Large-scale applications of cyber-physical systems (CPS) such as commercial buildings with Building Automation System (BAS)-based demand response (DR) can play a key role in alleviating demand peaks and associated grid stress, increased electricity unit cost, and carbon emissions. However, benefits of BAS alone are often limited because their demand peak reduction cannot be maintained long enough without unduly affecting occupant comfort. This project seeks to develop control algorithms to closely integrate battery storage-based DR with existing BAS capabilities. The overarching objective is to expand the building's DR capabilities, providing crucial benefits towards smarter grids, while maintaining appropriate occupant comfort and reducing building ownership cost. This project follows a 2-phase approach towards more effective integration. First, building peak demand forecasting will be added to existing battery dispatch methods. Under electricity tariffs geared towards daily [monthly] peaks, such forecasting could result in the same battery-enabled demand charge (dollars per kW) savings as previously demonstrated storage dispatch algorithms. However, supply charges (dollars per kWh) and associated emissions would be reduced because battery dispatch would be geared towards reducing only the biggest daily [monthly] peaks while not incurring roundtrip charging losses on more moderate peaks. Phase 2 builds on phase 1, adding closer integration and systematic optimization to the algorithms for forecasting, BAS, and battery dispatch. This integration will allow the integrated CPS to manipulate the BAS process itself, thereby optimizing, e.g., light dimming, temperature set-points, and pre-cooling in unison with battery-based DR. Feasibility and future promise of such experimental control methodology will be measured by a multi-objective cost function which includes demand and energy charges, savings from DR participation, storage equipment capital expenditure (required size, achievable lifetime), and occupant comfort. Integrating BAS- with battery-based DR is nascent, mostly because the peak demand forecast, BAS, and storage dispatch algorithms that such a CPS requires have yet to be developed. This project seeks to lay important methodological groundwork for such applications, thus furthering commercial buildings' role in the Internet of Things. The PI's participation in the NIST/US-Ignite Global City Team Challenge (with partners Urban Electric Power, Siemens Corporate Technology, City University of New York, and NY-Best) furthers public engagement with such technology and will help catalyze its translation into the commercial space.

Harnessing wind energy is one of the pressing challenges of our time. The scale, complexity, and robustness of wind power systems present compelling cyber-physical system design issues. Leveraging the physical infrastructure at Purdue, this project aims to develop comprehensive computational infrastructure for distributed real-time control. In contrast to traditional efforts that focus on programming-in-the-small, this project emphasizes programmability, robustness, longevity, and assurance of integrated wind farms. The design of the proposed computational infrastructure is motivated by, and validated on, complex cyber-physical interactions underlying Wind Power Engineering. There are currently no high-level tools for expressing coordinated behavior of wind farms. Using the proposed cyber-physical system, the project aims to validate the thesis that integrated control techniques can significantly improve performance, reduce downtime, improve predictability of maintenance, and enhance safety in operational environments. The project has significant broader impact. Wind energy in the US is the fastest growing source of clean, renewable domestically produced energy. Improvements in productivity and longevity of this clean energy source, even by a few percentage points will have significant impact on the overall energy landscape and decision-making. Mitigating failures and enhancing safety will go a long way towards shaping popular perceptions of wind farms -- accelerating broader acceptance within local communities. Given the relative infancy of "smart" wind farms, the potential of the project cannot be overstated.

This proposal will establish a framework for developing distributed Cyber-Physical Systems operating in a Networked Control Systems (NCS) environment. Specific attention is focused on an application where the computational, and communication challenges are unique due to the sheer size of the physical system, and communications between system elements include potential for significant losses and delays. An example of this is the power grid which includes large-scale deployment of distributed and networked Phasor Measurement Units (PMUs) and wind energy resources. Although, much has been done to model and analyze the impact of data dropouts and delay in NCS at a theoretical level, their impact on the behavior of cyber physical systems has received little attention. As a result much of the past research done on the `smart grid' has oversimplified the `physical' portion of the model, thereby overlooking key computational challenges lying at the heart of the dimensionality of the model and the heterogeneity in the dynamics of the grid. A clear gap has remained in understanding the implications of uncertainties in NCS (e.g. bandwidth limitations, packet dropout, packet disorientation, latency, signal loss, etc.) cross-coupled with the uncertainties in a large power grid with wind farms (e.g. variability in wind power, fault and nonlinearity, change in topology etc.) on the reliable operation of the grid. To address these challenges, this project will, for the first time, develop a modeling framework for discovering hitherto unknown interactions through co-simulation of NCS, distributed computing, and a large power grid included distributed wind generation resources. Most importantly, it addresses challenges in distributed computation through frequency domain abstractions and proposes two novel techniques in grid stabilization during packet dropout. The broader impact lies in providing deeper understanding of the impact of delays and dropouts in the Smart Grid. This will enable a better utilization of energy transmission assets and improve integration of renewable energy sources. The project will facilitate participation of women in STEM disciplines, and will include outreach with local Native American tribal community colleges This project will develop fundamental understanding of impact of network delays and drops using an approach that is applicable to a variety of CPS. It will enable transformative Wide-Areas Measurement Systems research for the smart grid through modeling adequacy studies of a representative sub-transient model of the grid along with the representation of packet drop in the communication network by a Gilbert model. Most importantly, fundamental concepts of frequency domain abstraction including balanced truncation and optimal Hankel-norm approximation are proposed to significantly reduce the burden of distributed computing. Finally, a novel `reduced copy' approach and a `modified Kalman filtering' approach are proposed to address the problem of grid stabilization using wind farm controls when packet drop is encountered.

