Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids

arXiv:2607.16004v2 Announce Type: replace-cross Abstract: Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids. This requires curative curtailment methods that can operate under sparse observability, noisy measurements, and imperfect grid models. Unlike prior end-to-end reinforcement-learning approaches for partially observable curtailment, this work decouples congestion detection and control by combining a random-forest violation pre-classifier with an actor-critic controller, and evaluates its robustness to measurement noise and grid-parameter mismatch. The framework is tested on a real low-voltage grid using synthetic future operating scenarios with low observability and controllability. With accurate grid parameters, the controller reduces total violation magnitude by 98.9%, and this performance remains nearly unchanged under the tested measurement-noise settings. Grid-model mismatch proves to be more challenging, but the controller still mitigates most violations under the tested mismatch assumptions.

Abstract:Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids. This requires curative curtailment methods that can operate under sparse observability, noisy measurements, and imperfect grid models. Unlike prior end-to-end reinforcement-learning approaches for partially observable curtailment, this work decouples congestion detection and control by combining a random-forest violation pre-classifier with an actor-critic controller, and evaluates its robustness to measurement noise and grid-parameter mismatch. The framework is tested on a real low-voltage grid using synthetic future operating scenarios with low observability and controllability. With accurate grid parameters, the controller reduces total violation magnitude by 98.9%, and this performance remains nearly unchanged under the tested measurement-noise settings. Grid-model mismatch proves to be more challenging, but the controller still mitigates most violations under the tested mismatch assumptions.

6 pages, 5 figures. Published at SEST 2026

Systems and Control (eess.SY); Artificial Intelligence (cs.AI)

arXiv:2607.16004 [eess.SY]

(or arXiv:2607.16004v2 [eess.SY] for this version)

J. Hoppe, S. Bouchkati, et al., "Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids," 2026 International Conference on Smart Energy Systems and Technologies (SEST), Ciudad Real, Spain, 2026

https://doi.org/10.1109/SEST67798.2026.11712361 DOI(s) linking to related resources

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Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids

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