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

Project: Research

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Description

The power network, from generation to transmission and distribution to consumption, will undergo the same kind of architectural transformation in the next decades that computing and the communication networks have gone through in the last two. The smart grid network has hundreds of millions of active endpoints. These are not merely passive loads as are most endpoints today, but endpoints that may generate, compute, communicate, and actuate. They will create both a severe risk and a tremendous opportunity: an interconnected system of hundreds of millions of distributed energy resources introducing 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 and indeed a key challenge is how to optimize these smart grid networks.A fundamental challenge of a smart grid is: to what extent can moving energy through space and time be optimized to benefit the power network, say to charge electric cars? The power flows become less predictable and the network now has to adapt to the network users instead of the other way around. In this project, we will study power flow optimization problems that consider important features in smart grid such as network connectivity, energy storage and electric vehicle charging. In these problems, when feasible equilibria exist, it is not known if the equilibrium is unique. Our recent investigations showed that the optimal power flow solution is unique in certain network topologies. This is a somewhat surprising result, as equilibrium is known to be non-unique in the general case. It motivates a complete characterization of the network topologies with unique equilibria. Our approach is a novel use of powerful mathematical tools such as differential topology, convex relaxation and optimization theory. This theory-driven approach has three distinctive characters. First, it demonstrates a novel way to characterize a long-standing problem which brings forth new intellectual and practical considerations. Second, it enables new algorithms that are much faster and have lower complexities than state-of-the-arts to be designed. Third, it is a building block for time-dependent power flow optimization problems that are applicable to large-scale storage integration and electric vehicle charging networks.This project will make significant profound contributions to power flow optimization theory. Its impact is even larger on its applicability to modern electric power smart grids as society's reliance on sustainable energy continues to expand tremendously.

Detail(s)

Project number9041912
Grant typeGRF
StatusFinished
Effective start/end date1/08/135/10/15