# Data-Assimilation-Assisted Reinforcement Learning for Power Grid Control under Load Uncertainty

arXiv: https://arxiv.org/abs/2609.27229 · HTML: https://mlsanity.com/paper/2609.27229/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems](https://mlsanity.com/paper/2309.00983/) (2023)
- [Learning to run a power network challenge for training topology controllers](https://mlsanity.com/paper/1912.04211/) (2019)
- [Action Set Based Policy Optimization for Safe Power Grid Management](https://mlsanity.com/paper/2106.15200/) (2021)
- [PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks](https://mlsanity.com/paper/2212.02397/) (2022)
- [Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents](https://mlsanity.com/paper/2304.00765/) (2023)
- [Ensemble score filter with image inpainting for data assimilation in tracking surface quasi-geostrophic dynamics with partial observations](https://mlsanity.com/paper/2501.12419/) (2025)
- [Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training](https://mlsanity.com/paper/2110.08956/) (2021)
- [Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges](https://mlsanity.com/paper/2504.08210/) (2025)
- [Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction](https://mlsanity.com/paper/2604.16590/) (2026)
- [Power Grid Control with Graph-Based Distributed Reinforcement Learning](https://mlsanity.com/paper/2509.02861/) (2025)

## 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.