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

• URL: http://arxiv.org/abs/2607.16004v1
• Date: Fri, 17 Jul 2026 14:39:48 GMT
• Title: Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
• Abstract summary: This work decouples congestion detection and control by combining a random-forest violation pre-classifier with an actor-critic controller.<n>The framework is tested on a real low-voltage grid using synthetic future operating scenarios with low observability and controllability.
• Score: 39.64197235904564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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