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

该项目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。我们将在确保整个铁路基础设施端到端的时间表约束的情况下，发展定价和能源利用的分布式协调。我们将通过交互控制确保这种分布式协调，在网络物理系统中明智地设计动态定价，该系统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。该项目是协同增效的，因为它建立在电力列车基础设施的专门知识和UIC的协调以及麻省理工学院的交互式控制的基础上。我们将通过与宾夕法尼亚州东南部交通管理局的工程师合作来验证这种方法，该地区正在进行重大的现代化工作，以改善他们的电力列车系统。该项目还涉及强有力的国际合作，这也将使技术得到验证。该项目将制定一种多尺度过渡控制策略，在地理分散的铁路电网中实现价格和能源利用的最小化，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演化，从电网与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。在执行所有这些事务的同时，还要满足端到端时间调度、电能质量和容量等系统约束。我们的研究重点是包括信息、控制和功率集成在内的基本问题，包括事件驱动的数据包从源节点到达目标节点，同时确保硬相对截止日期和最佳采样和感知；提出了在控制环间分配有限通信网络容量的网络凹效用函数。该项目开发的优化方法可以类似地应用于多个应用程序领域。

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.