Given the advancements in data-driven modeling for complex engineering and scientific applications, this work utilizes a data-driven predictive control method, namely subspace predictive control, to coordinate hybrid power plant components and meet a desired power demand despite the presence of weather uncertainties. An uncertainty-aware data-driven predictive controller is proposed, and its potential is analyzed using real-world electricity demand profiles. For the analysis, a hybrid power plant with wind, solar, and co-located energy storage capacity of 4 MW each is considered. The analysis shows that the predictive controller can track a real-world-inspired electricity demand profile despite the presence of weather-induced uncertainties and be an intelligent forecaster for HPP performance.

翻译：鉴于复杂工程与科学应用中数据驱动建模技术的进步，本研究采用一种数据驱动预测控制方法——即子空间预测控制——来协调混合发电厂各组件，以在存在天气不确定性的情况下满足预期电力需求。本文提出了一种不确定性感知数据驱动预测控制器，并利用实际电力需求曲线分析了其潜力。分析中考虑了一个由风电、光伏及共址储能构成的混合发电厂，各组件容量均为4兆瓦。分析表明，该预测控制器能够在存在天气诱发不确定性的情况下跟踪实际电力需求曲线，并可作为混合发电厂性能的智能预测器。