Advanced Signal Processing for Smard Grid and Renewable Energy Sources

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Modernizing the electric power grid has become a major national priority for many countries across the globe. With the increasing penetration of renewable and distributed energy sources along with the necessary means of energy storage technologies, it is envisioned that the so-called "smart grids" will make the production and delivery of electricity more reliable and more cost-effective, and will allow consumers to make more informed decisions about their energy consumption. The smart grid transforms the legacy grid that provides a one-way centrally generated power flow to end users into a more distributed and dynamic system of two-way flow of power and information. The essential concept of the smart grid, where the intelligence will be to a large extent distributed, is the integration of power electronics, real-time metering, digital communications, signal processing, and control technologies into the power system. Communications and information technology play a critical role in the smart grid. As the power grid becomes more complex, more interconnected, and more intelligent, large amount of data will be generated by meters, sensors and synchrophasors. Advanced techniques for managing, analyzing and acting on such data need to be developed. Further, as more and more renewable energy sources, such as photovoltaic (PV) solar arrays and wind turbine arrays are deployed, novel techniques are needed to optimize and monitor the energy generation performance. The numerous technical challenges that accompany the future smart-grid systems call for novel solutions. Hence it is important at this time to perform research that addresses the theoretical aspects of smart grid and renewable energy sources, and to acquire insights and theoretical tools that may help propel significant advances in this field. This project focuses on three major topics that are related to the distributed intelligence for smart grid and renewable energy sources: (1) to develop distributed and secure nonlinear state estimation methods for both power transmission and power distribution grids; (2) to develop decentralized sequential joint change detection and estimation algorithms for real-time detection and mitigation of cyber attacks in smart grid; and (3) to develop decentralized model-free adaptive algorithms for online optimization and monitoring of solar PV arrays, and for controlling of wind turbine arrays, respectively. Smart grid and renewable energy sources bring profound changes to the society and the proposed research will lead to new and powerful techniques for grid state estimation, cyber attack detection and mitigation, and efficient utilization of renewable energy sources. In addition to conducting theoretical analysis, computational procedures will be developed to facilitate the analytical work. The new concepts and algorithms developed under ideal conditions will be tailored to operate in practical systems with various constraints. It is expected that the proposed research will not only enhance our understanding of the fundamental underpinnings of the complex smart-grid systems and renewable energy sources, but also produce new and powerful tools for future electrical power systems. By coordinating with an established outreach program, this project will actively engage K-12 students and traditionally under-represented groups and inspire these students to pursue STEM (science, technology, engineering and mathematics) education and careers.