mlsanityData to 14 Oct 2026

Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks

arXiv 2510.17380 ↗First posted 20 Oct 2025cs.LG cs.AIcited by 0

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

Abstract (arXiv)

Optimizing the energy management within a smart grids scenario presents significant challenges, primarily due to the complexity of real-world systems and the intricate interactions among various components. Reinforcement Learning (RL) is gaining prominence as a solution for addressing the challenges of Optimal Power Flow in smart grids. However, RL needs to iterate compulsively throughout a given environment to obtain the optimal policy. This means obtaining samples from a, most likely, costly simulator, which can lead to a sample efficiency problem. In this work, we address this problem by substituting costly smart grid simulators with surrogate models built using Phisics-informed Neural Networks (PINNs), optimizing the RL policy training process by arriving to convergent results in a fraction of the time employed by the original environment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 51 references that are in the map. First posted on arXiv 20 Oct 2025.

Builds on

11 of 51 references.

References · 11

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2021Physics-Informed Neural Networks for AC Optimal Power Flowcited by 54
  3. 2021Leveraging power grid topology in machine learning assisted optimal power flowcited by 22
  4. 2021Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systemscited by 18
  5. 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 14
  6. 2023PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle modelcited by 6
  7. 2023Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flowcited by 5
  8. 2023PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D modelcited by 5
  9. 2024A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learningcited by 3
  10. 2024KKT-Informed Neural Networkcited by 2
  11. 2025Building surrogate models using trajectories of agents trained by Reinforcement Learningcited by 1

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  3. 2025Distributed Reinforcement Learning using Local Smart Meter Data for Voltage Regulation in Distribution Networks
  4. 2026Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources
  5. 2024Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
  6. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
  7. 2025A Reinforcement Learning Approach for Optimal Control in Microgrids
  8. 2024Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes