CPS: Breakthrough: Collaborative Research: Transactive Control of Smart Railway Grid

点击翻译按钮获取中文摘要

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.