发表机构
University of Oslo; Swiss Federal Laboratories for Materials Science and Technology (Empa)（奥斯陆大学; 瑞士联邦材料科学与技术实验室（Empa））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文首次实际部署基于场景的数据驱动预测控制（Scenario-DeePC）于并网电池系统，通过数据驱动方式构建约束鲁棒性，在保持跟踪性能的同时显著减少约束违规，并自适应应对SOC估计的异常跳变。
AI 中文摘要
电池储能系统必须严格遵守荷电状态（SOC）限制，以防止过充和深度放电，而测量噪声和难以建模的非线性动力学使这一任务变得复杂。数据驱动预测控制（DeePC）通过直接从数据预测未来行为，解决了建模成本高昂的问题。然而，其正则化仅增强了预测的鲁棒性，而非约束的鲁棒性。基于场景的DeePC（Scenario-DeePC）通过场景方法扩展了DeePC，完全基于观测到的预测误差而非假设的扰动分布，以数据驱动方式构建约束鲁棒性。本文首次将Scenario-DeePC实际部署于NEST研究设施的并网电池系统，其SOC估计除普通噪声外，还表现出突然、不规则的重新校准跳变。与标准DeePC相比，Scenario-DeePC在实现相当跟踪性能的同时，显著减少了约束违规次数。其自适应场景缓冲区进一步自动收紧约束处理，提高了对不可预测重新校准事件的鲁棒性。
英文摘要
Battery energy storage systems must respect strict state-of-charge (SOC) limits to prevent overcharge and deep discharge, a task complicated by measurement noise and costly-to-model nonlinear dynamics. Data-enabled predictive control (DeePC) resolves the costly modeling issue by predicting future behavior directly from data. However, its regularization robustifies only the prediction and not the constraints. Scenario-based DeePC (Scenario-DeePC) extends DeePC with the scenario approach, building constraint robustness fully data-driven from observed prediction errors rather than an assumed disturbance distribution. This paper presents the first real-world deployment of Scenario-DeePC, on a grid-connected battery system at the NEST research facility, whose SOC estimate exhibits abrupt, irregular recalibration jumps in addition to ordinary noise. Compared to standard DeePC, Scenario-DeePC achieves comparable tracking performance with substantially fewer constraint violations. Its adaptive scenario buffer further tightens constraint handling automatically, improving robustness to unpredictable recalibration events.
Comments8 pages, 5 figures
