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

CPS：突破：协作研究：人类与物理系统的交织：电力系统的视角

基本信息

批准号：
1544160
负责人：
Baosen Zhang
金额：
$ 25万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-10-01 至 2018-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544160&HistoricalAwards=false
关键词：
CPS Breakthrough Collaborative Research Interweaving

项目摘要

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.

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接口变得更加丰富和复杂。例如，从电力消费者的角度来看，一系列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;这些新技术也迫使我们重新思考网格如何与用户交互，因为诸如稳定性和鲁棒性之类的关键目标需要在网格中的许多不同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。这一原则要求用户拥有定义明确、稳定且可快速验证的首选项。这些假设在实践中越来越多地被违反：用户的偏好往往没有明确定义;随着时间的推移不稳定;需要时间来发现。我们的项目阐明了一个新的框架之间的物理系统和他们的用户，用户的偏好必须随着时间的推移不断学习，而系统不断运作的不完美的偏好信息。我们的项目有三个主要目标。首先，重新考虑用户模型，以反映用户偏好的新动态视图，即使用户也会随着时间的推移而学习。第二个重点是开发一个新的系统模型，学习用户，因为我们不能理解用户在一个“单镜头”;相反，与用户的反复互动是必需的。然后，我们将重点放在这两种新模式的整合上。我们如何控制和操作一个物理系统，在相互作用的“学习循环”的存在，同时调解许多竞争的用户？我们应用平均场博弈和最优潮流的思想来捕捉，分析和转换系统和正在进行的偏好发现过程之间的相互作用。我们的方法将产生指导市场设计的电力系统中，用户的喜好不断演变。如果成功，我们的项目将在物理系统和用户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如何设计需求响应程序;智能设备如何从用户那里学习;以及智能电网如何运行。为支持这一目标，方案执行机构打算通过与产业界的互动，开发知识转让的途径。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。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Online Active Linear Regression via Thresholding

DOI：
10.1609/aaai.v31i1.10859
发表时间：
2016-02
期刊：
ArXiv
影响因子：
0
作者：
C. Riquelme;Ramesh Johari;Baosen Zhang
通讯作者：
C. Riquelme;Ramesh Johari;Baosen Zhang

Estimating treatment effects in demand response

DOI：
10.1109/ssp.2016.7551825
发表时间：
2016-06
期刊：
2016 IEEE Statistical Signal Processing Workshop (SSP)
影响因子：
0
作者：
Pan Li;Baosen Zhang
通讯作者：
Pan Li;Baosen Zhang

An optimal treatment assignment strategy to evaluate demand response effect

DOI：
10.1109/allerton.2016.7852300
发表时间：
2016-09
期刊：
2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
影响因子：
0
作者：
Pan Li;Baosen Zhang
通讯作者：
Pan Li;Baosen Zhang

A Sparse Linear Model and Significance Test for Individual Consumption Prediction