mlsanityData to 14 Oct 2026

Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map

arXiv 2405.15831 ↗First posted 24 May 2024eess.SY cs.AI cs.LGcited by 6

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems. However, conventional model-based adjustment schemes are limited by the increasing variations and uncertainties occur in power systems, where the adjustment problems of different transmission interfaces are often treated as several independent tasks, ignoring their coupling relationship and even leading to conflict decisions. In this paper, we introduce a novel data-driven deep reinforcement learning (DRL) approach, to handle multiple power flow adjustment tasks jointly instead of learning each task from scratch. At the heart of the proposed method is a multi-task attribution map (MAM), which enables the DRL agent to explicitly attribute each transmission interface task to different power system nodes with task-adaptive attention weights. Based on this MAM, the agent can further provide effective strategies to solve the multi-task adjustment problem with a near-optimal operation cost. Simulation results on the IEEE 118-bus system, a realistic 300-bus system in China, and a very large European system with 9241 buses demonstrate that the proposed method significantly improves the performance compared with several baseline methods, and exhibits high interpretability with the learnable MAM.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 41 references that are in the map. First posted on arXiv 24 May 2024.

Builds on

13 of 41 references.

References · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  4. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  5. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  6. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  7. 2020Multi-Task Reinforcement Learning with Soft Modularizationcited by 143
  8. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 143
  9. 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 85
  10. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 50
  11. 2019Adaptive Power System Emergency Control using Deep Reinforcement Learningcited by 27
  12. 2019Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learningcited by 20
1 more
  1. 2016AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASEcited by 17

Built on by

Cited by year

Citing papers per year: 2024: 5; 2025: 1.

6 papers.

  1. 2024Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networkscited by 8
  2. 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approachcited by 3
  3. 2025FUSER: Feed-Forward MUltiview 3D Registration Transformer and SE(3)N Diffusion Refinementcited by 3
  4. 2024Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networkscited by 2
  5. 2024Improving Adversarial Robustness via Feature Pattern Consistency Constraintcited by 1
  6. 2024Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Deep Reinforcement Learning Method for Multi-objective Transmission Switching
  2. 2024Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
  3. 2020Rethink AI-based Power Grid Control: Diving Into Algorithm Design
  4. 2024End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability
  5. 2026Multi-Objective Deep Reinforcement Learning for Secure and Stable Power System Operation
  6. 2026Interpretable Policy Distillation for Power Grid Topology Control
  7. 2025Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach
  8. 2024DRL2FC: An Attack-Resilient Controller for Automatic Generation Control Based on Deep Reinforcement Learning