Cyber-physical critical infrastructures integrate networks of computational and physical processes to provide the society with essential services. The power grid, in particular, is a vast and interconnected cyber-physical network for delivering electricity from generation plants to end-point consumers. Protecting power grid critical infrastructures is a vital necessity because the failure of these systems would have a debilitating impact on economic security and public health and safety. However, several recent large-scale outages and the significant increase in the number of major attacks over the past four years confirm the insufficiency of the current protection solutions for these systems. Existing tedious manual tolerance procedures cannot protect those grids against sophisticated attacks. Additionally, use of purely-cyber security solutions for power grid resiliency is not sufficient because they ignore the cyber-physical interdependencies, power-side sensor measurements, and the possibility of countermeasures in power infrastructures. The objective of this research is to investigate fundamental problems in cyber-physical tolerance and develop an integrated set of mathematically rigorous and real-world deployable capabilities, resulting in a system that can model, analyze, predict, and tolerate complex security incidents in computing, physical, or communication assets in a near-real-time manner. The proposed research will provide system administrators and power grid operators with scalable and online integrated cyber-physical monitoring and incident response capabilities through keeping track of cyber-physical infrastructure's dynamic evolution caused by distributed security incidents, optimal proactive response and recovery countermeasures and adaptive preparation for potential future security incidents. The proposed research will facilitate trustworthy operation of next-generation complex and large-scale power grids. The research outcomes will be integrated into educational and knowledge transfer initiatives that involves implementation of curricular activities, innovative learning game development, university workshops, and hands-on K-12 summer camps and academic-year high-school courses, as well as Industry technology transfer efforts to develop a workforce with the capability to reason across multiple disciplines. Through holistic consideration of both cyber and physical factors under adversarial situations, this fundamental work will be applicable to other cyber-physical domains and can transform the way people approach the problem of cyber-physical security.

The electric power grid, a cyber-physical system (CPS), faces an alarmingly high risk of catastrophic damage from cyber-attacks. However, modeling cyber-attacks, evaluating consequences, and developing appropriate countermeasures require a detailed, realistic, and tractable model of electric power CPS operations. The primary barrier is the lack of access to models for the complex legacy proprietary systems upon which the electric power grid has relied for decades. This project aims to overcome these challenges with the development of an attack-verifying (verifiable) software framework that will capture the electric power system operations in adequate detail. Cyber threats will be verified using this framework through a combination of sound theoretical methods and an open-source commercial simulation engine accessible via a unique transition to practice (TTP) option. This research focuses on four fundamental and related thrusts: (i) identifying classes of cyber-attacks with quantifiable physical consequences and developing detection-based countermeasures; (ii) identifying communication attacks on distributed grid operations and developing information-sharing countermeasures; (iii) developing a verifiable software framework that models the spatio-temporal operations of the electric grid in tandem with thrusts (i) and (ii) to verify attack models, evaluate countermeasures, and develop new resiliency protocols; and (iv) a TTP option, in collaboration with industry-leading experts from IncSys and PowerData, to develop commercial grade open source power simulation software packages to integrate and test the attacks and countermeasures of Thrusts (i) through (iii) as well as develop workforce training curriculum for North American Electric Reliability Council (NERC) certification. This research also includes engagement with K-12 students via the Arizona Science Laboratory program.

The electric power grid is a complex cyber-physical system (CPS) that forms the lifeline of modern society. Cybersecurity and resiliency of the power grid is of paramount importance to national security and economic well-being. CPS security testbeds are enabling technologies that provide realistic experimental platforms for the evaluation and validation of security technologies within controlled environments, and they also enable the exploration of robust security solutions. The project has two objectives: (a) to develop innovative architectures, abstractions, models, and algorithms for large-scale CPS security testbeds; and (b) to design and implement a high-fidelity, scalable, open-access CPS security testbed for the smart grid, and to conduct research experimentation. The testbed integrates appropriate cyber-control-physical hardware/software components, models, and algorithms in a modular design that enables federation of smaller testbeds to form a large-scale virtual experimental environment. The use cases for the testbed include vulnerability assessment, risk assessment, risk mitigation studies, and attack-defense exercises. The project also aims to develop standardized datasets, models, libraries, and use cases, and make the testbed available to a broader research community through an open-, remote-access model by leveraging collaboration from academic and industry partners. Besides contributing to research and technology that will enable a future electric power grid that is secure and resilient, this project develops and disseminates innovative curriculum modules including CPS Cyber Defense Competitions (CPS-CDC) for imparting security knowledge to students via an inquiry-based learning paradigm. The project also mentors students, including underrepresented minorities, in thesis work and Capstone projects, and exposes high-school students to cybersecurity concepts via testbed demonstrations.

