AndersonBoulangerPowellScott-ASC IEEE Smart Grid Jan2011_final

AndersonBoulangerPowellScott-ASC IEEE Smart Grid Jan2011_final

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0199-SIP-2010-PIEEE 1 Abstract—Approximate Dynamic Programming driven Adaptive Stochastic Control for the Smart Grid holds the promise of providing the autonomous intelligence required to elevate the electric grid to efficiency and self-healing capabilities more comparable to the Internet. To that end, we demonstrate the load and source control necessary to optimize management of distributed generation and storage within the Smart Grid. Index Terms —Smart Grid, Adaptive Stochastic Control, Approximate Dynamic Programming, Control Systems. I. INTRODUCTION UTONOMOUS Control Systems for field operations such as at Electric Utilities and Independent System Operators, and especially for the Smart Grid, are more difficult than those required to control indoor and site-specific systems (e.g. factory assembly lines, petrochemical plants, and nuclear power plants). Below we describe such an Adaptive Stochastic Control (ASC) system for load and source management of real-time Smart Grid operations. Electric utilities operate in a difficult, outdoor environment that is dominated by stochastic (statistical) variability, primarily driven by the vagaries of the weather and by equipment failures. Within the Smart Grid, advanced dynamic control will be required for simultaneous management of real time pricing, curtailable loads, Electric Vehicle recharging, solar, wind and other distributed generation sources, many forms of energy storage, and microgrid management (Fig. 1). Computationally, controlling the Smart Grid is a multi- stage, time-variable, stochastic optimization problem. ASC using Approximate Dynamic Programming (ADP) offers the capability of achieving autonomous control using a computational learning system to manage the Smart Grid. Within the complexities of the Smart Grid (Fig. 1), ADP driven ASC is used as a decomposition strategy that breaks the problem of continuous Smart Grid management, with its long Authors contributed equally: R. N. Anderson and A. Boulanger are with the Center for Computational Learning Systems, Columbia University, NY, NY 10027. Their work is supported in part by Consolidated Edison of New York, Inc. and the Department of Energy through American Recovery and Reinvestment Act of 2009 contract E-OE0000197 by way of sub-award agreement SA-SG003. W. B. Powell and W. Scott are with the Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544. Their work is supported in part by the Air Force Office of Scientific Research, grant number FA9550-08-1-0195 and the National Science Foundation, grant CMMI-0856153. time horizons, into a series of short-term problems that a Mixed-Integer Nonlinear Programming solver can handle with sufficient speed and computational efficiency to make it practical for system-of-systems control.
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AndersonBoulangerPowellScott-ASC IEEE Smart Grid Jan2011_final

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