CPS: Breakthrough: Collaborative Research: . Transactive control of smart railway grid.

CPS：突破：协作研究：。

基本信息

批准号：
1644877
负责人：
Anuradha Annaswamy
金额：
$ 20万
依托单位：
Massachusetts Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1644877&HistoricalAwards=false
关键词：
CPS Breakthrough Collaborative Research Transactive

项目摘要

This project pursues a smart cyber-physical approach for improving the electric rail infrastructure in the United States and other nations. We will develop a distributed coordination of pricing and energy utilization even while ensuring end-to-end time schedule constraints for the overall rail infrastructure. We will ensure this distributed coordination through transactive control, a judicious design of dynamic pricing in a cyber-physical system that utilizes the computational and communication infrastructure and accommodates the physical constraints of the underlying train service. The project is synergistic in that it builds upon the expertise of the electric-train infrastructure and coordination at UIC and that of transactive control on the part of MIT. We will validate the approach through collaboration with engineers in the Southeastern Pennsylvania Transport Authority, where significant modernization efforts are underway to improve their electric-train system. The project also involves strong international collaboration which will also enable validation of the technologies.This project will formulate a multi-scale transitive control strategy for minimization of price and energy utilization in a geographically-dispersed railway grid with broader implications for evolving smart and micro grids. The transactions evolve over different temporal scales ranging from day-ahead offline transaction between the power grid and the railway system operators yielding price optimality to real-time optimal transaction among the trains or the area control centers (ACC). All of these transactions are carried out while meeting system constraints ranging from end-to-end time-scheduling, power-quality, and capacity. Our research focuses on fundamental issues encompassing integration of information, control, and power, including event-driven packet arrival from source to destination nodes while ensuring hard relative deadlines and optimal sampling and sensing; and formulation of network concave utility function for allocating finite communication-network capacity among control loops. The project develops optimization approaches that can be similarly applied across multiple application domains.

该项目致力于采用智能网络物理方法来改善美国和其他国家的电力铁路基础设施。我们将开发定价和能源利用的分布式协调，同时确保整个铁路基础设施的端到端时间表限制。我们将通过交互控制来确保这种分布式协调，交互控制是网络物理系统中动态定价的明智设计，利用计算和通信基础设施并适应底层列车服务的物理约束。该项目具有协同作用，因为它建立在伊利诺伊大学芝加哥分校的电动火车基础设施和协调方面的专业知识以及麻省理工学院的交互控制方面的专业知识之上。我们将通过与宾夕法尼亚州东南部运输管理局的工程师合作来验证该方法，该管理局正在进行重大现代化工作以改进其电动火车系统。该项目还涉及强有力的国际合作，这也将有助于验证技术。该项目将制定多尺度传递控制策略，以最大限度地减少地理分散的铁路电网中的价格和能源利用，对不断发展的智能和微电网具有更广泛的影响。交易在不同的时间尺度上发展，从电网和铁路系统运营商之间产生价格最优的日前离线交易到列车或区域控制中心（ACC）之间的实时最优交易。所有这些交易都是在满足端到端时间安排、电能质量和容量等系统约束的情况下进行的。我们的研究重点是信息、控制和电源集成等基本问题，包括事件驱动的数据包从源节点到达目标节点，同时确保严格的相对期限和最佳采样和传感；以及制定网络凹效用函数，用于在控制环路之间分配有限的通信网络容量。该项目开发了可以类似地应用于多个应用领域的优化方法。