One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Evolutionary algorithms (EAs), which embody a simplified computational model of the mutation and selection mechanisms of natural evolution, are known to be effective for design optimization. However, the traditional formulations are limited to choosing values for a predetermined set of parameters within a given fixed architecture. This project explores techniques, based on the idea of hidden genes, which enable EAs to select a variable number of components, thereby expanding the explored design space to include selection of a system's architecture. Hidden genetic optimization algorithms have a broad range of potential applications in cyber-physical systems, including automated construction systems, transportation systems, micro-grid systems, and space systems. The project integrates education with research by involving students ranging from high school through graduate school in activities commensurate with their skills, and promotes dissemination of the research results through open source distribution of algorithm implementation code and participation in the worldwide Global Trajectory Optimization Competition. Instead of using a single layer of coding to represent the variables of the system in current EAs, this project investigates adding a second layer of coding to enable hiding some of the variables, as needed, during the search for the optimal system's architecture. This genetic hiding concept is found in nature and provides a natural way of handling system architectures covering a range of different sizes in the design space. In addition, the standard mutation and selection operations in EAs will be replaced by new operations that are intended to extract the full potential of the hidden gene model. Specific applications include space mission design, microgrid optimization, and traffic network signal coordinated planning.

Securing critical networked cyber-physical systems (NCPSs) such as the power grid or transportation systems has emerged as a major national and global priority. The networked nature of such systems renders them vulnerable to a range of attacks both in cyber and physical domains as corroborated by recent threats such as the Stuxnet worm. Developing security mechanisms for such NCPSs significantly differs from traditional networked systems due to interdependence between cyber and physical subsystems (with attacks originating from either subsystem), possible cooperation between attackers or defenders, and the presence of human decision makers in the loop. The main goal of this research is to develop the necessary science and engineering tools for designing NCPS security solutions that are applicable to a broad range of application domains. This project will develop a multidisciplinary framework that weaves together principles from cybersecurity, control theory, networking and criminology. The framework will include novel security mechanisms for NCPSs founded on solid control-theoretic and related notions, analytical tools that allow incorporation of bounded human rationality in NCPS security, and experiments with real-world attack scenarios. A newly built cross-institutional NCPS simulator will be used to evaluate the proposed mechanisms in realistic environments. This research transcends specific cyber-physical systems domains and provides the necessary tools to building secure and trustworthy NCPSs. The broader impacts include a new infrastructure for NCPS research and education, training of students, new courses, and outreach events focused on under-represented student groups

1446582 (Shroff) and 1446478 (Hou). Buildings in the U.S. contribute to 39% of energy use, consume approximately 70% of the electricity, and account for 39% of CO2 emissions. Hence, developing green building architectures is an extremely critical component in energy sustainability. The investigators will develop a unified analytical approach for green building design that comprehensively manages energy sustainability by taking into account the complex interactions between these systems of systems, providing a high degree of security, agility and robust to extreme events. The project will serve to advance the general science in CPS, help bridge the gap between the cyber and civil infrastructure communities, educate students across different disciplines, include topics in curriculum development, and actively recruit underrepresented minority and undergraduate students. The main thesis of this research is that ad hoc green energy designs are often myopic, not taking into account key interdependencies between subsystems and users, and thus often lead to undesirable solutions. In fact, studies have shown that 28%-35% of LEED-certified buildings consumed more energy than their conventional counterparts, all of which calls for the development of a comprehensive analytical foundation for designing green buildings. In particular, the investigators will focus on three interrelated thrust areas: (i) Integrated energy management for a single-building, where the goal is to jointly consider the complex interactions among building subsystems. The investigators will develop novel control schemes that opportunistically exploit the energy demand elasticity of the building subsystems and adapt to occupancy patterns, human comfort zones, and ambient environments. (ii) Managing multi-building interactions to develop (near) optimal distributed control and coordination schemes that provide performance guarantees. (iii) Designing for anomalous conditions such as extreme weather and malicious attacks, where power grid connections and/or cyber-networks are disrupted. The research will provide directions at developing an analytical foundation and cross-cutting principles that will shed insight on the design and control of not only building systems, but also general CPS systems. An important goal is to help bridge the gap between the networking, controls, and civil infrastructure communities by giving talks and publishing works in all of these forums. The investigators will disseminate the research findings to industry as well as offer education and outreach programs to the K-12 students in STEM disciplines. The investigators will also actively continue their already strong existing efforts in recruiting women and underrepresented minorities, as well as providing rich research experience to undergraduate REU students. This project will provide fertile training for students spanning civil infrastructure research, networking, controls, optimization, and algorithmic development. The investigators will also actively include the outcomes of the research in existing and new courses at both the Ohio State University and Virginia Tech.