The electric grid in the United States has evolved over the past century from a series of small independent community-based systems to one of the largest and most complex cyber-physical systems today. However, the established conditions that made the electric grid an engineering marvel are being challenged by major changes, the most important being a worldwide effort to mitigate climate change by reducing carbon emissions. This research investigates key aspects of a computation and information foundation for future cyber-physical energy systems?the smart grids. The overall project objective is to support high penetrations of renewable energy sources, community based micro-grids, and the widespread use of electric cars and smart appliances. The research has three interconnected components that, collectively, address issues of computation architecture, information hierarchy, and experimental modeling and validation. On computation architecture, the framework based on cloud computing is investigated for the scalable, consistent, and secure operations of smart grids. The research aims to quantify fundamental design tradeoffs among scalability, data consistency, security, and trustworthiness for emerging applications of smart grids. On information hierarchy, temporal and spatial characteristics of information hierarchy are investigated with the goal of gaining a foundational understanding on how information should be partitioned, collected, distributed, compressed, and aggregated. The research also develops an open and scalable experimental platform (SmartGridLab) for empirical investigations and testing of algorithms and concepts developed in this project. SmartGridLab integrates the hardware testbed with a software simulator so that software virtual nodes can interact with physical nodes in the testbed. This research also includes a significant education component aimed at integrating frontier research with undergraduate and graduate curricula.

The national transmission networks that deliver high voltage electric power underpin our society and are central to the ongoing transformation of the American energy infrastructure. Transmission networks are very large and complicated engineering systems, and "keeping the lights on" as the transformation of the American energy infrastructure proceeds is a fundamental engineering challenge involving both the physical aspects of the equipment and the cyber aspects of the controls, communications, and computers that run the system. The project develops new principles of cyber-physical engineering by focusing on instabilities of electric power networks that can cause blackouts. It proposes novel approaches to analyze these instabilities and to design cyber-physical control methods to monitor, detect, and mitigate them. The controls must perform robustly in the presence of variability and uncertainty in electric generation, loads, communications, and equipment status, and during abnormal states caused by natural faults or malicious attacks. The research produces cyber-physical engineering methodologies that specifically help to mitigate power system blackouts and more generally show the way forward in designing robust cyber-physical systems in environments characterized by rich dynamics and uncertainty. Education and outreach efforts involve students at high school, undergraduate, and graduate levels, as well as dissemination of results to the public and the engineering and applied science communities in industry, government and universities.

The objective of this research is to understand how pervasive information changes energy production, distribution and use. The design of a more scalable and flexible electric infrastructure, encouraging efficient use, integrating local generation, and managing demand through awareness of energy availability and use over time, is investigated. The approach is to develop a cyber overlay on the energy distribution system in its physical manifestations: machine rooms, buildings, neighborhoods, isolated generation islands and regional grids. A scaled series of experimental energy networks will be constructed, to demonstrate monitoring, negotiation protocols, control algorithms and Intelligent Power Switches integrating information and energy flows in a datacenter, building, renewable energy: farm", and off-grid village. These will be generalized and validated through larger scale simulations. The proposal's intellectual merit is in understanding broadly how information enables energy efficiencies: through intelligent matching of loads to sources, via various levels of aggregation, and by managing how and when energy is delivered to demand, adapted in time and form to available supply. Bi-directional information exchange is integrated everywhere that power is transferred. Broader impacts include training diverse students, such as undergraduates and underrepresented groups, in a new interdisciplinary curriculum in information and energy technologies. Societal impact is achieved by demonstrating dramatic reductions in the carbon footprint of energy and its overall usage, greater penetration of renewables while avoiding additional fossil fuel plants, and shaping a new culture of energy awareness and management. The evolution of Computer Science will be accelerated to meet the challenges of cyber-physical information processing.

The objective of this research is the development of methods for the control of energy flow in buildings, as enabled by cyber infrastructure. The approach is inherently interdisciplinary, bringing together electrical and mechanical engineers alongside computer scientists to advance the state of the art in simulation, design, specification and control of buildings with multiple forms of energy systems, including generation and storage. A significant novelty of this project lies in a fundamental view of a building as a set of overlapping, interacting networks. These networks include the thermal network of the physical building, the energy distribution network, the sensing and control network, as well as the human network, which in the past have been considered only separately. This work thus seeks to develop methods for simulating, optimizing, modeling, and control of complex, heterogeneous networks, with specific application to energy efficient buildings. The advent of maturing distributed and renewable energy sources for on-site cooling, heating, and power production and the concomitant developments in the areas of cyberphysical and microgrid systems present an enormous opportunity to substantially increase energy efficiency and reduce energy-related emissions in the commercial building energy sector. In addition, there is a direct impact of the proposed work in training students with backgrounds in the unique blend of engineering and computer science that is needed for the study of cyber-enabled energy efficient management of structures, as well as planned interactions at the undergraduate and K-12 level.

The objective of this research is to develop methods to monitor and ensure the robustness of a class of cyber-physical systems termed "physical networks," such as electric, water, sewage, and gas networks. The approach is to analyze such networks using the mathematical formalism of graphical models. The project models a physical network as a graph, whose variables have a concrete physical interpretation, such as voltage, satisfying known physical laws, such as Kirchoff laws. The machinery of graphical models is used to develop methods to monitor and ensure the robustness of such networks, using the electric power network as a representative. By studying puzzling network-wide interactions, the project has the potential to clarify the role of complexity in large scale networks. Potential contributions will be made to the fields of distributed inference algorithms and fast numerical methods. Physical networks play a crucial role in modern society, and yet, often exhibit fragile behavior, such as black-outs in electric power networks, resulting in economic loss, as well as causing a security risk. This project seeks to understand the robustness behavior of such networks and to train a broad class of students in their theory and practice. Results from this research are to be incorporated into courses and disseminated via research publications. The Carnegie Mellon Conference on the Electricity Industry allows students to interact with faculty and electricity industry veterans. Interaction with the electricity industry aims to provide it with an understanding of cyber-intelligence, to ensure effective robustness monitoring capabilities in the power grid.

