Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations.

This project develops an integrated framework of communications, computation and control for understanding wide-area power system performance in the face of unpredictable disturbances. The power system is chosen as a particularly challenging cyber physical system (CPS) due to its extreme dimension, geographic reach and high reliability requirements. The following tasks are studied in the proposed research: (a) a Partial Difference Equation (PdE) framework to model the impact of network topology on the power system stability; (b) the design of a communication network for CPS, based on the PdE modeling;(c) the design of a control system, which addresses the challenges such as fast response and resource constraints; (d) the design of a computing infrastructure, which addresses the computation for controlling the power network, in particular, the communication complexity for controlling the power network in both cases of one-snapshot computation and iterative computations; and (e) the test and evaluation for both small scale system models of several hundred buses and very large system models of ~50,000 buses. This work contributes to the broader understanding of CPS with high reliability requirements, particularly, critical infrastructures such as the power grid. Modern infrastructures are complex systems of communications and computation tied to the controls of the physical system. The proposed research contributes to improved reliability by addressing the propagation of disturbances and advancing the understanding of geographically distributed CPS. The PIs plan to open multiple courses on CPS related to the proposed research.

Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. This project develops new computer system infrastructure and algorithms for self-sustainable data-driven systems in the field. Research outcomes of the project include (a) a low-maintenance, environmentally-friendly hardware platform with solar energy harvesting and super capacitor-based energy storage, (b) virtualization software infrastructure for low-power nodes to enable inter-operability among distributed field nodes and from/to the data center, and (c) new image and data processing approaches for resource-adaptive fidelity adjustment and function partitioning. The synergy between the self-sustainable hardware, system software support, wireless communications management, and application data processing manifests through global coordination for quality-of-service, energy efficiency, and data privacy. In broader impacts, this project enables data-driven intelligence in the field for important physical system domains. Integration of the technologies involved is accomplished through real-world system deployment and experimentation, including an intelligent campus traffic and parking management system and collaborative work with industry collaborators. The results of this project will further enhance the technological competitiveness for US industries in key areas such as intelligent transportation. The education component includes cross-disciplinary curriculum enhancements and the development of a new instructional platform for realistic experiments with cyber-physical systems. Within the scope of this project, the PIs perform mentoring and outreach activities to recruit/retain women and minorities in science and engineering.