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This research advances AI applications in energy systems by demonstrating how decoupled reinforcement learning architectures can maintain high robustness against measurement noise and grid-model mismatches in low-voltage distribution networks. By combining a random-forest pre-classifier with an actor-critic controller, the study provides critical insights into deploying machine learning for real-time congestion management where data is sparse or imperfect.
Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
arXiv:2607.16004v1 Announce Type: 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. Accepted for publication at SEST 2026
Systems and Control (eess.SY); Artificial Intelligence (cs.AI)
arXiv:2607.16004 [eess.SY]
(or arXiv:2607.16004v1 [eess.SY] for this version)
https://doi.org/10.48550/arXiv.2607.16004 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
arXiv · 12 oct. 2026
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