论文标题
通过模型预测控制，受监管湖的小时操作
Hourly operation of a regulated lake via Model Predictive Control
论文作者
论文摘要
受管制湖泊的最佳操作是一项具有挑战性的任务，涉及矛盾的目标，从控制湖泊水平到避免洪水和低水平到下游的供水。传统的操作策略设计方法基于离线优化，在一组观察数据上，反馈控制规则将湖泊存储映射到日常释放决策中。在本文中，我们提出了一种退缩的胜利政策，以对湖泊水平进行更频繁的在线调节，与基准方法相比，我们讨论了它的调整。作为附带贡献，我们提供了基于同一基本原理的每日替代方案，并且我们表明，在水流入的某些假设下，这仍然有效。数值模拟用于显示所提出的方法的有效性。我们演示了意大利科莫湖科莫湖的方法。
The optimal operation of regulated lakes is a challenging task involving conflicting objectives, ranging from controlling lake levels to avoid floods and low levels to water supply downstream. The traditional approach to operation policy design is based on an offline optimization, where a feedback control rule mapping lake storage into daily release decisions is identified over a set of observational data. In this paper, we propose a receding-horizon policy for a more frequent, online regulation of the lake level, and we discuss its tuning as compared to benchmark approaches. As side contributions, we provide a daily alternative based on the same rationale, and we show that this is still valid under some assumptions on the water inflow. Numerical simulations are used to show the effectiveness of the proposed approach. We demonstrate the approach on the regulated lake Como, Italy.