The objective of this research is to establish a foundational framework for smart grids that enables significant penetration of renewable DERs and facilitates flexible deployments of plug-and-play applications, similar to the way users connect to the Internet. The approach is to view the overall grid management as an adaptive optimizer to iteratively solve a system-wide optimization problem, where networked sensing, control and verification carry out distributed computation tasks to achieve reliability at all levels, particularly component-level, system-level, and application level. Intellectual merit. Under the common theme of reliability guarantees, distributed monitoring and inference algorithms will be developed to perform fault diagnosis and operate resiliently against all hazards. To attain high reliability, a trustworthy middleware will be used to shield the grid system design from the complexities of the underlying software world while providing services to grid applications through message passing and transactions. Further, selective load/generation control using Automatic Generation Control, based on multi-scale state estimation for energy supply and demand, will be carried out to guarantee that the load and generation in the system remain balanced. Broader impact. The envisioned architecture of the smart grid is an outstanding example of the CPS technology. Built on this critical application study, this collaborative effort will pursue a CPS architecture that enables embedding intelligent computation, communication and control mechanisms into physical systems with active and reconfigurable components. Close collaborations between this team and major EMS and SCADA vendors will pave the path for technology transfer via proof-of-concept demonstrations.

The objective of this research is to enable cyberphysical systems (CPS) to be context-aware of people in the environment and to use data from real-world probabilistic sensors. The approach is (1) to use radio tomography (RT) and RFID to provide awareness (location and potential identification) of every person in a building or area, and (2) to develop new middleware tools to enable context-aware computing systems to use probabilistic data, thus allowing new applications to exploit sometimes unreliable estimates of the environment.The intellectual merit of the proposal is in the development of new algorithms and models for building-scale RT with low radio densities and across multiple frequencies; the development of efficient multichannel access protocols for rapid and adaptive peer-to-peer measurements; the development of space-time and probabilistic data representations for use in stream-based context awareness systems and for merging ID and non-ID data; (4) and the development of a human context-aware software development toolkit that interfaces between probabilistic data and context-aware applications. The proposal impacts broadly the area of Cyber-physical systems that reason about human presence and rely on noisy and potentially ambiguous (practical) sensors. The research has additional dramatic impact in: (1) smart facilities which automatically enforce safety, privacy, and security procedures, increasing the ability to respond in emergency situations and prevent accidents and sabotage; (2) elder care, to monitor for physical or social decline so that effective intervention can be implemented, extending the period elders can live in their own home, without pervasive video surveillance.

The objective of this research is to establish a foundational framework for smart grids that enables significant penetration of renewable DERs and facilitates flexible deployments of plug-and-play applications, similar to the way users connect to the Internet. The approach is to view the overall grid management as an adaptive optimizer to iteratively solve a system-wide optimization problem, where networked sensing, control and verification carry out distributed computation tasks to achieve reliability at all levels, particularly component-level, system-level, and application level. Intellectual merit. Under the common theme of reliability guarantees, distributed monitoring and inference algorithms will be developed to perform fault diagnosis and operate resiliently against all hazards. To attain high reliability, a trustworthy middleware will be used to shield the grid system design from the complexities of the underlying software world while providing services to grid applications through message passing and transactions. Further, selective load/generation control using Automatic Generation Control, based on multi-scale state estimation for energy supply and demand, will be carried out to guarantee that the load and generation in the system remain balanced. Broader impact. The envisioned architecture of the smart grid is an outstanding example of the CPS technology. Built on this critical application study, this collaborative effort will pursue a CPS architecture that enables embedding intelligent computation, communication and control mechanisms into physical systems with active and reconfigurable components. Close collaborations between this team and major EMS and SCADA vendors will pave the path for technology transfer via proof-of-concept demonstrations.

The objective of this research is to enable cyberphysical systems (CPS) to be context-aware of people in the environment and to use data from real-world probabilistic sensors. The approach is (1) to use radio tomography (RT) and RFID to provide awareness (location and potential identification) of every person in a building or area, and (2) to develop new middleware tools to enable context-aware computing systems to use probabilistic data, thus allowing new applications to exploit sometimes unreliable estimates of the environment.The intellectual merit of the proposal is in the development of new algorithms and models for building-scale RT with low radio densities and across multiple frequencies; the development of efficient multichannel access protocols for rapid and adaptive peer-to-peer measurements; the development of space-time and probabilistic data representations for use in stream-based context awareness systems and for merging ID and non-ID data; (4) and the development of a human context-aware software development toolkit that interfaces between probabilistic data and context-aware applications. The proposal impacts broadly the area of Cyberphysical systems that reason about human presence and rely on noisy and potentially ambiguous (practical) sensors. The research has additional dramatic impact in: (1) smart facilities which automatically enforce safety, privacy, and security procedures, increasing the ability to respond in emergency situations and prevent accidents and sabotage; (2) elder care, to monitor for physical or social decline so that effective intervention can be implemented, extending the period elders can live in their own home, without pervasive video surveillance.