Multi-Scale Predictive Control of Coupled Energy Networks

基础设施网络（电力、天然气、水、交通）已经发展成为庞大而高度复杂的工程系统。 美国的输电网络包括30，000条输电线路，跨越450，000英里，连接到55，000个变电站。天然气输送网络由210条管道组成，跨越305，000英里，包括1，400个压缩机站和11，000多个输送门。每个变电站和输送门都连接到一个巨大的配电（公用事业）网络，该网络将资源输送到建筑物，家庭和工业设施。 水和交通网络具有相似的安排和复杂性。基础设施网络呈现出截然不同的时间尺度和布局，这使得它们非常难以同步。例如，电力几乎瞬间流过整个电网，而天然气以每小时30-50英里的速度在管道中流动。实现同步的困难在2014年所谓的极地涡旋期间变得明显，当时美国中西部地区经历了创纪录的低温，引发了影响整个国家的天然气和电力短缺。该项目旨在开发新的控制架构，通过系统地管理空间和时间尺度，有效地同步基础设施网络。 控制架构将能够更有效地缓解极端天气和人为事件，以及更有效地分配资源。研究团队将通过开发一种新的变革性控制范式来实现项目目标-研究人员将其称为多尺度模型预测控制（msMPC）。msMPC公式将使分层控制架构的系统设计能够处理覆盖巨大和不同的空间和时间尺度的异构能源网络。msMPC背后的关键思想是创建一个控制层次结构，其中顶层协调控制器使用整个系统的高度粗糙但易处理的时空表示来计算控制动作。然后，粗控制动作在较低级别上进行通信并逐步细化。在最低层是一组分散的控制代理，每个控制代理在时空域的一部分上操作。每个代理拒绝本地和高频干扰，同时保持协调与其他代理通过捕获从粗层次获得的全球信息。换句话说，msMPC是一种寻求弥合完全集中式控制和完全分散式控制之间的差距的范例。该项目还旨在为msMPC层次结构开发稳定性理论，并进行研究以确定更有效的基础设施安排（例如，基于中心而不是基于资源）。 工作的跨学科性质将为研究生提供一个独特的培训环境，结合控制和经济理论，系统建模，优化算法和高性能计算。

Infrastructure networks (electrical, natural gas, water, transportation) have evolved into massive and highly sophisticated engineering systems. The U.S. electrical transmission network comprises 30,000 transmission lines that span 450,000 miles and that are connected to 55,000 substations. The gas transmission network consists of 210 pipelines that span 305,000 miles and comprise 1,400 compressor stations and over 11,000 delivery gates. Each substation and delivery gate is connected to a vast distribution (utility) network that takes resources to buildings, homes, and industrial facilities. Water and transportation networks have similar arrangements and complexity. Infrastructure networks present drastically different time scales and layouts that make them notoriously difficult to synchronize. For instance, electricity flows throughout the power grid nearly instantaneously while natural gas flows in pipelines at 30-50 miles per hour. The difficulty in achieving synchronization became evident during the so-called Polar Vortex of 2014 in which record low temperatures experienced in the Midwest region of the U.S. triggered cascading shortages of natural gas and electricity that affected the entire country. This project seeks to develop new control architectures that can effectively synchronize infrastructure networks by managing space and time scales in a systematic manner. The control architectures will enable more effective mitigation of extreme weather and man-made events as well as a more efficient distribution of resources. The research team will pursue the project goals by developing a new transformative control paradigm -referred to by the investigators as multi-scale model predictive control (msMPC). The msMPC formulation will enable the systematic design of hierarchical control architectures capable of handling heterogenous energy networks covering vast and disparate spatial and temporal scales. The key idea behind msMPC is to create a control hierarchy in which a top level coordinating controller computes control actions using highly coarse but tractable space-time representations of the entire system. The coarse control actions are then communicated and progressively refined at the lower levels. At the lowest level is a set of decentralized control agents each operating on a portion of the time-space domain. Each agent rejects local and high-frequency disturbances, while remaining coordinated with other agents through capturing global information obtained from the coarser levels. In other words, msMPC is a paradigm that seeks to bridge the gap between fully centralized and fully decentralized control. The project also aims at developing a stability theory for msMPC hierarchies and performing studies to identify more effective infrastructure arrangements (e.g., hub-based as opposed to resource-based). The interdisciplinary nature of the work will provide a unique training environment for graduate students that combines control and economic theory, systems modeling, optimization algorithms, and high-performance computing.