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.
—Smart Grid, Adaptive Stochastic Control,
Approximate Dynamic Programming, Control Systems.
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
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.