Multi-Scale Predictive Control of Coupled Energy Networks

耦合能源网络的多尺度预测控制

• 批准号: 1609183
• 负责人: Victor Zavala Tejeda
• 金额: $ 32.86万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609183&HistoricalAwards=false
• 关键词: Multi; Scale; Predictive; Control; Coupled

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.

基础设施网络（电气，天然气，水，运输）已演变为庞大且高度复杂的工程系统。 美国电气传输网络包括30,000条传输线，跨越450,000英里，并连接到55,000个变电站。气体传输网络由210个管道组成，跨越305,000英里，包括1,400个压缩机站和超过11,000个运送门。每个变电站和交付门都连接到一个庞大的分布（实用程序）网络，该网络将资源带入建筑物，房屋和工业设施。 水和运输网络具有相似的布置和复杂性。基础架构网络呈现出截然不同的时间尺度和布局，使它们臭名昭著地同步。例如，电力在整个电网上几乎瞬间流动，而天然气以每小时30-50英里的速度流动。在2014年所谓的极地涡流中，实现同步的困难变得显而易见，在美国中西部地区，创纪录的低温引发了影响整个国家的天然气和电力的级联短缺。该项目旨在开发新的控制体系结构，这些架构可以通过系统地管理时空尺度来有效地同步基础架构网络。 控制体系结构将使对极端天气和人造事件以及更有效的资源分配更有效地缓解。研究团队将通过开发一个新的变革性控制范式来追求项目目标，并作为多规模模型预测控制（MSMPC）引用了研究人员。 MSMPC公式将使能够处理能够处理涵盖庞大而不同的空间和时间尺度的异质能量网络的分层控制体系结构的系统设计。 MSMPC背后的关键思想是创建一个控制层次结构，在该层次结构中，顶层协调控制器使用整个系统的高度粗糙但可拖延的时空表示来计算控制操作。然后在较低级别传达粗制控制动作并逐步完善。在最低级别是一组分散的控制剂，每个控制剂在时空域的一部分上工作。每个代理商都拒绝本地和高频干扰，同时通过捕获从粗糙级别获得的全球信息与其他代理保持协调。换句话说，MSMPC是一种范式，旨在弥合完全集中和完全分散的控制之间的差距。该项目还旨在为MSMPC层次结构开发稳定理论，并进行研究以确定更有效的基础架构布置（例如，基于HUB而不是基于资源的基础）。 这项工作的跨学科性质将为研究生提供独特的培训环境，这些培训环境结合了控制和经济理论，系统建模，优化算法和高性能计算。