Data-Assimilation-Assisted Reinforcement Learning for Power Grid Control under Load Uncertainty
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Reliable power grid control requires sequential decisions under transmission constraints, time-varying demand, renewable variability, and imperfect load information. We investigate how load information quality affects control in a customized transmission network simulator inspired by Grid2Op and based on a DC power flow model. The environment includes stochastic spatial loads, temporally correlated renewable-like generation, energy storage, line protection, generator dispatch, and line reconnection. An Ensemble Score Filter (EnSF) corrects noisy load information before it is passed to the controller. A heuristic study first isolates the effect of this correction under a fixed feedback rule. Proximal policy optimization (PPO) agents are then trained for sequential generator and reconnection control, frozen, and evaluated with Forward, EnSF-corrected, and Truth load information on paired stochastic scenarios. EnSF consistently lowers load estimation error. Under heuristic control, the correction extends survival and reduces warning and overload exposure. With frozen PPO policies, it increases cumulative return and keeps performance closer to the Truth benchmark, with a larger advantage when load uncertainty is amplified beyond the nominal training distribution. In both experiments, EnSF reduces load-estimation error and improves the resulting control performance in the simplified transmission network.
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 12 of its 36 references that are in the map. First posted on arXiv 23 Sep 2026.
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- 2025Power Grid Control with Graph-Based Distributed Reinforcement Learning2 Sep
- 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges11 Apr
Other references · 10
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 55
- 2023An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systemscited by 38
- 2019Learning to run a power network challenge for training topology controllerscited by 34
- 2021Action Set Based Policy Optimization for Safe Power Grid Managementcited by 21
- 2022PowRL: A Reinforcement Learning Framework for Robust Management of Power Networkscited by 18
- 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 16
- 2025Ensemble score filter with image inpainting for data assimilation in tracking surface quasi-geostrophic dynamics with partial observationscited by 10
- 2021Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Trainingcited by 8
- 2026Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Predictioncited by 2
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- 2026Toward Adaptive Grid Resilience: A Gradient-Free Meta-RL Framework for Critical Load Restoration
- 2026Interpretable Policy Distillation for Power Grid Topology Control
- 2024Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid
- 2025Leveraging Predictions in Power System Voltage Control: An Adaptive Approach ↗
- 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations