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  Dynamic Optimization of Power Flow and Electric Vehicle Resources in Smart Grid

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  Smart Grid

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Envision the possibility that smart electric cars cruise carefreely on the streets without worrying the batteries running out of juice. During hot summer days when air conditioners are running at the maximum, the electric utility can quickly balance supply and demand, shuffle and smooth the load to deliver power efficiently to the millions of households. The first scenario is a system of smart mobile batteries, while the second is a smart power system. What if we could connect the two? These benefits could be realized by technologies developed in the smart electric power grid (Smart Grid). Indeed, a key challenge is to control and optimize the Smart Grid network. An interconnected system of hundreds of millions of electric cars introduces rapid, large, and random fluctuations in power supply and demand, voltage and frequency. Such deployments create stronger demands and highly variable (in time and space) operating conditions than those experienced in current networks. With energy storage, how should power flow be optimized and control to maximize efficiency and minimize power consumption cost?

This project helps to realize the vision of Smart Grid by addressing several fundamental challenges. First, the physics of power flow make these power flow optimization problems extremely hard to solve as they are nonlinear and non-convex optimization problems. Second, the power flows become less predictable and the network now has to adapt to the network users (including electric vehicles) instead of the other way around. Using rigorous mathematical analysis, we have made significant contributions to overcome the bottleneck problem of non-convexity in a distributed manner. In other words, we want to "convexify" and "decompose" the original non-convex problem to find exact global solutions. This is mathematically challenging because it is known that convex relaxation does not provide global solutions of the original problem. Our mathematical techniques using differential topology and convex relaxation are very promising because they can guarantee global solutions and the development of faster distributed control algorithms.


Distributed Energy Storage Algorithms

In addition to overcoming this notorious non-convexity barrier, our theory-inspired approach also highlights the importance of network connectivity for energy storage and electric vehicle charging. We show that there are clearly benefits in a joint optimization of energy storage and the power flow. Energy storage at demand buses can absorb the transients while the power loads are rebalanced. When the power price is low, energy can be stored in electric vehicle batteries. On the other hand, when the power price is high, users can first draw energy locally from the electric vehicle batteries before consuming the energy directly from the power grid network. The electric vehicle batteries play an interestingly dual role: they represent a new type of demand load to be managed and also as new energy storage resources (when power flows from the vehicle to grid) for smoothing the aggregate load in the system. This dual role can be realized by our distributed control algorithms in the Smart Grid and mobile computing software that run on wireless communication networks. The research innovations in this project have immense scientific value and have been published in top-tiered journal publications (the IEEE Transactions on Power Systems, the IEEE Journal of Selected Topics in Signal Processing and the IEEE Journal on Selected Areas in Communications).

Research into this joint flow and charging dynamic control and optimization has led to various collaborations around the world. Together with the California Institute of Technology (Caltech) and a Californian utility, we have studied how the power flow optimization and distributed control algorithms can be used for experimentation on a power microgrid installed on Catalina Island located southwest of Los Angeles, California. The impact of this project is even larger on its applicability to modern electric power smart grids as the society's reliance on sustainable energy continues to expand tremendously.

Dr Chee Wei TAN with President Daniel MOTE of the National Academy of Engineering at the 2015 China-America Frontiers of Engineering Symposium

Dr Chee Wei TAN
Department of Computer Science

City University of Hong Kong