Networking Research for Smart Energy Systems

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Research on smart energy systems has been moving to the forefront worldwide. Key technological advances that empower such research include the continuing development and harnessing of energy generated from renewable energy resources, and the increasing novel use of distributed computing and communications to balance the supply and demand in the electrical power grid. This latter aspect has enabled the transformation of the traditional electrical grid into a smart cyber-physical infrastructure referred to as the smart grid. The proposed networking research investigates problems in three broad directions related to the above currently evolving vital systems. The first broad direction is on the design and analysis of terrestrial wireless sensor networks (WSNs) and underwater sensor networks (UWSNs) that utilize energy replenishment methods such as environmental energy harvesting or wireless charging to achieve long-lasting operation. Research in this direction is enabled by current advances in designing efficient solar panels, micro wind tribunes, devices that harvest kinetic energy of underwater currents, and wireless charging technologies. A common characteristic of such networks is the fluctuation of a node's energy level over time. Such fluctuations introduce uncertainty in a node's ability to sense and communicate. Consequently, many of the network design and analysis problems that have been investigated for battery operated nodes need to be revisited. We will endeavor to advance the state-of-the-art by designing new efficient, reliable, and intelligent methods for operating such networks. A core aspect in the proposed work is the development of effective methods for dealing with a node's residual energy and a node's foreseeable obtainable energy during any interval of time. The second broad direction pertains to the currently evolving organization of new electrical grids around microgrid networks that provide distributed generation of electrical power. Microgrids can cooperate to complement each other to achieve higher robustness, reliability, and profitability. The proposal addresses distributed coordination problems that arise in this context. The third broad direction considers the uncertainty in knowing many of the operational aspects that can affect the power flow and data flow in a microgrid network, and consequently affect the overall performance of the network. The proposed research in the above areas aims at developing algorithms, protocols, and performance evaluation models to cope with the problems. The methodologies will also incorporate the use of machine learning techniques to develop prediction and learning-based resource management approaches to cope with various uncertainty aspects of the problems.