发表机构
E.ON impulse GmbH; E.ON Group Innovation GmbH; Schleswig-Holstein Netz GmbH（E.ON impulse公司; E.ON集团创新公司; 石勒苏益格-荷尔斯泰因电网公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低压电网运行挑战，研究结合随机森林预分类器与行为-评判控制器解耦拥塞检测与控制，评估其对测量噪声和电网参数失配的鲁棒性，通过实际电网测试，该方法能有效降低违规幅度，在多种情况下表现良好。
AI 中文摘要
光伏发电增加、电动汽车充电及热泵需求增长对低压配电网运行极限构成挑战，需要能在稀疏观测、噪声测量和不完美电网模型下运行的治理削减方法。本研究将随机森林违规预分类器与行为-评判控制器相结合，解耦拥塞检测与控制，并评估其对测量噪声和电网参数失配的鲁棒性。在实际低压电网上利用低可观测性和可控性的合成未来运行场景进行测试，结果表明在准确电网参数下，控制器将总违规幅度降低98.9%，在测试噪声设置下性能几乎不变，电网模型失配虽更具挑战性，但控制器仍能减轻大多数违规。
英文摘要
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.
Comments6 pages, 5 figures. Accepted for publication at SEST 2026
