Framework for Robust Management of Power Networkscited by 17
- 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 15
- 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
- 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approachcited by 10
- 2022Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Designcited by 8
- 2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Controlcited by 5
- 2024Blackout Mitigation via Physics-guided RLcited by 3
- 2025Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representationscited by 2
- 2025Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Controlcited by 2
- 2022Towards Practical Physics-Informed ML Design and Evaluation for Power Gridcited by 1
12 most cited of 13 All 13 (JSON)
- 2025Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control26 Mar 2025
- 2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control12 Feb 2025
- 2025Multi-Objective Reinforcement Learning for Power Grid Topology Control27 Jan 2025
- 2025Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations13 Jan 2025
- 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach1 May 2024
- 2024Blackout Mitigation via Physics-guided RL17 Jan 2024
- 2023Hierarchical Reinforcement Learning for Power Network Topology Control3 Nov 2023
- 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents3 Apr 2023
- 2022PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks5 Dec 2022
- 2022Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design21 Jul 2022
- 2022Towards Practical Physics-Informed ML Design and Evaluation for Power Grid7 May 2022
- 2021Learning to run a power network with trust21 Oct 2021
12 newest of 13 All 13 (JSON)