用随机森林+强化学习应对低压电网拥堵，抗噪声和模型误差能力强。
Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
- 分两步：先用随机森林预判拥堵，再用强化学习控制
- 电网参数准确时，违规总量减少98.9%，噪声下性能稳定
- 适合电力系统、智能电网领域研究者参考
光伏发电、电动汽车充电和热泵负荷增长正挑战低压配电网的运行极限。需要在观测稀疏、测量噪声大、电网模型不完善的情况下实施有效的限电措施。与以往端到端强化学习方法不同，本文将拥堵检测与控制解耦，结合随机森林违规预分类器与演员-评论家控制器，并评估其对测量噪声和电网参数不匹配的鲁棒性。该框架在真实低压电网上基于合成未来运行场景进行测试，观测与控制能力均受限。当电网参数准确时，控制器使总违规量降低98.9%，且在各类测量噪声条件下性能几乎不变。电网模型失配更具挑战性，但在测试假设下仍能有效缓解多数违规。
原文摘要 · Abstract (English)
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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