CPS: Breakthrough: Collaborative Research: The Interweaving of Humans and Physical Systems: A Perspective from Power Systems

随着信息技术改变了电网等物理系统，这些系统与其人类用户之间的界面变得更加丰富和复杂。例如，从电力消费者的角度来看，大量的设备和技术正在改变它们与电网的互动方式：需求响应程序；电动汽车;“智能”恒温器和电器；等。这些新技术也迫使我们重新思考电网如何与用户交互，因为稳定性和健壮性等关键目标需要在电网中许多不同的用户之间进行有效的集成。这个项目研究人类和物理系统的复杂交织。传统上，分离原则被用来将人类与物理系统隔离开来。这一原则要求用户具有定义良好、稳定且可快速发现的首选项。这些假设在实践中被越来越多地违背：用户的偏好往往没有明确定义；不稳定的：随着时间的推移不稳定的；花时间去发现。我们的项目阐明了物理系统与其用户之间交互的新框架，其中用户的偏好必须随着时间的推移反复学习，而系统则在不完美的偏好信息下持续运行。我们专注于电力系统领域。我们的项目有三个重点。首先，重新考虑用户模型，以反映用户偏好的这种新的动态视图，甚至用户也在随着时间的推移而学习。第二个重点是开发一种新的系统模型来了解用户，因为我们无法“一次性”了解用户；相反，需要与用户进行重复的交互。然后我们关注这两个新模型的集成。在相互作用的“学习循环”中，我们如何控制和操作一个物理系统，同时在许多竞争用户之间进行调解？我们运用平均场博弈和最优功率流的思想来捕获、分析和转换系统与持续偏好发现过程之间的相互作用。我们的方法将为用户偏好不断变化的电力系统的市场设计提供指导。如果成功，我们的项目将在物理系统和用户的接口方面带来根本性的变化。例如，在电网中，我们的项目直接影响公用事业公司如何设计需求响应方案；智能设备如何向用户学习；以及智能电网的运行方式。为了支持这一目标，pi打算通过与工业界的互动来开发知识转移的途径。pi还将改变他们的教育计划，以反映物理系统和用户之间更大的纠缠。

As information technology has transformed physical systems such as the power grid, the interface between these systems and their human users has become both richer and much more complex. For example, from the perspective of an electricity consumer, a whole host of devices and technologies are transforming how they interact with the grid: demand response programs; electric vehicles; "smart" thermostats and appliances; etc. These novel technologies are also forcing us to rethink how the grid interacts with its users, because critical objectives such as stability and robustness require effective integration among the many diverse users in the grid. This project studies the complex interweaving of humans and physical systems. Traditionally, a separation principle has been used to isolate humans from physical systems. This principle requires users to have preferences that are well-defined, stable, and quickly discoverable. These assumptions are increasingly violated in practice: users' preferences are often not well-defined; unstable over time; and take time to discover. Our project articulates a new framework for interactions between physical systems and their users, where users' preferences must be repeatedly learned over time while the system continually operates with respect to imperfect preference information.We focus on the area of power systems. Our project has three main thrusts. First, user models are rethought to reflect the fact this new dynamic view of user preferences, where even the users are learning over time. The second thrust focuses on developing a new system model that learns about users, since we cannot understand users in a "single-shot"; rather, repeated interaction with the user is required. We then focus on the integration of these two new models. How do we control and operate a physical system, in the presence of the interacting "learning loops", while mediating between many competing users? We apply ideas from mean field games and optimal power flow to capture, analyze, and transform the interaction between the system and the ongoing preference discovery process. Our methods will yield guidance for market design in power systems where user preferences are constantly evolving. If successful, our project will usher in a fundamental change in interfacing physical systems and users. For example, in the power grid, our project directly impacts how utilities design demand response programs; how smart devices learn from users; and how the smart grid operates. In support of this goal, the PIs intend to develop avenues for knowledge transfer through interactions with industry. The PIs will also change their education programs to reflect a greater entanglement between physical systems and users.