Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
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.
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- PossiblePossibly related (embedding) · 50%Application of machine learning decision tree based power switching control for a standalone PV system with grid support - Nature →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Robustness of Reinforcement Learning-Based Congestion Manage” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Josef Hoppe →
“Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids”
- LinkedLinked via arxiv author · 85%Sarra Bouchkati →
“Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids”
- LinkedLinked via arxiv author · 85%Farah Nasr →
“Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids”
- LinkedLinked via arxiv author · 85%Jonathan Krapp →
“Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids”
- LinkedLinked via arxiv author · 85%Alexander Och →
“Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids”
- LinkedLinked via arxiv author · 85%Maximilian Wirth →
“Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids”